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The top list of academic research databases

2. Web of Science

5. ieee xplore, 6. sciencedirect, 7. directory of open access journals (doaj), get the most out of your academic research database, frequently asked questions about academic research databases, related articles.

Whether you are writing a thesis , dissertation, or research paper it is a key task to survey prior literature and research findings. More likely than not, you will be looking for trusted resources, most likely peer-reviewed research articles.

Academic research databases make it easy to locate the literature you are looking for. We have compiled the top list of trusted academic resources to help you get started with your research:

Scopus is one of the two big commercial, bibliographic databases that cover scholarly literature from almost any discipline. Besides searching for research articles, Scopus also provides academic journal rankings, author profiles, and an h-index calculator .

• Coverage: 90.6 million core records
• References: N/A
• Discipline: Multidisciplinary
• Access options: Limited free preview, full access by institutional subscription only
• Provider: Elsevier

Web of Science also known as Web of Knowledge is the second big bibliographic database. Usually, academic institutions provide either access to Web of Science or Scopus on their campus network for free.

• Coverage: approx. 100 million items
• References: 1.4 billion
• Access options: institutional subscription only
• Provider: Clarivate (formerly Thomson Reuters)

PubMed is the number one resource for anyone looking for literature in medicine or biological sciences. PubMed stores abstracts and bibliographic details of more than 30 million papers and provides full text links to the publisher sites or links to the free PDF on PubMed Central (PMC) .

• Coverage: approx. 35 million items
• Discipline: Medicine and Biological Sciences
• Access options: free
• Provider: NIH

For education sciences, ERIC is the number one destination. ERIC stands for Education Resources Information Center, and is a database that specifically hosts education-related literature.

• Coverage: approx. 1.6 million items
• Discipline: Education
• Provider: U.S. Department of Education

IEEE Xplore is the leading academic database in the field of engineering and computer science. It's not only journal articles, but also conference papers, standards and books that can be search for.

• Coverage: approx. 6 million items
• Discipline: Engineering
• Provider: IEEE (Institute of Electrical and Electronics Engineers)

ScienceDirect is the gateway to the millions of academic articles published by Elsevier, 1.4 million of which are open access. Journals and books can be searched via a single interface.

• Coverage: approx. 19.5 million items

The DOAJ is an open-access academic database that can be accessed and searched for free.

• Coverage: over 8 million records
• Provider: DOAJ

JSTOR is another great resource to find research papers. Any article published before 1924 in the United States is available for free and JSTOR also offers scholarships for independent researchers.

• Coverage: more than 12 million items
• Provider: ITHAKA

Start using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with PubMed and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Scopus is one of the two big commercial, bibliographic databases that cover scholarly literature from almost any discipline. Beside searching for research articles, Scopus also provides academic journal rankings, author profiles, and an h-index calculator .

PubMed is the number one resource for anyone looking for literature in medicine or biological sciences. PubMed stores abstracts and bibliographic details of more than 30 million papers and provides full text links to the publisher sites or links to the free PDF on PubMed Central (PMC)

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21 Legit Research Databases for Free Journal Articles in 2024

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Written by  Scribendi

Has this ever happened to you? While looking for websites for research, you come across a research paper site that claims to connect academics to a peer-reviewed article database for free.

Intrigued, you search for keywords related to your topic, only to discover that you must pay a hefty subscription fee to access the service. After the umpteenth time being duped, you begin to wonder if there's even such a thing as free journal articles.

Subscription fees and paywalls are often the bane of students and academics, especially those at small institutions who don't provide access to many free article directories and repositories.

Whether you're working on an undergraduate paper, a PhD dissertation, or a medical research study, we want to help you find tools to locate and access the information you need to produce well-researched, compelling, and innovative work.

Below, we discuss why peer-reviewed articles are superior and list out the best free article databases to use in 2024.

Download Our Free Research Database Roundup PDF

Why peer-reviewed scholarly journal articles are more authoritative.

Determining what sources are reliable can be challenging. Peer-reviewed scholarly journal articles are the gold standard in academic research. Reputable academic journals have a rigorous peer-review process.

The peer review process provides accountability to the academic community, as well as to the content of the article. The peer review process involves qualified experts in a specific (often very specific) field performing a review of an article's methods and findings to determine things like quality and credibility.

Peer-reviewed articles can be found in peer-reviewed article databases and research databases, and if you know that a database of journals is reliable, that can offer reassurances about the reliability of a free article. Peer review is often double blind, meaning that the author removes all identifying information and, likewise, does not know the identity of the reviewers. This helps reviewers maintain objectivity and impartiality so as to judge an article based on its merit.

Where to Find Peer-Reviewed Articles

Peer-reviewed articles can be found in a variety of research databases. Below is a list of some of the major databases you can use to find peer-reviewed articles and other sources in disciplines spanning the humanities, sciences, and social sciences.

What Are Open Access Journals?

An open access (OA) journal is a journal whose content can be accessed without payment. This provides scholars, students, and researchers with free journal articles. OA journals use alternate methods of funding to cover publication costs so that articles can be published without having to pass those publication costs on to the reader.

Some of these funding models include standard funding methods like advertising, public funding, and author payment models, where the author pays a fee in order to publish in the journal. There are OA journals that have non-peer-reviewed academic content, as well as journals that focus on dissertations, theses, and papers from conferences, but the main focus of OA is peer-reviewed scholarly journal articles.

The internet has certainly made it easier to access research articles and other scholarly publications without needing access to a university library, and OA takes another step in that direction by removing financial barriers to academic content.

Choosing Wisely

Features of legitimate oa journals.

 There are things to look out for when trying to decide if a free publication journal is legitimate:

Mission statement —The mission statement for an OA journal should be available on their website.

Publication history —Is the journal well established? How long has it been available?

Editorial board —Who are the members of the editorial board, and what are their credentials?

Indexing —Can the journal be found in a reliable database?

Peer review —What is the peer review process? Does the journal allow enough time in the process for a reliable assessment of quality?

Impact factor —What is the average number of times the journal is cited over a two-year period?

Features of Illegitimate OA Journals

There are predatory publications that take advantage of the OA format, and they are something to be wary of. Here are some things to look out for:

Contact information —Is contact information provided? Can it be verified?

Turnaround —If the journal makes dubious claims about the amount of time from submission to publication, it is likely unreliable.

Editorial board —Much like determining legitimacy, looking at the editorial board and their credentials can help determine illegitimacy.

Indexing —Can the journal be found in any scholarly databases?

Peer review —Is there a statement about the peer review process? Does it fit what you know about peer review?

How to Find Scholarly Articles

Identify keywords.

Keywords are included in an article by the author. Keywords are an excellent way to find content relevant to your research topic or area of interest. In academic searches, much like you would on a search engine, you can use keywords to navigate through what is available to find exactly what you're looking for.

Authors provide keywords that will help you easily find their article when researching a related topic, often including general terms to accommodate broader searches, as well as some more specific terms for those with a narrower scope. Keywords can be used individually or in combination to refine your scholarly article search.

Narrow Down Results

Sometimes, search results can be overwhelming, and searching for free articles on a journal database is no exception, but there are multiple ways to narrow down your results. A good place to start is discipline.

What category does your topic fall into (psychology, architecture, machine learning, etc.)? You can also narrow down your search with a year range if you're looking for articles that are more recent.

A Boolean search can be incredibly helpful. This entails including terms like AND between two keywords in your search if you need both keywords to be in your results (or, if you are looking to exclude certain keywords, to exclude these words from the results).

Consider Different Avenues

If you're not having luck using keywords in your search for free articles, you may still be able to find what you're looking for by changing your tactics. Casting a wider net sometimes yields positive results, so it may be helpful to try searching by subject if keywords aren't getting you anywhere.

You can search for a specific publisher to see if they have OA publications in the academic journal database. And, if you know more precisely what you're looking for, you can search for the title of the article or the author's name.

Determining the Credibility of Scholarly Sources

Ensuring that sources are both credible and reliable is crucial to academic research. Use these strategies to help evaluate the usefulness of scholarly sources:

• Peer Review : Look for articles that have undergone a rigorous peer-review process. Peer-reviewed articles are typically vetted by experts in the field, ensuring the accuracy of the research findings.

Tip: To determine whether an article has undergone rigorous peer review, review the journal's editorial policies, which are often available on the journal's website. Look for information about the peer-review process, including the criteria for selecting reviewers, the process for handling conflicts of interest, and any transparency measures in place.

• Publisher Reputation : Consider the reputation of the publisher. Established publishers, such as well-known academic journals, are more likely to adhere to high editorial standards and publishing ethics.
• Author Credentials : Evaluate the credentials and expertise of the authors. Check their affiliations, academic credentials, and past publications to assess their authority in the field.
• Citations and References : Examine the citations and references provided in the article. A well-researched article will cite credible sources to support its arguments and findings. Verify the accuracy of the cited sources and ensure they are from reputable sources.
• Publication Date : Consider the publication date of the article. While older articles may still be relevant, particularly in certain fields, it is best to prioritize recent publications for up-to-date research and findings.
• Journal Impact Factor : Assess the journal's impact factor or other metrics that indicate its influence and reputation within the academic community. Higher impact factor journals are generally considered more prestigious and reliable. 

Tip: Journal Citation Reports (JCR), produced by Clarivate Analytics, is a widely used source for impact factor data. You can access JCR through academic libraries or directly from the Clarivate Analytics website if you have a subscription.

• Peer Recommendations : Seek recommendations from peers, mentors, or professors in your field. They can provide valuable insights and guidance on reputable sources and journals within your area of study.
• Cross-Verification : Cross-verify the information presented in the article with other credible sources. Compare findings, methodologies, and conclusions with similar studies to ensure consistency and reliability.

By employing these strategies, researchers can confidently evaluate the credibility and reliability of scholarly sources, ensuring the integrity of their research contributions in an ever-evolving landscape.

The Top 21 Free Online Journal and Research Databases

Navigating OA journals, research article databases, and academic websites trying to find high-quality sources for your research can really make your head spin. What constitutes a reliable database? What is a useful resource for your discipline and research topic? How can you find and access full-text, peer-reviewed articles?

Fortunately, we're here to help. Having covered some of the ins and outs of peer review, OA journals, and how to search for articles, we have compiled a list of the top 21 free online journals and the best research databases. This list of databases is a great resource to help you navigate the wide world of academic research.

These databases provide a variety of free sources, from abstracts and citations to full-text, peer-reviewed OA journals. With databases covering specific areas of research and interdisciplinary databases that provide a variety of material, these are some of our favorite free databases, and they're totally legit!

CORE is a multidisciplinary aggregator of OA research. CORE has the largest collection of OA articles available. It allows users to search more than 219 million OA articles. While most of these link to the full-text article on the original publisher's site, or to a PDF available for download, five million records are hosted directly on CORE.

CORE's mission statement is a simple and straightforward commitment to offering OA articles to anyone, anywhere in the world. They also host communities that are available for researchers to join and an ambassador community to enhance their services globally. In addition to a straightforward keyword search, CORE offers advanced search options to filter results by publication type, year, language, journal, repository, and author.

CORE's user interface is easy to use and navigate. Search results can be sorted based on relevance or recency, and you can search for relevant content directly from the results screen.

Collection : 219,537,133 OA articles

Other Services : Additional services are available from CORE, with extras that are geared toward researchers, repositories, and businesses. There are tools for accessing raw data, including an API that provides direct access to data, datasets that are available for download, and FastSync for syncing data content from the CORE database.

CORE has a recommender plug-in that suggests relevant OA content in the database while conducting a search and a discovery feature that helps you discover OA versions of paywalled articles. Other features include tools for managing content, such as a dashboard for managing repository output and the Repository Edition service to enhance discoverability.

Good Source of Peer-Reviewed Articles : Yes

Advanced Search Options : Language, author, journal, publisher, repository, DOI, year

2. ScienceOpen

Functioning as a research and publishing network, ScienceOpen offers OA to more than 74 million articles in all areas of science. Although you do need to register to view the full text of articles, registration is free. The advanced search function is highly detailed, allowing you to find exactly the research you're looking for.

The Berlin- and Boston-based company was founded in 2013 to "facilitate open and public communications between academics and to allow ideas to be judged on their merit, regardless of where they come from." Search results can be exported for easy integration with reference management systems.

You can also bookmark articles for later research. There are extensive networking options, including your Science Open profile, a forum for interacting with other researchers, the ability to track your usage and citations, and an interactive bibliography. Users have the ability to review articles and provide their knowledge and insight within the community.

Collection : 74,560,631

Other Services : None

Advanced Search Options :   Content type, source, author, journal, discipline

3. Directory of Open Access Journals

A multidisciplinary, community-curated directory, the Directory of Open Access Journals (DOAJ) gives researchers access to high-quality peer-reviewed journals. It has archived more than two million articles from 17,193 journals, allowing you to either browse by subject or search by keyword.

The site was launched in 2003 with the aim of increasing the visibility of OA scholarly journals online. Content on the site covers subjects from science, to law, to fine arts, and everything in between. DOAJ has a commitment to "increase the visibility, accessibility, reputation, usage and impact of quality, peer-reviewed, OA scholarly research journals globally, regardless of discipline, geography or language."

Information about the journal is available with each search result. Abstracts are also available in a collapsible format directly from the search screen. The scholarly article website is somewhat simple, but it is easy to navigate. There are 16 principles of transparency and best practices in scholarly publishing that clearly outline DOAJ policies and standards.

Collection : 6,817,242

Advanced Search Options :   Subject, journal, year

4. Education Resources Information Center

The Education Resources Information Center (ERIC) of the Institution of Education Sciences allows you to search by topic for material related to the field of education. Links lead to other sites, where you may have to purchase the information, but you can search for full-text articles only. You can also search only peer-reviewed sources.

The service primarily indexes journals, gray literature (such as technical reports, white papers, and government documents), and books. All sources of material on ERIC go through a formal review process prior to being indexed. ERIC's selection policy is available as a PDF on their website.

The ERIC website has an extensive FAQ section to address user questions. This includes categories like general questions, peer review, and ERIC content. There are also tips for advanced searches, as well as general guidance on the best way to search the database. ERIC is an excellent database for content specific to education.

Collection : 1,292,897

Advanced Search Options : Boolean

5. arXiv e-Print Archive

The arXiv e-Print Archive is run by Cornell University Library and curated by volunteer moderators, and it now offers OA to more than one million e-prints.

There are advisory committees for all eight subjects available on the database. With a stated commitment to an "emphasis on openness, collaboration, and scholarship," the arXiv e-Print Archive is an excellent STEM resource.

The interface is not as user-friendly as some of the other databases available, and the website hosts a blog to provide news and updates, but it is otherwise a straightforward math and science resource. There are simple and advanced search options, and, in addition to conducting searches for specific topics and articles, users can browse content by subject. The arXiv e-Print Archive clearly states that they do not peer review the e-prints in the database.

Collection : 1,983,891

Good Source of Peer-Reviewed Articles : No

Advanced Search Options :   Subject, date, title, author, abstract, DOI

6. Social Science Research Network

The Social Science Research Network (SSRN) is a collection of papers from the social sciences community. It is a highly interdisciplinary platform used to search for scholarly articles related to 67 social science topics. SSRN has a variety of research networks for the various topics available through the free scholarly database.

The site offers more than 700,000 abstracts and more than 600,000 full-text papers. There is not yet a specific option to search for only full-text articles, but, because most of the papers on the site are free access, it's not often that you encounter a paywall. There is currently no option to search for only peer-reviewed articles.

You must become a member to use the services, but registration is free and enables you to interact with other scholars around the world. SSRN is "passionately committed to increasing inclusion, diversity and equity in scholarly research," and they encourage and discuss the use of inclusive language in scholarship whenever possible.

Collection : 1,058,739 abstracts; 915,452 articles

Advanced Search Options : Term, author, date, network

7. Public Library of Science

Public Library of Science (PLOS) is a big player in the world of OA science. Publishing 12 OA journals, the nonprofit organization is committed to facilitating openness in academic research. According to the site, "all PLOS content is at the highest possible level of OA, meaning that scientific articles are immediately and freely available to anyone, anywhere."

PLOS outlines four fundamental goals that guide the organization: break boundaries, empower researchers, redefine quality, and open science. All PLOS journals are peer-reviewed, and all 12 journals uphold rigorous ethical standards for research, publication, and scientific reporting.

PLOS does not offer advanced search options. Content is organized by topic into research communities that users can browse through, in addition to options to search for both articles and journals. The PLOS website also has resources for peer reviewers, including guidance on becoming a reviewer and on how to best participate in the peer review process.

Collection : 12 journals

Advanced Search Options : None

8. OpenDOAR

OpenDOAR, or the Directory of Open Access Repositories, is a comprehensive resource for finding free OA journals and articles. Using Google Custom Search, OpenDOAR combs through OA repositories around the world and returns relevant research in all disciplines.

The repositories it searches through are assessed and categorized by OpenDOAR staff to ensure they meet quality standards. Inclusion criteria for the database include requirements for OA content, global access, and categorically appropriate content, in addition to various other quality assurance measures. OpenDOAR has metadata, data, content, preservation, and submission policies for repositories, in addition to two OA policy statements regarding minimum and optimum recommendations.

This database allows users to browse and search repositories, which can then be selected, and articles and data can be accessed from the repository directly. As a repository database, much of the content on the site is geared toward the support of repositories and OA standards.

Collection : 5,768 repositories

Other Services : OpenDOAR offers a variety of additional services. Given the nature of the platform, services are primarily aimed at repositories and institutions, and there is a marked focus on OA in general. Sherpa services are OA archiving tools for authors and institutions.

They also offer various resources for OA support and compliance regarding standards and policies. The publication router matches publications and publishers with appropriate repositories.

There are also services and resources from JISC for repositories for cost management, discoverability, research impact, and interoperability, including ORCID consortium membership information. Additionally, a repository self-assessment tool is available for members.

Advanced Search Options :   Name, organization name, repository type, software name, content type, subject, country, region

9. Bielefeld Academic Search Engine

The Bielefeld Academic Search Engine (BASE) is operated by the Bielefeld University Library in Germany, and it offers more than 240 million documents from more than 8,000 sources. Sixty percent of its content is OA, and you can filter your search accordingly.

BASE has rigorous inclusion requirements for content providers regarding quality and relevance, and they maintain a list of content providers for the sake of transparency, which can be easily found on their website. BASE has a fairly elegant interface. Search results can be organized by author, title, or date.

From the search results, items can be selected and exported, added to favorites, emailed, and searched in Google Scholar. There are basic and advanced search features, with the advanced search offering numerous options for refining search criteria. There is also a feature on the website that saves recent searches without additional steps from the user.

Collection : 276,019,066 documents; 9,286 content providers

Advanced Search Options :   Author, subject, year, content provider, language, document type, access, terms of reuse

10. Digital Library of the Commons Repository

Run by Indiana University, the Digital Library of the Commons (DLC) Repository is a multidisciplinary journal repository that allows users to access thousands of free and OA articles from around the world. You can browse by document type, date, author, title, and more or search for keywords relevant to your topic.

DCL also offers the Comprehensive Bibliography of the Commons, an image database, and a keyword thesaurus for enhanced search parameters. The repository includes books, book chapters, conference papers, journal articles, surveys, theses and dissertations, and working papers. DCL advanced search features drop-down menus of search types with built-in Boolean search options.

Searches can be sorted by relevance, title, date, or submission date in ascending or descending order. Abstracts are included in selected search results, with access to full texts available, and citations can be exported from the same page. Additionally, the image database search includes tips for better search results.

Collection : 10,784

Advanced Search Options :   Author, date, title, subject, sector, region, conference

11. CIA World Factbook

The CIA World Factbook is a little different from the other resources on this list in that it is not an online journal directory or repository. It is, however, a useful free online research database for academics in a variety of disciplines.

All the information is free to access, and it provides facts about every country in the world, which are organized by category and include information about history, geography, transportation, and much more. The World Factbook can be searched by country or region, and there is also information about the world's oceans.

This site contains resources related to the CIA as an organization rather than being a scientific journal database specifically. The site has a user interface that is easy to navigate. The site also provides a section for updates regarding changes to what information is available and how it is organized, making it easier to interact with the information you are searching for.

Collection : 266 countries

12. Paperity

Paperity boasts its status as the "first multidisciplinary aggregator of OA journals and papers." Their focus is on helping you avoid paywalls while connecting you to authoritative research. In addition to providing readers with easy access to thousands of journals, Paperity seeks to help authors reach their audiences and help journals increase their exposure to boost readership.

Paperity has journal articles for every discipline, and the database offers more than a dozen advanced search options, including the length of the paper and the number of authors. There is even an option to include, exclude, or exclusively search gray papers.

Paperity is available for mobile, with both a mobile site and the Paperity Reader, an app that is available for both Android and Apple users. The database is also available on social media. You can interact with Paperity via Twitter and Facebook, and links to their social media are available on their homepage, including their Twitter feed.

Collection : 8,837,396

Advanced Search Options : Title, abstract, journal title, journal ISSN, publisher, year of publication, number of characters, number of authors, DOI, author, affiliation, language, country, region, continent, gray papers

13. dblp Computer Science Bibliography

The dblp Computer Science Bibliography is an online index of major computer science publications. dblp was founded in 1993, though until 2010 it was a university-specific database at the University of Trier in Germany. It is currently maintained by the Schloss Dagstuhl – Leibniz Center for Informatics.

Although it provides access to both OA articles and those behind a paywall, you can limit your search to only OA articles. The site indexes more than three million publications, making it an invaluable resource in the world of computer science. dblp entries are color-coded based on the type of item.

dblp has an extensive FAQ section, so questions that might arise about topics like the database itself, navigating the website, or the data on dblp, in addition to several other topics, are likely to be answered. The website also hosts a blog and has a section devoted to website statistics.

Collection : 5,884,702

14. EconBiz

EconBiz is a great resource for economic and business studies. A service of the Leibniz Information Centre for Economics, it offers access to full texts online, with the option of searching for OA material only. Their literature search is performed across multiple international databases.

EconBiz has an incredibly useful research skills section, with resources such as Guided Walk, a service to help students and researchers navigate searches, evaluate sources, and correctly cite references; the Research Guide EconDesk, a help desk to answer specific questions and provide advice to aid in literature searches; and the Academic Career Kit for what they refer to as Early Career Researchers.

Other helpful resources include personal literature lists, a calendar of events for relevant calls for papers, conferences, and workshops, and an economics terminology thesaurus to help in finding keywords for searches. To stay up-to-date with EconBiz, you can sign up for their newsletter.

Collection : 1,075,219

Advanced Search Options :   Title, subject, author, institution, ISBN/ISSN, journal, publisher, language, OA only

15. BioMed Central

BioMed Central provides OA research from more than 300 peer-reviewed journals. While originally focused on resources related to the physical sciences, math, and engineering, BioMed Central has branched out to include journals that cover a broader range of disciplines, with the aim of providing a single platform that provides OA articles for a variety of research needs. You can browse these journals by subject or title, or you can search all articles for your required keyword.

BioMed Central has a commitment to peer-reviewed sources and to the peer review process itself, continually seeking to help and improve the peer review process. They're "committed to maintaining high standards through full and stringent peer review." They publish the journal Research Integrity and Peer Review , which publishes research on the subject.

Additionally, the website includes resources to assist and support editors as part of their commitment to providing high-quality, peer-reviewed OA articles.

Collection : 507,212

Other Services : BMC administers the International Standard Randomised Controlled Trial Number (ISRCTN) registry. While initially designed for registering clinical trials, since its creation in 2000, the registry has broadened its scope to include other health studies as well.

The registry is recognized by the International Committee of Medical Journal Editors, as well as the World Health Organization (WHO), and it meets the requirements established by the WHO International Clinical Trials Registry Platform.

The study records included in the registry are all searchable and free to access. The ISRCTN registry "supports transparency in clinical research, helps reduce selective reporting of results and ensures an unbiased and complete evidence base."

Advanced Search Options :   Author, title, journal, list

A multidisciplinary search engine, JURN provides links to various scholarly websites, articles, and journals that are free to access or OA. Covering the fields of the arts, humanities, business, law, nature, science, and medicine, JURN has indexed almost 5,000 repositories to help you find exactly what you're looking for.

Search features are enhanced by Google, but searches are filtered through their index of repositories. JURN seeks to reach a wide audience, with their search engine tailored to researchers from "university lecturers and students seeking a strong search tool for OA content" and "advanced and ambitious students, age 14-18" to "amateur historians and biographers" and "unemployed and retired lecturers."

That being said, JURN is very upfront about its limitations. They admit to not being a good resource for educational studies, social studies, or psychology, and conference archives are generally not included due to frequently unstable URLs.

Collection : 5,064 indexed journals

Other Services : JURN has a browser add-on called UserScript. This add-on allows users to integrate the JURN database directly into Google Search. When performing a search through Google, the add-on creates a link that sends the search directly to JURN CSE. JURN CSE is a search service that is hosted by Google.

Clicking the link from the Google Search bar will run your search through the JURN database from the Google homepage. There is also an interface for a DuckDuckGo search box; while this search engine has an emphasis on user privacy, for smaller sites that may be indexed by JURN, DuckDuckGo may not provide the same depth of results.

Advanced Search Options :   Google search modifiers

Dryad is a digital repository of curated, OA scientific research data. Launched in 2009, it is run by a not-for-profit membership organization, with a community of institutional and publisher members for whom their services have been designed. Members include institutions such as Stanford, UCLA, and Yale, as well as publishers like Oxford University Press and Wiley.

Dryad aims to "promote a world where research data is openly available, integrated with the scholarly literature, and routinely reused to create knowledge." It is free to access for the search and discovery of data. Their user experience is geared toward easy self-depositing, supports Creative Commons licensing, and provides DOIs for all their content.

Note that there is a publishing charge associated if you wish to publish your data in Dryad. When searching datasets, they are accompanied by author information and abstracts for the associated studies, and citation information is provided for easy attribution.

Collection : 44,458

Advanced Search Options : No

Run by the British Library, the E-Theses Online Service (EThOS) allows you to search over 500,000 doctoral theses in a variety of disciplines. All of the doctoral theses available on EThOS have been awarded by higher education institutions in the United Kingdom.

Although some full texts are behind paywalls, you can limit your search to items available for immediate download, either directly through EThOS or through an institution's website. More than half of the records in the database provide access to full-text theses.

EThOS notes that they do not hold all records for all institutions, but they strive to index as many doctoral theses as possible, and the database is constantly expanding, with approximately 3,000 new records added and 2,000 new full-text theses available every month. The availability of full-text theses is dependent on multiple factors, including their availability in the institutional repository and the level of repository development.

Collection : 500,000+

Advanced Search Options : Abstract, author's first name, author's last name, awarding body, current institution, EThOS ID, year, language, qualifications, research supervisor, sponsor/funder, keyword, title

PubMed is a research platform well-known in the fields of science and medicine. It was created and developed by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM). It has been available since 1996 and offers access to "more than 33 million citations for biomedical literature from MEDLINE, life science journals, and online books."

While PubMed does not provide full-text articles directly, and many full-text articles may be behind paywalls or require subscriptions to access them, when articles are available from free sources, such as through PubMed Central (PMC), those links are provided with the citations and abstracts that PubMed does provide.

PMC, which was established in 2000 by the NLM, is a free full-text archive that includes more than 6,000,000 records. PubMed records link directly to corresponding PMC results. PMC content is provided by publishers and other content owners, digitization projects, and authors directly.

Collection : 33,000,000+

Advanced Search Options : Author's first name, author's last name, identifier, corporation, date completed, date created, date entered, date modified, date published, MeSH, book, conflict of interest statement, EC/RN number, editor, filter, grant number, page number, pharmacological action, volume, publication type, publisher, secondary source ID, text, title, abstract, transliterated title

20. Semantic Scholar

A unique and easy-to-use resource, Semantic Scholar defines itself not just as a research database but also as a "search and discovery tool." Semantic Scholar harnesses the power of artificial intelligence to efficiently sort through millions of science-related papers based on your search terms.

Through this singular application of machine learning, Semantic Scholar expands search results to include topic overviews based on your search terms, with the option to create an alert for or further explore the topic. It also provides links to related topics.

In addition, search results produce "TLDR" summaries in order to provide concise overviews of articles and enhance your research by helping you to navigate quickly and easily through the available literature to find the most relevant information. According to the site, although some articles are behind paywalls, "the data [they] have for those articles is limited," so you can expect to receive mostly full-text results.

Collection : 203,379,033

Other Services : Semantic Scholar supports multiple popular browsers. Content can be accessed through both mobile and desktop versions of Firefox, Microsoft Edge, Google Chrome, Apple Safari, and Opera.

Additionally, Semantic Scholar provides browser extensions for both Chrome and Firefox, so AI-powered scholarly search results are never more than a click away. The mobile interface includes an option for Semantic Swipe, a new way of interacting with your research results.

There are also beta features that can be accessed as part of the Beta Program, which will provide you with features that are being actively developed and require user feedback for further improvement.

Advanced Search Options : Field of study, date range, publication type, author, journal, conference, PDF

Zenodo, powered by the European Organization for Nuclear Research (CERN), was launched in 2013. Taking its name from Zenodotus, the first librarian of the ancient library of Alexandria, Zenodo is a tool "built and developed by researchers, to ensure that everyone can join in open science." Zenodo accepts all research from every discipline in any file format.

However, Zenodo also curates uploads and promotes peer-reviewed material that is available through OA. A DOI is assigned to everything that is uploaded to Zenodo, making research easily findable and citable. You can sort by keyword, title, journal, and more and download OA documents directly from the site.

While there are closed access and restricted access items in the database, the vast majority of research is OA material. Search results can be filtered by access type, making it easy to view the free articles available in the database.

Collection : 2,220,000+

Advanced Search Options : Access, file type, keywords

Check out our roundup of free research databases as a handy one-page PDF.

How to find peer-reviewed articles.

There are a lot of free scholarly articles available from various sources. The internet is a big place. So how do you go about finding peer-reviewed articles when conducting your research? It's important to make sure you are using reputable sources.

The first source of the article is the person or people who wrote it. Checking out the author can give you some initial insight into how much you can trust what you’re reading. Looking into the publication information of your sources can also indicate whether the article is reliable.

Aspects of the article, such as subject and audience, tone, and format, are other things you can look at when evaluating whether the article you're using is valid, reputable, peer-reviewed material. So, let's break that down into various components so you can assess your research to ensure that you're using quality articles and conducting solid research.

Check the Author

Peer-reviewed articles are written by experts or scholars with experience in the field or discipline they're writing about. The research in a peer-reviewed article has to pass a rigorous evaluation process, so it's a foregone conclusion that the author(s) of a peer-reviewed article should have experience or training related to that research.

When evaluating an article, take a look at the author's information. What credentials does the author have to indicate that their research has scholarly weight behind it? Finding out what type of degree the author has—and what that degree is in—can provide insight into what kind of authority the author is on the subject.

Something else that might lend credence to the author's scholarly role is their professional affiliation. A look at what organization or institution they are affiliated with can tell you a lot about their experience or expertise. Where were they trained, and who is verifying their research?

Identify Subject and Audience

The ultimate goal of a study is to answer a question. Scholarly articles are also written for scholarly audiences, especially articles that have gone through the peer review process. This means that the author is trying to reach experts, researchers, academics, and students in the field or topic the research is based on.

Think about the question the author is trying to answer by conducting this research, why, and for whom. What is the subject of the article? What question has it set out to answer? What is the purpose of finding the information? Is the purpose of the article of importance to other scholars? Is it original content?

Research should also be approached analytically. Is the methodology sound? Is the author using an analytical approach to evaluate the data that they have obtained? Are the conclusions they've reached substantiated by their data and analysis? Answering these questions can reveal a lot about the article's validity.

Format Matters

Reliable articles from peer-reviewed sources have certain format elements to be aware of. The first is an abstract. An abstract is a short summary or overview of the article. Does the article have an abstract? It's unlikely that you're reading a peer-reviewed article if it doesn't. Peer-reviewed journals will also have a word count range. If an article seems far too short or incredibly long, that may be reason to doubt it.

Another feature of reliable articles is the sections the information is divided into. Peer-reviewed research articles will have clear, concise sections that appropriately organize the information. This might include a literature review, methodology, and results in the case of research articles and a conclusion.

One of the most important sections is the references or bibliography. This is where the researcher lists all the sources of their information. A peer-reviewed source will have a comprehensive reference section.

An article that has been written to reach an academic community will have an academic tone. The language that is used, and the way this language is used, is important to consider. If the article is riddled with grammatical errors, confusing syntax, and casual language, it almost definitely didn't make it through the peer review process.

Also consider the use of terminology. Every discipline is going to have standard terminology or jargon that can be used and understood by other academics in the discipline. The language in a peer-reviewed article is going to reflect that.

If the author is going out of their way to explain simple terms, or terms that are standard to the field or discipline, it's unlikely that the article has been peer reviewed, as this is something that the author would be asked to address during the review process.

Publication

The source of the article will be a very good indicator of the likelihood that it was peer reviewed. Where was the article published? Was it published alongside other academic articles in the same discipline? Is it a legitimate and reputable scholarly publication?

A trade publication or newspaper might be legitimate or reputable, but it is not a scholarly source, and it will not have been subject to the peer review process. Scholarly journals are the best resource for peer-reviewed articles, but it's important to remember that not all scholarly journals are peer reviewed.

It's helpful to look at a scholarly source's website, as peer-reviewed journals will have a clear indication of the peer review process. University libraries, institutional repositories, and reliable databases (and you now might have a list of some legit ones) can also help provide insight into whether an article comes from a peer-reviewed journal.

Common Research Mistakes to Avoid

Research is a lot of work. Even with high standards and good intentions, it's easy to make mistakes. Perhaps you searched for access to scientific journals for free and found the perfect peer-reviewed sources, but you forgot to document everything, and your references are a mess. Or, you only searched for free online articles and missed out on a ground-breaking study that was behind a paywall.

Whether your research is for a degree or to get published or to satisfy your own inquisitive nature, or all of the above, you want all that work to produce quality results. You want your research to be thorough and accurate.

To have any hope of contributing to the literature on your research topic, your results need to be high quality. You might not be able to avoid every potential mistake, but here are some that are both common and easy to avoid.

Sticking to One Source

One of the hallmarks of good research is a healthy reference section. Using a variety of sources gives you a better answer to your question. Even if all of the literature is in agreement, looking at various aspects of the topic may provide you with an entirely different picture than you would have if you looked at your research question from only one angle.

Not Documenting Every Fact

As you conduct your research, do yourself a favor and write everything down. Everything you include in your paper or article that you got from another source is going to need to be added to your references and cited.

It's important, especially if your aim is to conduct ethical, high-quality research, that all of your research has proper attribution. If you don't document as you go, you could end up making a lot of work for yourself if the information you don't write down is something that later, as you write your paper, you really need.

Using Outdated Materials

Academia is an ever-changing landscape. What was true in your academic discipline or area of research ten years ago may have since been disproven. If fifteen studies have come out since the article that you're using was published, it's more than a little likely that you're going to be basing your research on flawed or dated information.

If the information you're basing your research on isn't as up-to-date as possible, your research won't be of quality or able to stand up to any amount of scrutiny. You don't want all of your hard work to be for naught.

Relying Solely on Open Access Journals

OA is a great resource for conducting academic research. There are high-quality journal articles available through OA, and that can be very helpful for your research. But, just because you have access to free articles, that doesn't mean that there's nothing to be found behind a paywall.

Just as dismissing high-quality peer-reviewed articles because they are OA would be limiting, not exploring any paid content at all is equally short-sighted. If you're seeking to conduct thorough and comprehensive research, exploring all of your options for quality sources is going to be to your benefit.

Digging Too Deep or Not Deep Enough

Research is an art form, and it involves a delicate balance of information. If you conduct your research using only broad search terms, you won't be able to answer your research question well, or you'll find that your research provides information that is closely related to your topic but, ultimately, your findings are vague and unsubstantiated.

On the other hand, if you delve deeply into your research topic with specific searches and turn up too many sources, you might have a lot of information that is adjacent to your topic but without focus and perhaps not entirely relevant. It's important to answer your research question concisely but thoroughly.

Different Types of Scholarly Articles

Different types of scholarly articles have different purposes. An original research article, also called an empirical article, is the product of a study or an experiment. This type of article seeks to answer a question or fill a gap in the existing literature.

Research articles will have a methodology, results, and a discussion of the findings of the experiment or research and typically a conclusion.

Review articles overview the current literature and research and provide a summary of what the existing research indicates or has concluded. This type of study will have a section for the literature review, as well as a discussion of the findings of that review. Review articles will have a particularly extensive reference or bibliography section.

Theoretical articles draw on existing literature to create new theories or conclusions, or look at current theories from a different perspective, to contribute to the foundational knowledge of the field of study.

10 Tips for Navigating Journal Databases

Use the right academic journal database for your search, be that interdisciplinary or specific to your field. Or both!

If it's an option, set the search results to return only peer-reviewed sources.

Start by using search terms that are relevant to your topic without being overly specific.

Try synonyms, especially if your keywords aren't returning the desired results.

Even if you've found some good articles, try searching using different terms.

Explore the advanced search features of the database(s).

Learn to use Booleans (AND, OR, NOT) to expand or narrow your results.

Once you've gotten some good results from a more general search, try narrowing your search.

Read through abstracts when trying to find articles relevant to your research.

Keep track of your research and use citation tools. It'll make life easier when it comes time to compile your references.

7 Frequently Asked Questions

1. how do i get articles for free.

Free articles can be found through free online academic journals, OA databases, or other databases that include OA journals and articles. These resources allow you to access free papers online so you can conduct your research without getting stuck behind a paywall.

Academics don't receive payment for the articles they contribute to journals. There are often, in fact, publication fees that scholars pay in order to publish. This is one of the funding structures that allows OA journals to provide free content so that you don't have to pay fees or subscription costs to access journal articles.

2. How Do I Find Journal Articles?

Journal articles can be found in databases and institutional repositories that can be accessed at university libraries. However, online research databases that contain OA articles are the best resource for getting free access to journal articles that are available online.

Peer-reviewed journal articles are the best to use for academic research, and there are a number of databases where you can find peer-reviewed OA journal articles. Once you've found a useful article, you can look through the references for the articles the author used to conduct their research, and you can then search online databases for those articles, too.

3. How Do I Find Peer-Reviewed Articles?

Peer-reviewed articles can be found in reputable scholarly peer-reviewed journals. High-quality journals and journal articles can be found online using academic search engines and free research databases. These resources are excellent for finding OA articles, including peer-reviewed articles.

OA articles are articles that can be accessed for free. While some scholarly search engines and databases include articles that aren't peer reviewed, there are also some that provide only peer-reviewed articles, and databases that include non-peer-reviewed articles often have advanced search features that enable you to select "peer review only." The database will return results that are exclusively peer-reviewed content.

4. What Are Research Databases?

A research database is a list of journals, articles, datasets, and/or abstracts that allows you to easily search for scholarly and academic resources and conduct research online. There are databases that are interdisciplinary and cover a variety of topics.

For example, Paperity might be a great resource for a chemist as well as a linguist, and there are databases that are more specific to a certain field. So, while ERIC might be one of the best educational databases available for OA content, it's not going to be one of the best databases for finding research in the field of microbiology.

5. How Do I Find Scholarly Articles for Specific Fields?

There are interdisciplinary research databases that provide articles in a variety of fields, as well as research databases that provide articles that cater to specific disciplines. Additionally, a journal repository or index can be a helpful resource for finding articles in a specific field.

When searching an interdisciplinary database, there are frequently advanced search features that allow you to narrow the search results down so that they are specific to your field. Selecting "psychology" in the advanced search features will return psychology journal articles in your search results. You can also try databases that are specific to your field.

If you're searching for law journal articles, many law reviews are OA. If you don't know of any databases specific to history, visiting a journal repository or index and searching "history academic journals" can return a list of journals specific to history and provide you with a place to begin your research.

6. Are Peer-Reviewed Articles Really More Legitimate?

The short answer is yes, peer-reviewed articles are more legitimate resources for academic research. The peer review process provides legitimacy, as it is a rigorous review of the content of an article that is performed by scholars and academics who are experts in their field of study. The review provides an evaluation of the quality and credibility of the article.

Non-peer-reviewed articles are not subject to a review process and do not undergo the same level of scrutiny. This means that non-peer-reviewed articles are unlikely, or at least not as likely, to meet the same standards that peer-reviewed articles do.

7. Are Free Article Directories Legitimate?

Yes! As with anything, some databases are going to be better for certain requirements than others. But, a scholarly article database being free is not a reason in itself to question its legitimacy.

Free scholarly article databases can provide access to abstracts, scholarly article websites, journal repositories, and high-quality peer-reviewed journal articles. The internet has a lot of information, and it's often challenging to figure out what information is reliable. 

Research databases and article directories are great resources to help you conduct your research. Our list of the best research paper websites is sure to provide you with sources that are totally legit.

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How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications. If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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LITERATURE REVIEW SOFTWARE FOR BETTER RESEARCH

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5 tips to enhance your research paper’s visibility and altmetric score.

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I previously wrote about the importance of attracting public attention to scientific research . In today’s world, where billions of people are attached to their digital devices watching the very addictive but often useless TikTok content or receiving instant gratification by engaging in meaningless debates about celebrities, scientists need to find creative ways to have their research noticed. Popularizing scientific research helps inspire the younger generations to go into science and provide the general public with a sense of optimism enabling the government to channel more resources into science. People do need inspiration. But very often, even very important scientific breakthroughs requiring many years, hard work, skill, funding, and genuine serendipity go largely unnoticed by the general public.

One of the best ways to measure expert and public attention is the cumulative Altmetric Attention Score , originally developed by Digital Science and adopted by many prestigious publishers, including Nature Publishing Group. Every Nature paper and the papers published by pretty much every credible publisher are tracked by Digital Science by the Document Object Identification (DOI) or the Unique Resource Locator (URL) . While Altmetric has many limitations, for example, it does not track LinkedIn posts and may not adequately cover the impact of top-tier media coverage, at the moment it is the blueprint for tracking attention.

Altmetric Score in The Age of Generative AI

Media attention is likely to be very important in the age of generative AI. Many modern generative systems, such as ChatGPT, Claude, Mistral, and Gemini, as well as hundreds of Large Language Models (LLMs) in China, use the data from the same sources referenced in Altmetric to learn. The more times generative systems see the same concept presented in different contexts, the better they learn. So if you want to contribute to the training of AI systems that may thank you for it in the future - Altmetric is the way to go.

So what can a research group do to ensure they are communicating their findings effectively and increasing the visibility of their research to ensure it gets reflected in the Altmetric Attention Score?

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Altmetric openly discloses the weights of the various sources and the scoring algorithm is relatively straightforward. It is easy to learn, and there are multiple online resources providing advice on how to share your research in ways that will be captured by Altmetric. Cambridge University Press published a guideline to Altmetric for the authors on how to popularize their research with Altmetric in mind. Wolters Kluwer put out a guide and the editor of Toxicology and Pathology wrote a comprehensive overview of Altmetric and how to use it. Surprisingly, this overview got an Altmetric Attention Score of only 4 at the time of the writing, but was cited 137 times according to Google Scholar .

Altmetric monitors social networks, including X (formerly Twitter), Facebook, and Reddit; all major top-tier mainstream media, mainstream science blogs, policy documents, patents, Wikipedia articles, peer review websites, F1000, Syllabi, X (formerly Twitter), tracked Facebook pages, Reddit, one of the Stack Exchange sites, and Youtube. Unfortunately, several powerful platforms, including LinkedIn, are not currently tracked.

The popularity of the paper depends on many factors. Firstly, it has to be novel, trendy, and newsworthy. You are unlikely to get high Altmetric Score with a boring topic. Secondly, papers coming out of popular labs in top-tier academic institutions and in top journals are likely to attract more attention. Often, the communications officers in these academic institutions work closely with the media to amplify notable research. Celebrity companies, for example, Google DeepMind, consistently get higher coverage.

Screenshot of the Altmetric Attention Score "Flower" showing several tracked sources

Here are the five tips for increasing the visibility of your work and ensuring that reach is tracked and reflected by Altmetric:

1. Understand How Altmetric System Works

Congratulations, if you read this article and looked at what sources are tracked by Altmetric. Most likely, you got the basics and will be able to get a “balanced flower” by making a press release, tweeting the DOI of the paper on X, posting a video overview of your paper on Youtube, announcing on Reddit (I still need to learn how to do this).

To understand how Altmetric works, I emailed a few questions to Miguel Garcia, Director of Product and Data Analytics Hub at Digital Science and my first question was wether the Altmetric algorithm is open source. “The Altmetric Attention Score's calculation is not open source but we try to provide as much information as possible around how we calculate it here, and are currently considering what steps we might take to make our algorithms more transparent.” He also provided a link to how the Altmetric Attention Score is calculated.

Many professionals use LinkedIn as the primary social media resource and I was wondering why Altmetric stopped tracking it. Bad news - technical reasons prevent tracking DOIs on LinkedIn. Good news - they are actively seeking ways to appropriately track mentions on LinkedIn and we may see some news toward the end of the year.

My other big question was how does Altmetric count tweets and retweets on X. What if there are many posts from the same account? Miguel’s response was: “Re-tweets count less than original tweets. In addition to that, modifiers are applied to the type of account that is tweeting in order to reduce the weight of the tweet in situations where we find signals of bias or promiscuity (for example a journal publisher only tweeting their own articles). Besides that, we have conditions around the maximum number of retweets in order to limit the maximum impact they would have.”

So tweeting the article many times will not help you. But if other scientists tweet you paper with a DOI - these tweets will get counted. So tweet others as you would like to be tweeted.

2. Make a Press Release and Distribute to Science-focused Media

If your paper is significant, for example, you elucidated novel disease biology, discovered a new drug, developed a new fancy algorithm, designed a new material, or developed a new application for a quantum computer, it is worthwhile investing some time and resources in writing a press release. If you are working for an academic institution, most likely they have a communications office that will help you. If you do not have this luxury, you will need to learn how to write a press release. Plenty of free online guides cover the basics of press release writing. And press releases are one area where ChatGPT and other generative tools do surprisingly well. Upload your paper and ask it to write a press release, check for errors or exaggerations, edit, and you are ready to go. Just make sure to include the DOI and the URL of your paper. A proper business press release on BusinessWire or PRNewswire may cost several thousand dollars. In my opinion, these resources are dramatically overcharging while providing little service. I don't remember a case where a journalist picked up our news based on a commercial press release. But these releases are often reposted by other online press release distributors and the boost to Altmetric may be considerable. The default news release distribution service for research news is EurekAlert. This resource may sometimes result in journalistic coverage as many reporters are using it for science news. There are many free resources you can use if you do not have any budget.

Once the press release is issued, share it with the media. Share the resulting news coverage via your social networks and contacts. Many journalists track the popularity of their news articles and giving them several thousand extra views from professional audience and increasing their social following increases the chances that they will cover the next important research paper.

3. Make a Blog Post

Writing a blog post can be longer and more comprehensive than the press release. Make sure to add fancy diagrams and graphical explainers. You can share the blog post with the journalists at the same time as the press release. Your blog may serve as a source of inspiration for third party news coverage. Make sure to reference the DOI and URL of your paper.

If your paper is in one of the Nature journals, consider writing a “Behind the Paper" blog post on Nature Bioengineering Community. Surprisingly, these blogs are rarely picked up by Altmetric but may serve as a source of inspiration for the journalists and social media influencers. Plus, it is a resource by the Nature Publishing Group.

4. Tweet and Ask Your Team Members to Tweet

Each post on X gives you a quarter of an Altmetric point. If your paper goes viral on X, your Altmetric score can be considerable. Plus, once journalists notice that it went viral, they will be more likely to cover the story, further increasing the score.

Try to choose the time of the post, the hashtags, and the images wisely. Since Elon Musk took over X and opened the algorithm it became very transparent and easy to optimize for. Here are the top 10 tips for boosting attention for a post on X. Make sure to include the DOI or the URL of the paper for Altmetric to find the post.

5. Experiment, Learn, Repeat

My highest Altmetric Attention Score core to date was around 1,500 for a paper in Nature Biotechnology published in 2019, where we used a novel method for designing small molecules called Generative Tensorial Reinforcement Learning (GENTRL) to generate new molecules with druglike properties that got synthesized and tested all the way into mice. In 2024, we went further and showed that an AI-generated molecule for an AI-discovered target was tested all the way up to Phase II human trials, but the paper published in Nature Biotechnology, let’s call it the TNIK paper , has achieved a score ofjust over 600 to date. So what has changed and what can we learn from these two papers?

The popularity of the paper depends on many factors. Ones which capture the public imagination or have widespread appeal are of course, much more likely to gain traction online. When we published the GENTRL paper in 2019, Generative AI was in its infancy, and there are pretty much no other companies that I heard of at the intersection of generative AI and drug discovery. We also published multiple articles in this field in the years leading to that paper and many key opinion leaders (KOLs) followed us. That following included a small army of generative AI skeptics who not only contributed to multiple rejections in peer-reviewed journals but also openly criticized this approach in social networks. This criticism also helped boost the Altmetric Score and bring more attention to the study. So first learning from this exercise - negative publicity helps overall publicity. As long as you are certain that your research results are honest - leave room for criticism and even help expose your paper’s weaknesses. Critics are your greatest Altmetric boosters. Since readers and, by extension journalists, react to negative news and drama stronger than to positive news, critical reviews will boost your Altmetric as long as the DOI or URL of the paper is properly referenced.

Secondly, papers coming out of popular labs in top-tier academic institutions and in top journals are likely to attract more attention. Often, the communications officers in these academic institutions work closely with the media to amplify notable research. Celebrity companies, for example, Google DeepMind, always get a higher level of coverage. For example, the AlphaFold paper published in July 2021 in Nature got an Altmetric Attention Score of over 3,500 . Even though I have not seen any drugs out of AlphaFold reaching preclinical candidate status, I predict the popularity of this tool will result in the first Nobel Prize in this area. Therefore, in order to become famous and popularize your research more effectively, it is a good idea to build up the public profile of yourself and your work. For example, Kardashians are famous for being famous .

Be careful with Wikipedia. I made a mistake explaining the importance of Wikipedia to students when lecturing on the future of generative AI, and one or two of them got banned for expanding the articles with paper references. Wikipedia requires that these are added by independent editors rather than the authors of papers themselves, but if some editors do not like it, they will not go deep or investigate - they will assume wrongdoing. So it is better to avoid even talking about Wikipedia. References there should happen naturally and often some of the more popular papers get picked up and referenced by veteran editors.

Experimenting with Altmetric will also help you explore new strategies for popularizing scientific research and develop new strategies for inspiring people to learn or even get into the new exciting field. UNESCO estimates that there was just over 8 million full-time equivalent (FTE) researchers in 2018 globally. Only a fraction of these are in biotechnology - less than 0.01% of the global population. If you motivate a million students to go into biotechnology by popularizing your research, you double this number.

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This paper is in the following e-collection/theme issue:

Published on 17.4.2024 in Vol 26 (2024)

Digital Interventions for Recreational Cannabis Use Among Young Adults: Systematic Review, Meta-Analysis, and Behavior Change Technique Analysis of Randomized Controlled Studies

Authors of this article:

• José Côté 1, 2, 3 , RN, PhD   ; 
• Gabrielle Chicoine 3, 4 , RN, PhD   ; 
• Billy Vinette 1, 3 , RN, MSN   ; 
• Patricia Auger 2, 3 , MSc   ; 
• Geneviève Rouleau 3, 5, 6 , RN, PhD   ; 
• Guillaume Fontaine 7, 8, 9 , RN, PhD   ; 
• Didier Jutras-Aswad 2, 10 , MSc, MD  

1 Faculty of Nursing, Université de Montréal, Montreal, QC, Canada

2 Research Centre of the Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada

3 Research Chair in Innovative Nursing Practices, Montreal, QC, Canada

4 Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

5 Department of Nursing, Université du Québec en Outaouais, Saint-Jérôme, QC, Canada

6 Women's College Hospital Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada

7 Ingram School of Nursing, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada

8 Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Sir Mortimer B. Davis Jewish General Hospital, Montreal, QC, Canada

9 Kirby Institute, University of New South Wales, Sydney, Australia

10 Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada

Corresponding Author:

José Côté, RN, PhD

Research Centre of the Centre Hospitalier de l’Université de Montréal

850 Saint-Denis

Montreal, QC, H2X 0A9

Phone: 1 514 890 8000

Email: [email protected]

Background: The high prevalence of cannabis use among young adults poses substantial global health concerns due to the associated acute and long-term health and psychosocial risks. Digital modalities, including websites, digital platforms, and mobile apps, have emerged as promising tools to enhance the accessibility and availability of evidence-based interventions for young adults for cannabis use. However, existing reviews do not consider young adults specifically, combine cannabis-related outcomes with those of many other substances in their meta-analytical results, and do not solely target interventions for cannabis use.

Objective: We aimed to evaluate the effectiveness and active ingredients of digital interventions designed specifically for cannabis use among young adults living in the community.

Methods: We conducted a systematic search of 7 databases for empirical studies published between database inception and February 13, 2023, assessing the following outcomes: cannabis use (frequency, quantity, or both) and cannabis-related negative consequences. The reference lists of included studies were consulted, and forward citation searching was also conducted. We included randomized studies assessing web- or mobile-based interventions that included a comparator or control group. Studies were excluded if they targeted other substance use (eg, alcohol), did not report cannabis use separately as an outcome, did not include young adults (aged 16-35 y), had unpublished data, were delivered via teleconference through mobile phones and computers or in a hospital-based setting, or involved people with mental health disorders or substance use disorders or dependence. Data were independently extracted by 2 reviewers using a pilot-tested extraction form. Authors were contacted to clarify study details and obtain additional data. The characteristics of the included studies, study participants, digital interventions, and their comparators were summarized. Meta-analysis results were combined using a random-effects model and pooled as standardized mean differences.

Results: Of 6606 unique records, 19 (0.29%) were included (n=6710 participants). Half (9/19, 47%) of these articles reported an intervention effect on cannabis use frequency. The digital interventions included in the review were mostly web-based. A total of 184 behavior change techniques were identified across the interventions (range 5-19), and feedback on behavior was the most frequently used (17/19, 89%). Digital interventions for young adults reduced cannabis use frequency at the 3-month follow-up compared to control conditions (including passive and active controls) by −6.79 days of use in the previous month (95% CI −9.59 to −4.00; P <.001).

Conclusions: Our results indicate the potential of digital interventions to reduce cannabis use in young adults but raise important questions about what optimal exposure dose could be more effective, both in terms of intervention duration and frequency. Further high-quality research is still needed to investigate the effects of digital interventions on cannabis use among young adults.

Trial Registration: PROSPERO CRD42020196959; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=196959

Introduction

Cannabis use among young adults is recognized as a public health concern.

Young adulthood (typically the ages of 18-30 y) is a critical developmental stage characterized by a peak prevalence of substance use [ 1 , 2 ]. Worldwide, cannabis is a substance frequently used for nonmedical purposes due in part to its high availability in some regions and enhanced product variety and potency [ 3 , 4 ]. The prevalence of cannabis use (CU) among young adults is high [ 5 , 6 ], and its rates have risen in recent decades [ 7 ]. In North America and Oceania, the estimated past-year prevalence of CU is ≥25% among young adults [ 8 , 9 ].

While the vast majority of cannabis users do not experience severe problems from their use [ 4 ], the high prevalence of CU among young adults poses substantial global health concerns due to the associated acute and long-term health and psychosocial risks [ 10 , 11 ]. These include impairment of cognitive function, memory, and psychomotor skills during acute intoxication; increased engagement in behaviors with a potential for injury and fatality (eg, driving under the influence); socioeconomic problems; and diminished social functioning [ 4 , 12 - 14 ]. Importantly, an extensive body of literature reveals that subgroups engaging in higher-risk use, such as intensive or repeated use, are more prone to severe and chronic consequences, including physical ailments (eg, respiratory illness and reproductive dysfunction), mental health disorders (eg, psychosis, depression, and suicidal ideation or attempts), and the potential development of CU disorder [ 4 , 15 - 17 ].

Interventions to Reduce Public Health Impact of Young Adult CU

Given the increased prevalence of lifetime and daily CU among young adults and the potential negative impact of higher-risk CU, various prevention and intervention programs have been implemented to help users reduce or cease their CU. These programs primarily target young adults regardless of their CU status [ 2 , 18 ]. In this context, many health care organizations and international expert panels have developed evidence-based lower-risk CU guidelines to promote safer CU and intervention options to help reduce risks of adverse health outcomes from nonmedical CU [ 4 , 16 , 17 , 19 ]. Lower-risk guidance-oriented interventions for CU are based on concepts of health promotion [ 20 - 22 ] and health behavior change [ 23 - 26 ] and on other similar harm reduction interventions implemented in other areas of population health (eg, lower-risk drinking guidelines, supervised consumption sites and services, and sexual health) [ 27 , 28 ]. These interventions primarily aim to raise awareness of negative mental, physical, and social cannabis-related consequences to modify individual-level behavior-related risk factors.

Meta-analyses have shown that face-to-face prevention and treatment interventions are generally effective in reducing CU in young adults [ 18 , 29 - 32 ]. However, as the proportion of professional help seeking for CU concerns among young adults remains low (approximately 15%) [ 33 , 34 ], alternative strategies that consider the limited capacities and access-related barriers of traditional face-to-face prevention and treatment facilities are needed. Digital interventions, including websites, digital platforms, and mobile apps, have emerged as promising tools to enhance the accessibility and availability of evidence-based programs for young adult cannabis users. These interventions address barriers such as long-distance travel, concerns about confidentiality, stigma associated with seeking treatment, and the cost of traditional treatments [ 35 - 37 ]. By overcoming these barriers, digital interventions have the potential to have a stronger public health impact [ 18 , 38 ].

State of Knowledge of Digital Interventions for CU and Young Adults

The literature regarding digital interventions for substance use has grown rapidly in the past decade, as evidenced by several systematic reviews and meta-analyses of randomized controlled trial (RCT) studies on the efficacy or effectiveness of these interventions in preventing or reducing harmful substance use [ 2 , 39 - 41 ]. However, these reviews do not focus on young adults specifically. In addition, they combine CU-related outcomes with those of many other substances in their meta-analytical results. Finally, they do not target CU interventions exclusively.

In total, 4 systematic reviews and meta-analyses of digital interventions for CU among young people have reported mixed results [ 42 - 45 ]. In their systematic review (10 studies of 5 prevention and 5 treatment interventions up to 2012), Tait et al [ 44 ] concluded that digital interventions effectively reduced CU among adolescents and adults at the posttreatment time point. Olmos et al [ 43 ] reached a similar conclusion in their meta-analysis of 9 RCT studies (2 prevention and 7 treatment interventions). In their review, Hoch et al [ 42 ] reported evidence of small effects at the 3-month follow-up based on 4 RCTs of brief motivational interventions and cognitive behavioral therapy (CBT) delivered on the web. In another systematic review and meta-analysis, Beneria et al [ 45 ] found that web-based CU interventions did not significantly reduce consumption. However, these authors indicated that the programs tested varied significantly across the studies considered and that statistical heterogeneity was attributable to the inclusion of studies of programs targeting more than one substance (eg, alcohol and cannabis) and both adolescents and young adults. Beneria et al [ 45 ] recommend that future work “establish the effectiveness of the newer generation of interventions as well as the key ingredients” of effective digital interventions addressing CU by young people. This is of particular importance because behavior change interventions tend to be complex as they consist of multiple interactive components [ 46 ].

Behavior change interventions refer to “coordinated sets of activities designed to change specified behavior patterns” [ 47 ]. Their interacting active ingredients can be conceptualized as behavior change techniques (BCTs) [ 48 ]. BCTs are specific and irreducible. Each BCT has its own individual label and definition, which can be used when designing and reporting complex interventions and as a nomenclature system when coding interventions for their content [ 47 ]. The Behavior Change Technique Taxonomy version 1 (BCTTv1) [ 48 , 49 ] was developed to provide a shared, standardized terminology for characterizing complex behavior change interventions and their active ingredients. Several systematic reviews with meta-regressions that used the BCTTv1 have found interventions with certain BCTs to be more effective than those without [ 50 - 53 ]. A better understanding of the BCTs used in digital interventions for young adult cannabis users would help not only to establish the key ingredients of such interventions but also develop and evaluate effective interventions.

In the absence of any systematic review of the effectiveness and active ingredients of digital interventions designed specifically for CU among community-living young adults, we set out to achieve the following:

• conduct a comprehensive review of digital interventions for preventing, reducing, or ceasing CU among community-living young adults,
• describe the active ingredients (ie, BCTs) in these interventions from the perspective of behavior change science, and
• analyze the effectiveness of these interventions on CU outcomes.

Protocol Registration

We followed the Cochrane Handbook for Systematic Reviews of Interventions [ 54 ] in designing this systematic review and meta-analysis and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines in reporting our findings (see Multimedia Appendix 1 [ 55 ] for the complete PRISMA checklist). This review was registered in PROSPERO (CRD42020196959).

Search Strategy

The search strategy was designed by a health information specialist together with the research team and peer reviewed by another senior information specialist before execution using Peer Review of Electronic Search Strategies for systematic reviews [ 56 ]. The search strategy revolved around three concepts:

• CU (eg, “cannabis,” “marijuana,” and “hashish”)
• Digital interventions (eg, “telehealth,” “website,” “mobile applications,” and “computer”)
• Young adults (eg, “emerging adults” and “students”)

The strategy was initially implemented on March 18, 2020, and again on October 13, 2021, and February 13, 2023. The full, detailed search strategies for each database are presented in Multimedia Appendix 2 .

Information Sources

We searched 7 electronic databases of published literature: CINAHL Complete, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Embase, MEDLINE, PubMed, and PsycINFO. No publication date filters or language restrictions were applied. A combination of free-text keywords and Medical Subject Headings was tailored to the conventions of each database for optimal electronic searching. The research team also manually screened the reference lists of the included articles and the bibliographies of existing systematic reviews [ 18 , 31 , 42 - 45 ] to identify additional relevant studies (snowballing). Finally, a forward citation tracking procedure (ie, searching for articles that cited the included studies) was carried out in Google Scholar.

Inclusion Criteria

The population, intervention, comparison, outcome, and study design process is presented in Multimedia Appendix 3 . The inclusion criteria were as follows: (1) original research articles published in peer-reviewed journals; (2) use of an experimental study design (eg, RCT, cluster RCT, or pilot RCT); (3) studies evaluating the effectiveness (or efficacy) of digital interventions designed specifically to prevent, reduce, or cease CU as well as promote CU self-management or address cannabis-related harm and having CU as an outcome measure; (4) studies targeting young adults, including active and nonactive cannabis users; (5) cannabis users and nonusers not under substance use treatment used as controls in comparator, waitlist, or delayed-treatment groups offered another type of intervention (eg, pharmacotherapy or psychosocial) different from the one being investigated or participants assessed only for CU; and (6) quantitative CU outcomes (frequency and quantity) or cannabis abstinence. Given the availability of numerous CU screening and assessment tools with adequate psychometric properties and the absence of a gold standard in this regard [ 57 ], any instrument capturing aspects of CU was considered. CU outcome measures could be subjective (eg, self-reported number of CU days or joints in the previous 3 months) or objective (eg, drug screening test). CU had to be measured before the intervention (baseline) and at least once after.

Digital CU interventions were defined as web- or mobile-based interventions that included one or more activities (eg, self-directed or interactive psychoeducation or therapy, personalized feedback, peer-to-peer contact, and patient-to-expert communication) aimed at changing CU [ 58 ]. Mobile-based interventions were defined as interventions delivered via mobile phone through SMS text message, multimedia messaging service (ie, SMS text messages that include　multimedia content, such as pictures, videos, or emojis), or mobile apps, whereas web-based interventions (eg, websites and digital platforms) were defined as interventions designed to be accessed on the web (ie, the internet), mainly via computers. Interventions could include self-directed and web-based interventions with human support. We defined young adults as aged 16 to 35 years and included students and nonstudents. While young adulthood is typically defined as covering the ages of 18 to 30 years [ 59 ], we broadened the range given that the age of majority and legal age to purchase cannabis differs across countries and jurisdictions. This was also in line with the age range targeted by several digital CU interventions (college or university students or emerging adults aged 15-24 years) [ 31 , 45 ]. Given the language expertise of the research team members and the available resources, only English- and French-language articles were retained.

Exclusion Criteria

Knowledge synthesis articles, study protocols, and discussion papers or editorials were excluded, as were articles with cross-sectional, cohort, case study or report, pretest-posttest, quasi-experimental, or qualitative designs. Mixed methods designs were included only if the quantitative component was an RCT. We excluded studies if (1) use of substances other than cannabis (eg, alcohol, opioids, or stimulants) was the focus of the digital intervention (though studies that included polysubstance users were retained if CU was assessed and reported separately); (2) CU was not reported separately as an outcome or only attitudes or beliefs regarding, knowledge of, intention to reduce, or readiness or motivation to change CU was measured; and (3) the data reported were unpublished (eg, conferences and dissertations). Studies of traditional face-to-face therapy delivered via teleconference on mobile phones and computers or in a hospital-based setting and informational campaigns (eg, web-based poster presentations or pamphlets) were excluded as well. Studies with samples with a maximum age of <15 years and a minimum age of >35 years were also excluded. Finally, we excluded studies that focused exclusively on people with a mental health disorder or substance use disorder or dependence or on adolescents owing to the particular health care needs of these populations, which may differ from those of young adults [ 1 ].

Data Collection

Selection of studies.

Duplicates were removed from the literature search results in EndNote (version X9.3.3; Clarivate Analytics) using the Bramer method for deduplication of database search results for systematic reviews [ 60 ]. The remaining records were uploaded to Covidence (Veritas Health Innovation), a web-based systematic review management system. A reviewer guide was developed that included screening questions and a detailed description of each inclusion and exclusion criterion based on PICO (population, intervention, comparator, and outcome), and a calibration exercise was performed before each stage of the selection process to maximize consistency between reviewers. Titles and abstracts of studies flagged for possible inclusion were screened first by 2 independent reviewers (GC, BV, PA, and GR; 2 per article) against the eligibility criteria (stage 1). Articles deemed eligible for full-text review were then retrieved and screened for inclusion (stage 2). Full texts were assessed in detail against the eligibility criteria again by 2 reviewers independently. Disagreements between reviewers were resolved through consensus or by consulting a third reviewer.

Data Extraction Process

In total, 2 reviewers (GC, BV, PA, GR, and GF; 2 per article) independently extracted relevant data (or informal evidence) using a data extraction form developed specifically for this review and integrated into Covidence. The form was pilot-tested on 2 randomly selected studies and refined accordingly. Data pertaining to the following domains were extracted from the included studies: (1) Study characteristics included information on the first and corresponding authors, publication year, country of origin, aims and hypotheses, study period, design (including details on randomization and blinding), follow-up times, data collection methods, and types of statistical analysis. (2) Participant characteristics included study target population, participant inclusion and exclusion criteria, sex or gender, mean age, and sample sizes at each data collection time point. (3) Intervention characteristics, for which the research team developed a matrix inspired by the template for intervention description and replication 12-item checklist [ 61 ] to extract informal evidence (ie, intervention descriptions) from the included studies under the headings name of intervention, purpose, underpinning theory of design elements, treatment approach, type of technology (ie, web or mobile) and software used, delivery format (ie, self-directed, human involvement, or both), provider characteristics (if applicable), intervention duration (ie, length of treatment and number of sessions or modules), material and procedures (ie, tools or activities offered, resources provided, and psychoeducational content), tailoring, and unplanned modifications. (4) Comparator characteristics were details of the control or comparison group or groups, including nature (passive vs active), number of groups or clusters (if applicable), type and length of the intervention (if applicable), and number of participants at each data collection time point. (5) Outcome variables, including the primary outcome variable examined in this systematic review, that is, the mean difference in CU frequency before and after the intervention and between the experimental and control or comparison groups. When possible, we examined continuous variables, including CU frequency means and SDs at the baseline and follow-up time points, and standardized regression coefficients (ie, β coefficients and associated 95% CIs). The secondary outcomes examined included other CU outcome variables (eg, quantity of cannabis used and abstinence) and cannabis-related negative consequences (or problems). Details on outcome variables (ie, definition, data time points, and missing data) and measurements (ie, instruments, measurement units, and scales) were also extracted.

In addition, data on user engagement and use of the digital intervention and study attrition rates (ie, dropouts and loss to follow-up) were extracted. When articles had missing data, we contacted the corresponding authors via email (2 attempts were made over a 2-month period) to obtain missing information. Disagreements over the extracted data were limited and resolved through discussion.

Data Synthesis Methods

Descriptive synthesis.

The characteristics of the included studies, study participants, interventions, and comparators were summarized in narrative and table formats. The template for intervention description and replication 12-item checklist [ 61 ] was used to summarize and organize intervention characteristics and assess to what extent the interventions were appropriately described in the included articles. As not all studies had usable data for meta-analysis purposes and because of heterogeneity, we summarized the main findings (ie, intervention effects) of the included studies in narrative and table formats for each outcome of interest in this review.

The BCTs used in the digital interventions were identified from the descriptions of the interventions (ie, experimental groups) provided in the articles as well as any supplementary material and previously published research protocols. A BCT was defined as “an observable, replicable, and irreducible component of an intervention designed to alter or redirect causal processes that regulate behavior” [ 48 ]. The target behavior in this review was the cessation or reduction of CU by young adults. BCTs were identified and coded using the BCTTv1 [ 48 , 49 ], a taxonomy of 93 BCTs organized into 16 hierarchical thematic clusters or categories. Applying the BCTTv1 in a systematic review allows for the comparison and synthesis of evidence across studies in a structured manner. This analysis allows for the identification of the explicit mechanisms underlying the reported behavior change induced by interventions, successful or not, and, thus, avoids making implicit assumptions about what works [ 62 ].

BCT coding was performed by 2 reviewers independently—BV coded all studies, and GC and GF coded a subset of the studies. All reviewers completed web-based training on the BCTTv1, and GF is an experienced implementation scientist who had used the BCTTv1 in prior work [ 63 - 65 ]. The descriptions of the interventions in the articles were read line by line and analyzed for the clear presence of BCTs using the guidelines developed by Michie et al [ 48 ]. For each article, the BCTs identified were documented and categorized using supporting textual evidence. They were coded only once per article regardless of how many times they came up in the text. Disagreements about including a BCT were resolved through discussion. If there was uncertainty about whether a BCT was present, it was coded as absent. Excel (Microsoft Corp) was used to compare the reviewers’ independent BCT coding and generate an overall descriptive synthesis of the BCTs identified. The BCTs were summarized by study and BCT cluster.

Statistical Analysis

Meta-analyses were conducted to estimate the size of the effect of the digital interventions for young adult CU on outcomes of interest at the posttreatment and follow-up assessments compared with control or alternative intervention conditions. The outcome variables considered were (1) CU frequency and other CU outcome variables (eg, quantity of cannabis used and abstinence) at baseline and the posttreatment time point or follow-up measured using standardized instruments of self-reported CU (eg, the timeline followback [TLFB] method) [ 66 ] and (2) cannabis-related negative consequences measured using standardized instruments (eg, the Marijuana Problems Scale) [ 67 ].

Under our systematic review protocol, ≥2 studies were needed for a meta-analysis. On the basis of previous systematic reviews and meta-analyses in the field of digital CU interventions [ 31 , 42 - 45 ], we expected between-study heterogeneity regarding outcome assessment. To minimize heterogeneity, we chose to pool studies with similar outcomes of interest based on four criteria: (1) definition of outcome (eg, CU frequency, quantity consumed, and abstinence), (2) type of outcome variable (eg, days of CU in the previous 90 days, days high per week in the previous 30 days, and number of CU events in the previous month) and measure (ie, instruments or scales), (3) use of validated instruments, and (4) posttreatment or follow-up time points (eg, 2 weeks or 1 month after the baseline or 3, 6, and 12 months after the baseline).

Only articles that reported sufficient statistics to compute a valid effect size with 95% CIs were included in the meta-analyses. In the case of articles that were not independent (ie, more than one published article reporting data from the same clinical trial), only 1 was included, and it was represented only once in the meta-analysis for a given outcome variable regardless of whether the data used to compute the effect size were extracted from the original paper or a secondary analysis paper. We made sure that the independence of the studies included in the meta-analysis of each outcome was respected. In the case of studies that had more than one comparator, we used the effect size for each comparison between the intervention and control groups.

Meta-analyses were conducted only for mean differences based on the change from baseline in CU frequency at 3 months after the baseline as measured using the number of self-reported days of use in the previous month. As the true value of the estimated effect size for outcome variables might vary across different trials and samples, we used a random-effects model given that the studies retained did not have identical target populations. The random-effects model incorporates between-study variation in the study weights and estimated effect size [ 68 ]. In addition, statistical heterogeneity across studies was assessed using I 2 , which measures the proportion of heterogeneity to the total observed dispersion; 25% was considered low, 50% was considered moderate, and 75% was considered high [ 69 ]. Because only 3 studies were included in the meta-analysis [ 70 - 72 ], publication bias could not be assessed. All analyses were completed using Stata (version 18; StataCorp) [ 73 ].

Risk-of-Bias Assessment

The risk of bias (RoB) of the included RCTs was assessed using the Cochrane RoB 2 tool at the outcome level [ 74 ]. Each distinct risk domain (ie, randomization process, deviations from the intended intervention, missing outcome data, measurement of the outcome, and selection of the reported results) was assessed as “low,” “some concerns,” or “high” based on the RoB 2 criteria. In total, 2 reviewers (GC and BV) conducted the assessments independently. Disagreements were discussed, and if not resolved consensually by the 2, the matter was left for a third reviewer (GF) to settle. The assessments were summarized by risk domain and outcome and converted into figures using the RoB visualization tool robvis [ 75 ].

Search Results

The database search generated a total of 13,232 citations, of which 7822 (59.11%) were from the initial search on March 18, 2020, and 2805 (21.2%) and 2605 (19.69%) were from the updates on October 13, 2021, and February 13, 2023, respectively. Figure 1 presents the PRISMA study flow diagram [ 76 ]. Of the 6606 unique records, 6484 (98.15%) were excluded based on title and abstract screening. Full texts of the remaining 1.85% (122/6606) of the records were examined, as were those of 25 more reports found through hand searching. Of these 147 records, 128 (87.1%) were excluded after 3 rounds of full-text screening. Of these 128 records, 39 (30.5%) were excluded for not being empirical research articles (eg, research protocols). Another 28.1% (36/128) were excluded for not meeting our definition of digital CU intervention. The remaining records were excluded for reasons that occurred with a frequency of ≤14%, including young adults not being the target population and the study not meeting our study design criteria (ie, RCT, cluster RCT, or pilot RCT). Excluded studies and reasons for exclusion are listed in Multimedia Appendix 4 . Finally, 19 articles detailing the results of 19 original studies were included.

Description of Studies

Study characteristics.

Multimedia Appendix 5 [ 70 - 72 , 77 - 92 ] describes the general characteristics of the 19 included studies. The studies were published between 2010 and 2023, with 58% (11/19) published in 2018 or later. A total of 53% (10/19) of the studies were conducted in the United States [ 77 - 86 ], 11% (2/19) were conducted in Canada [ 87 , 88 ], 11% (2/19) were conducted in Australia [ 71 , 89 ], 11% (2/19) were conducted in Germany [ 72 , 90 ], 11% (2/19) were conducted in Switzerland [ 70 , 91 ], and 5% (1/19) were conducted in Sweden [ 92 ]. A total of 79% (15/19) were RCTs [ 70 - 72 , 77 , 79 , 81 - 83 , 86 - 92 ], and 21% (4/19) were pilot RCTs [ 78 , 80 , 84 , 85 ].

Participant Characteristics

The studies enrolled a total of 6710 participants—3229 (48.1%) in the experimental groups, 3358 (50%) in the control groups, and the remaining 123 (1.8%) from 1 study [ 82 ] where participant allocation to the intervention condition was not reported. Baseline sample sizes ranged from 49 [ 81 ] to 1292 [ 72 ] (mean 352.89, SD 289.50), as shown in Multimedia Appendix 5 . Participant mean ages ranged from 18.03 (SD 0.31) [ 79 ] to 35.3 (SD 12.6) years [ 88 ], and the proportion of participants who identified as female ranged from 24.7% [ 91 ] to 84.1% [ 80 ].

Of the 19 included studies, 10 (53%) targeted adults aged ≥18 years, of which 7 (70%) studies focused on adults who had engaged in past-month CU [ 70 , 71 , 80 , 84 , 85 , 90 , 91 ], 2 (20%) studies included adults who wished to reduce or cease CU [ 72 , 89 ], and 1 (10%) study focused on noncollege adults with a moderate risk associated with CU [ 88 ]. Sinadinovic et al [ 92 ] targeted young adults aged ≥16 years who had used cannabis at least once a week in the previous 6 months. The remaining 8 studies targeted college or university students (aged ≥17 y) specifically, of which 7 (88%) studies focused solely on students who reported using cannabis [ 78 , 79 , 81 - 83 , 86 , 87 ] and 1 (12%) study focused solely on students who did not report past-month CU (ie, abstainers) [ 77 ].

Intervention Characteristics

The 19 included studies assessed nine different digital interventions: (1) 5 (26%) evaluated Marijuana eCHECKUP TO GO (e-TOKE), a commercially available electronic intervention used at colleges throughout the United States and Canada [ 77 , 78 , 81 - 83 ]; (2) 2 (11%) examined the internationally known CANreduce program [ 70 , 91 ]; (3) 2 (11%) evaluated the German Quit the Shit program [ 72 , 90 ]; (4) 2 (11%) assessed a social media–delivered, physical activity–focused cannabis intervention [ 84 , 85 ]; (5) 1 (5%) investigated the Swedish Cannabishjälpen intervention [ 92 ]; (6) 1 (5%) evaluated the Australian Grassessment: Evaluate Your Use of Cannabis website program [ 89 ]; (7) 1 (5%) assessed the Canadian Ma réussite, mon choix intervention [ 87 ]; (8) 1 (5%) examined the Australian Reduce Your Use: How to Break the Cannabis Habit program [ 71 ]; and (9) 4 (21%) each evaluated a unique no-name intervention described as a personalized feedback intervention (PFI) [ 79 , 80 , 86 , 88 ]. Detailed information regarding the characteristics of all interventions as reported in each included study is provided in Multimedia Appendix 6 [ 70 - 72 , 77 - 113 ] and summarized in the following paragraphs.

In several studies (8/19, 42%), the interventions were designed to support cannabis users in reducing or ceasing their consumption [ 70 , 72 , 80 , 87 , 89 - 92 ]. In 37% (7/19) of the studies, the interventions aimed at reducing both CU and cannabis-related consequences [ 79 , 81 - 85 , 88 ]. Other interventions focused on helping college students think carefully about the decision to use cannabis [ 77 , 78 ] and on reducing either cannabis-related problems among undergraduate students [ 86 ] or symptoms associated with CU disorder in young adults [ 71 ].

In 26% (5/19) of the studies, theory was used to inform intervention design along with a clear rationale for theory use. Of these 5 articles, only 1 (20%) [ 87 ] reported using a single theory of behavior change, the theory of planned behavior [ 114 ]. A total of 21% (4/19) of the studies selected only constructs of theories (or models) for their intervention design. Of these 4 studies, 2 (50%) evaluated the same intervention [ 72 , 90 ], which focused on principles of self-regulation and self-control theory [ 93 ]; 1 (25%) [ 70 ] used the concept of adherence-focused guidance enhancement based on the supportive accountability model of guidance [ 94 ]; and 1 (25%) [ 71 ] reported that intervention design was guided by the concept of self-behavioral management.

The strategies (or approaches) used in the delivery of the digital interventions were discussed in greater detail in 84% (16/19) of the articles [ 70 - 72 , 79 - 81 , 83 - 92 ]. Many of these articles (9/19, 47%) reported using a combination of approaches based on CBT or motivational interviewing (MI) [ 70 , 71 , 79 , 83 - 85 , 90 - 92 ]. PFIs were also often mentioned as an approach to inform intervention delivery [ 7 , 71 , 79 , 86 - 88 ].

More than half (13/19, 68%) of all the digital interventions were asynchronous and based on a self-guided approach without support from a counselor or therapist. The study by Côté et al [ 87 ] evaluated the efficacy of a web-based tailored intervention focused on reinforcing a positive attitude toward and a sense of control over cannabis abstinence through psychoeducational messages delivered by a credible character in short video clips and personalized reinforcement messages. Lee et al [ 79 ] evaluated a brief, web-based personalized feedback selective intervention based on the PFI approach pioneered by Marlatt et al [ 95 ] for alcohol use prevention and on the MI approach described by Miller and Rollnick [ 96 ]. Similarly, Rooke et al [ 71 ] combined principles of MI and CBT to develop a web-based intervention delivered via web modules, which were informed by previous automated feedback interventions targeting substance use. The study by Copeland et al [ 89 ] assessed the short-term effectiveness of Grassessment: Evaluate Your Use of Cannabis, a brief web-based, self-complete intervention based on motivational enhancement therapy that included personalized feedback messages and psychoeducational material. In the studies by Buckner et al [ 80 ], Cunningham et al [ 88 ], and Walukevich-Dienst et al [ 86 ], experimental groups received a brief web-based PFI available via a computer. A total of 16% (3/19) of the studies [ 77 , 78 , 82 ] applied a program called the Marijuana eCHECKUP TO GO (e-TOKE) for Universities and Colleges, which was presented as a web-based, norm-correcting, brief preventive and intervention education program designed to prompt self-reflection on consequences and consideration of decreasing CU among students. Riggs et al [ 83 ] developed and evaluated an adapted version of e-TOKE that provided participants with university-specific personalized feedback and normative information based on protective behavioral strategies for CU [ 97 ]. Similarly, Goodness and Palfai [ 81 ] tested the efficacy of eCHECKUP TO GO-cannabis, a modified version of e-TOKE combining personalized feedback, norm correction, and a harm and frequency reduction strategy where a “booster” session was provided at 3 months to allow participants to receive repeated exposure to the intervention.

In the remaining 32% (6/19) of the studies, which examined 4 different interventions, the presence of a therapist guide was reported. The intervention evaluated by Sinadinovic et al [ 92 ] combined principles of psychoeducation, MI, and CBT organized into 13 web-based modules and a calendar involving therapist guidance, recommendations, and personal feedback. In total, 33% (2/6) of these studies evaluated a social media–delivered intervention with e-coaches that combined principles of MI and CBT and a harm reduction approach for risky CU [ 84 , 85 ]. Schaub et al [ 91 ] evaluated the efficacy of CANreduce, a web-based self-help intervention based on both MI and CBT approaches, using automated motivational and feedback emails, chat with a counselor, and web-based psychoeducational modules. Similarly, Baumgartner et al [ 70 ] investigated the effectiveness of CANreduce 2.0, a modified version of CANreduce, using semiautomated motivational and adherence-focused guidance-based email feedback with or without a personal online coach. The studies by Tossman et al [ 72 ] and Jonas et al [ 90 ] used a solution-focused approach and MI to evaluate the effectiveness of the German Quit the Shit web-based program that involves weekly feedback provided by counselors.

In addition to using different intervention strategies or approaches, the interventions were diverse in terms of the duration and frequency of the program (eg, web-based activities, sessions, or modules). Of the 12 articles that provided details in this regard, 2 (17%) on the same intervention described it as a brief 20- to 45-minute web-based program [ 77 , 78 ], 2 (17%) on 2 different interventions reported including 1 or 2 modules per week for a duration of 6 weeks [ 71 , 92 ], and 7 (58%) on 4 different interventions described them as being available over a longer period ranging from 6 weeks to 3 months [ 70 , 72 , 79 , 84 , 85 , 87 , 90 , 91 ].

Comparator Types

A total of 42% (8/19) of the studies [ 72 , 77 - 80 , 85 , 87 , 92 ] used a passive comparator only, namely, a waitlist control group ( Multimedia Appendix 5 ). A total of 26% (5/19) of the studies used an active comparator only where participants were provided with minimal general health feedback regarding recommended guidelines for sleep, exercise, and nutrition [ 81 , 82 ]; strategies for healthy stress management [ 83 ]; educational materials about risky CU [ 88 ]; or access to a website containing information about cannabis [ 71 ]. In another 21% (4/19) of the studies, which used an active comparator, participants received the same digital intervention minus a specific component: a personal web-based coach [ 70 ], extended personalized feedback [ 89 ], web-based chat counseling [ 91 ], or information on risks associated with CU [ 86 ]. A total of 21% (4/19) of the studies had more than one control group [ 70 , 84 , 90 , 91 ].

Outcome Variable Assessment and Summary of Main Findings of the Studies

The methodological characteristics and major findings of the included studies (N=19) are presented in Multimedia Appendix 7 [ 67 , 70 - 72 , 77 - 92 , 115 - 120 ] and summarized in the following sections for each outcome of interest in this review (ie, CU and cannabis-related consequences). Of the 19 studies, 11 (58%) were reported as efficacy trials [ 7 , 77 , 79 , 81 - 83 , 86 - 88 , 91 , 92 ], and 8 (42%) were reported as effectiveness trials [ 70 - 72 , 78 , 84 , 85 , 89 , 90 ].

Across all the included studies (19/19, 100%), participant attrition rates ranged from 1.6% at 1 month after the baseline [ 77 , 78 ] to 75.1% at the 3-month follow-up [ 70 ]. A total of 37% (7/19) of the studies assessed and reported results regarding user engagement [ 71 , 78 , 84 , 85 , 90 - 92 ] using different types of metrics. In one article on the Marijuana eCHECKUP TO GO (e-TOKE) web-based program [ 78 ], the authors briefly reported that participation was confirmed for 98.1% (158/161) of participants in the intervention group. In 11% (2/19) of the studies, which were on a similar social media–delivered intervention [ 84 , 85 ], user engagement was quantified by tallying the number of comments or posts and reactions (eg, likes and hearts) left by participants. In both studies [ 84 , 85 ], the intervention group, which involved a CU-related Facebook page, displayed greater interactions than the control groups, which involved a Facebook page unrelated to CU. One article [ 84 ] reported that 80% of participants in the intervention group posted at least once (range 0-60) and 50% posted at least weekly. In the other study [ 85 ], the results showed that intervention participants engaged (ie, posting or commenting or clicking reactions) on average 47.9 times each over 8 weeks. In total, 11% (2/19) of the studies [ 90 , 91 ] on 2 different web-based intervention programs, both consisting of web documentation accompanied by chat-based counseling, measured user engagement either by average duration or average number of chat sessions. Finally, 16% (3/19) of the studies [ 71 , 91 , 92 ], which involved 3 different web-based intervention programs, characterized user engagement by the mean number of web modules completed per participant. Overall, the mean number of web modules completed reported in these articles was quite similar: 3.9 out of 13 [ 92 ] and 3.2 [ 91 ] and 3.5 [ 71 ] out of 6.

Assessment of CU

As presented in Multimedia Appendix 7 , the included studies differed in terms of how they assessed CU, although all used at least one self-reported measure of frequency. Most studies (16/19, 84%) measured frequency by days of use, including days of use in the preceding week [ 91 ] or 2 [ 80 ], days of use in the previous 30 [ 70 - 72 , 78 , 84 - 86 , 88 - 90 ] or 90 days [ 79 , 81 , 82 ], and days high per week [ 83 ]. Other self-reported measures of CU frequency included (1) number of CU events in the previous month [ 87 , 90 ], (2) cannabis initiation or use in the previous month (ie, yes or no) [ 77 ], and (3) days without CU in the previous 7 days [ 92 ]. In addition to measuring CU frequency, 42% (8/19) of the studies also assessed CU via self-reported measures of quantity used, including estimated grams consumed in the previous week [ 92 ] or 30 days [ 72 , 85 , 90 ] and the number of standard-sized joints consumed in the previous 7 days [ 91 ] or the previous month [ 70 , 71 , 89 ].

Of the 19 articles included, 10 (53%) [ 70 - 72 , 80 , 84 - 86 , 89 , 90 , 92 ] reported using a validated instrument to measure CU frequency or quantity, including the TLFB instrument [ 66 ] (n=9, 90% of the studies) and the Marijuana Use Form (n=1, 10% of the studies); 1 (10%) [ 79 ] reported using CU-related questions from an adaptation of the Global Appraisal of Individual Needs–Initial instrument [ 115 ]; and 30% (3/10) [ 81 , 82 , 91 ] reported using a questionnaire accompanied by a calendar or a diary of consumption. The 19 studies also differed with regard to their follow-up time measurements for assessing CU, ranging from 2 weeks after the baseline [ 80 ] to 12 months after randomization [ 90 ], although 12 (63%) of the studies included a 3-month follow-up assessment [ 70 - 72 , 79 , 81 , 82 , 84 , 85 , 88 , 90 - 92 ].

Of all studies assessing and reporting change in CU frequency from baseline to follow-up assessments (19/19, 100%), 47% (9/19) found statistically significant differences between the experimental and control groups [ 70 - 72 , 80 , 81 , 83 , 85 , 87 , 91 ]. Importantly, 67% (6/9) of these studies showed that participants in the experimental groups exhibited greater decreases in CU frequency 3 months following the baseline assessment compared with participants in the control groups [ 70 - 72 , 81 , 85 , 91 ], 22% (2/9) of the studies showed greater decreases in CU frequency at 6 weeks after the baseline assessment [ 71 , 83 ], 22% (2/9) of the studies showed greater decreases in CU frequency at 6 months following the baseline assessment [ 81 , 85 ], 11% (1/9) of the studies showed greater decreases in CU frequency at 2 weeks after the baseline [ 80 ], and 11% (1/9) of the studies showed greater decreases in CU frequency at 2 months after treatment [ 87 ].

In the study by Baumgartner et al [ 70 ], a reduction in CU days was observed in all groups, but the authors reported that the difference was statistically significant only between the intervention group with the service team and the control group (the reduction in the intervention group with social presence was not significant). In the study by Bonar et al [ 85 ], the only statistically significant difference between the intervention and control groups at the 3- and 6-month follow-ups involved total days of cannabis vaping in the previous 30 days. Finally, in the study by Buckner et al [ 80 ], the intervention group had less CU than the control group 2 weeks after the baseline; however, this was statistically significant only for participants with moderate or high levels of social anxiety.

Assessment of Cannabis-Related Negative Consequences

A total of 53% (10/19) of the studies also assessed cannabis-related negative consequences [ 78 - 84 , 86 , 88 , 92 ]. Of these 10 articles, 8 (80%) reported using a validated self-report instrument: 4 (50%) [ 81 , 82 , 86 , 88 ] used the 19-item Marijuana Problems Scale [ 67 ], 2 (25%) [ 78 , 79 ] used the 18-item Rutgers Marijuana Problem Index [ 121 , 122 ], and 2 (25%) [ 80 , 84 ] used the Brief Marijuana Consequences Questionnaire [ 116 ]. Only 10% (1/10) of the studies [ 92 ] used a screening tool, the Cannabis Abuse Screening Test [ 117 , 118 ]. None of these 10 studies demonstrated a statistically significant difference between the intervention and control groups. Of note, Walukevich-Dienst et al [ 86 ] found that women (but not men) who received an web-based PFI with additional information on CU risks reported significantly fewer cannabis-related problems than did women in the control group at 1 month after the intervention ( B =−1.941; P =.01).

Descriptive Summary of BCTs Used in Intervention Groups

After the 19 studies included in this review were coded, a total of 184 individual BCTs targeting CU in young adults were identified. Of these 184 BCTs, 133 (72.3% ) were deemed to be present beyond a reasonable doubt, and 51 (27.7%) were deemed to be present in all probability. Multimedia Appendix 8 [ 48 , 70 - 72 , 77 - 92 ] presents all the BCTs coded for each included study summarized by individual BCT and BCT cluster.

The 184 individual BCTs coded covered 38% (35/93) of the BCTs listed in the BCTTv1 [ 48 ]. The number of individual BCTs identified per study ranged from 5 to 19, with two-thirds of the 19 studies (12/19, 63%) using ≤9 BCTs (mean 9.68). As Multimedia Appendix 8 shows, at least one BCT fell into 13 of the 16 possible BCT clusters. The most frequent clusters were feedback monitoring , natural consequences , goal planning , and comparison of outcomes .

The most frequently coded BCTs were (1) feedback on behavior (BCT 2.2; 17/19, 89% of the studies; eg, “Once a week, participants receive detailed feedback by their counselor on their entries in diary and exercises. Depending on the involvement of each participant, up to seven feedbacks are given” [ 90 ]), (2) social support (unspecified) (BCT 3.1; 15/19, 79% of the studies; eg, “The website also features [...] blogs from former cannabis users, quick assist links, and weekly automatically generated encouragement emails” [ 71 ]), and (3) pros and cons (BCT 9.2; 14/19, 74% of the studies; eg, “participants are encouraged to state their personal reasons for and against their cannabis consumption, which they can review at any time, so they may reflect on what they could gain by successfully completing the program” [ 70 ]). Other commonly identified BCTs included social comparison (BCT 6.2; 12/19, 63% of the studies) and information about social and environmental consequences (BCT 5.3; 11/19, 58% of the studies), followed by problem solving (BCT 2.1; 10/19, 53% of the studies) and information about health consequences (BCT 5.1; 10/19, 53% of the studies).

RoB Assessment

Figure 2 presents the overall assessment of risk in each domain for all the included studies, whereas Figure 3 [ 70 - 72 , 77 - 92 ] summarizes the assessment of each study at the outcome level for each domain in the Cochrane RoB 2 [ 74 ].

Figure 2 shows that, of the included studies, 93% (27/29) were rated as having a “low” RoB arising from the randomization process (ie, selection bias) and 83% (24/29) were rated as having a “low” RoB due to missing data (ie, attrition bias). For bias due to deviations from the intended intervention (ie, performance bias), 72% (21/29) were rated as having a “low” risk, and for selective reporting of results, 59% (17/29) were rated as having a “low” risk. In the remaining domain regarding bias in measurement of the outcome (ie, detection bias), 48% (14/29) of the studies were deemed to present “some concerns,” mainly owing to the outcome assessment not being blinded (eg, self-reported outcome measure of CU). Finally, 79% (15/19) of the included studies were deemed to present “some concerns” or were rated as having a “high” RoB at the outcome level ( Figure 3 [ 70 - 72 , 77 - 92 ]). The RoB assessment for CU and cannabis consequences of each included study is presented in Multimedia Appendix 9 [ 70 - 72 , 77 - 92 ].

Meta-Analysis Results

Due to several missing data points and despite contacting the authors, we were able to carry out only 1 meta-analysis of our primary outcome, CU frequency. Usable data were retrieved from only 16% (3/19) [ 70 - 72 ] of the studies included in this review. These 3 studies provided sufficient information to calculate an effect size, including mean differences based on change-from-baseline measurements and associated 95% CIs (or SE of the mean difference) and sample sizes per intervention and comparison conditions. The reasons for excluding the other 84% (16/19) of the studies included heterogeneity in outcome variables or measurements, inconsistent results, and missing data ( Multimedia Appendix 10 [ 77 - 92 ]).

Figure 4 [ 70 - 72 ] illustrates the mean differences and associated 95% CIs of 3 unique RCTs [ 70 - 72 ] that provided sufficient information to allow for the measurement of CU frequency at 3 months after the baseline relative to a comparison condition in terms of the number of self-reported days of use in the previous month using the TLFB method. Overall, the synthesized effect of digital interventions for young adult cannabis users on CU frequency, as measured using days of use in the previous month, was −6.79 (95% CI −9.59 to −4.00). This suggests that digital CU interventions had a statistically significant effect ( P <.001) on reducing CU frequency at the 3-month follow-up compared with the control conditions (both passive and active controls). The results of the meta-analysis also showed low between-study heterogeneity ( I 2 =48.3%; P =.12) across the 3 included studies.

The samples of the 3 studies included in the meta-analysis varied in size from 225 to 1292 participants (mean 697.33, SD 444.11), and the mean age ranged from 24.7 to 31.88 years (mean 26.38, SD 3.58 years). These studies involved 3 different digital interventions and used different design approaches to assess intervention effectiveness. One study assessed the effectiveness of a web-based counseling program (ie, Quit the Shit) against a waitlist control [ 72 ], another examined the effectiveness of a fully self-guided web-based treatment program for CU and related problems (ie, Reduce Your Use: How to Break the Cannabis Habit) against a control condition website consisting of basic educational information on cannabis [ 71 ], and the third used a 3-arm RCT design to investigate whether the effectiveness of a minimally guided internet-based self-help intervention (ie, CANreduce 2.0) might be enhanced by implementing adherence-focused guidance and emphasizing the social presence factor of a personal e-coach [ 70 ].

Summary of Principal Findings

The primary aim of this systematic review was to evaluate the effectiveness of digital interventions in addressing CU among community-living young adults. We included 19 randomized controlled studies representing 9 unique digital interventions aimed at preventing, reducing, or ceasing CU and evaluated the effects of 3 different digital interventions on CU. In summary, the 3 digital interventions included in the meta-analysis proved superior to control conditions in reducing the number of days of CU in the previous month at the 3-month follow-up.

Our findings are consistent with those of 2 previous meta-analyses by Olmos et al [ 43 ] and Tait et al [ 44 ] and with the findings of a recently published umbrella review of systematic reviews and meta-analyses of RCTs [ 123 ], all of which revealed a positive effect of internet- and computer-based interventions on CU. However, a recent systematic review and meta-analysis by Beneria et al [ 45 ] found that web-based CU interventions did not significantly reduce CU. Beneria et al [ 45 ] included studies with different intervention programs that targeted diverse population groups (both adolescents and young adults) and use of more than one substance (eg, alcohol and cannabis). In our systematic review, a more conservative approach was taken—we focused specifically on young adults and considered interventions targeting CU only. Although our results indicate that digital interventions hold great promise in terms of effectiveness, an important question that remains unresolved is whether there is an optimal exposure dose in terms of both duration and frequency that might be more effective. Among the studies included in this systematic review, interventions varied considerably in terms of the number of psychoeducational modules offered (from 2 to 13), time spent reviewing the material, and duration (from a single session to a 12-week spread period). Our results suggest that an intervention duration of at least 6 weeks yields better results.

Another important finding of this review is that, although almost half (9/19, 47%) of the included studies observed an intervention effect on CU frequency, none reported a statistically significant improvement in cannabis-related negative consequences, which may be considered a more distal indicator. More than half (10/19, 53%) of the included studies investigated this outcome. It seems normal to expect to find an effect on CU frequency given that reducing CU is often the primary objective of interventions and because the motivation of users’ is generally focused on changing consumption behavior. It is plausible to think that the change in behavior at the consumption level must be maintained over time before an effect on cannabis-related negative consequences can be observed. However, our results showed that, in all the included studies, cannabis-related negative consequences and change in behavior (CU frequency) were measured at the same time point, namely, 3 months after the baseline. Moreover, Grigsby et al [ 124 ] conducted a scoping review of risk and protective factors for CU and suggested that interventions to reduce negative CU consequences should prioritize multilevel methods or strategies “to attenuate the cumulative risk from a combination of psychological, contextual, and social influences.”

A secondary objective of this systematic review was to describe the active ingredients used in digital interventions for CU among young adults. The vast majority of the interventions were based on either a theory or an intervention approach derived from theories such as CBT, MI, and personalized feedback. From these theories and approaches stem behavior change strategies or techniques, commonly known as BCTs. Feedback on behavior , included in the feedback monitoring BCT cluster, was the most common BCT used in the included studies. This specific BCT appears to be a core strategy in behavior change interventions [ 125 , 126 ]. In their systematic review of remotely delivered alcohol or substance misuse interventions for adults, Howlett et al [ 53 ] found that feedback on behavior , problem solving , and goal setting were the most frequently used BCTs in the included studies. In addition, this research group noted that the most promising BCTs for alcohol misuse were avoidance/reducing exposure to cues for behavior , pros and cons , and self-monitoring of behavior, whereas 2 very promising strategies for substance misuse in general were problem solving and self-monitoring of behavior . In our systematic review, in addition to feedback on behavior , the 6 most frequently used BCTs in the included studies were social support , pros and cons , social comparison , problem solving , information about social and environmental consequences , and information about health consequences . Although pros and cons and problem solving were present in all 3 studies of digital interventions included in our meta-analysis, avoidance/reducing exposure to cues for behavior was reported in only 5% (1/19) of the articles, and feedback on behavior was more frequently used than self-monitoring of behavior. However, it should be noted that the review by Howlett et al [ 53 ] examined digital interventions for participants with alcohol or substance misuse problems, whereas in this review, we focused on interventions that targeted CU from a harm reduction perspective. In this light, avoidance/reducing exposure to cues for behavior may be a BCT better suited to populations with substance misuse problems. Lending support to this, a meta-regression by Garnett et al [ 127 ] and a Cochrane systematic review by Kaner et al [ 128 ] both found interventions that used behavior substitution and credible source to be associated with greater reduction in excessive alcohol consumption compared with interventions that used other BCTs.

Beyond the number and types of BCTs used, reflecting on the extent to which each BCT in a given intervention suits (or does not suit) the targeted determinants (ie, behavioral and environmental causes) is crucial for planning intervention programs [ 26 ]. It is important when designing digital CU interventions not merely to pick a combination of BCTs that have been associated with effectiveness. Rather, the active ingredients must fit the determinants that the interventionists seek to influence. For example, action planning would be more relevant as a BCT for young adults highly motivated and ready to take action on their CU than would pros and cons , which aims instead to bolster motivation. Given that more than half of all digital interventions are asynchronous and based on a self-guided approach and do not offer counselor or therapist support, a great deal of motivation is required to engage in intervention and behavior change. Therefore, it is essential that developers consider the needs and characteristics of the targeted population to tailor intervention strategies (ie, BCTs) for successful behavior change (eg, tailored to the participant’s stage of change). In most of the digital interventions included in this systematic review, personalization was achieved through feedback messages about CU regarding descriptive norms, motives, risks and consequences, and costs, among other things.

Despite the high number of recent studies conducted in the field of digital CU interventions, most of the included articles in our review (17/19, 89%) reported on the development and evaluation of web-based intervention programs. A new generation of health intervention modalities such as mobile apps and social media has drawn the attention of researchers in the past decade and is currently being evaluated. In this regard, the results from a recently published scoping review [ 129 ], which included 5 studies of mobile apps for nonmedical CU, suggested that these novel modes of intervention delivery demonstrated adequate feasibility and acceptability. Nevertheless, the internet remains a powerful and convenient medium for reaching young adults with digital interventions intended to support safe CU behaviors [ 123 , 130 ].

Quality of Evidence

The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach [ 131 - 133 ] was used to assess the quality of the evidence reviewed. It was deemed to be moderate for the primary outcome of this review, that is, CU frequency in terms of days of use in the previous month (see the summary of evidence in Multimedia Appendix 11 [ 70 , 72 ]). The direction of evidence was broadly consistent—in all 3 RCT studies [ 70 - 72 ] included in the meta-analysis, participants who received digital CU interventions reduced their consumption compared with those who received no or minimal interventions. The 3 RCTs were similar in that they all involved a web-based, multicomponent intervention program aimed at reducing or ceasing CU. However, the interventions did differ or vary in terms of several characteristics, including the strategies used, content, frequency, and duration. Given the small number of studies included in the meta-analysis, we could not conclude with certainty which intervention components, if any, contributed to the effect estimate observed.

Although inconsistency, indirectness, and imprecision were not major issues in the body of evidence, we downgraded the evidence from high to moderate quality on account of RoB assessments at the outcome level. The 3 RCT studies included in the meta-analysis were rated as having “some concerns” of RoB, mainly due to lack of blinding, which significantly reduced our certainty relative to subjective outcomes (ie, self-reported measures of CU frequency). A positive feature of these digital intervention trials is that most procedures are fully automated, and so there was typically a low RoB regarding randomization procedures, allocation to different conditions, and intervention delivery. It is impossible to blind participants to these types of behavior change interventions, and although some researchers have made attempts to counter the impact of this risk, performance bias is an inescapable issue in RCT studies of this kind. Blinding of intervention providers was not an issue in the 3 RCTs included in the meta-analysis because outcome data collection was automated. However, this same automated procedure made it very difficult to ensure follow‐up. Consequently, attrition was another source of bias in these RCT studies [ 70 - 72 ]. The participants lost to follow-up likely stopped using the intervention. However, there is no way of determining whether these people would have benefited more or less than the completers if they had seen the trial through.

The 3 RCTs included in the meta-analysis relied on subjective self-reported measures of CU at baseline and follow‐up, which are subject to recall and social desirability bias. However, all 3 studies used a well-validated instrument of measurement to determine frequency of CU, the TLFB [ 66 ]. This is a widely used, subjective self-report tool for measuring frequency (or quantity) of substance use (or abstinence). It is considered a reliable measure of CU [ 134 , 135 ]. Finally, it should be pointed out that any potential bias related to self‐reported CU frequency would have affected both the intervention and control groups (particularly in cases in which control groups received cannabis‐related information), and thus, it was unlikely to account for differential intervention effects. Moreover, we found RoB due to selective reporting in some studies owing mainly to the absence of any reference to a protocol. Ultimately, these limitations may have biased the results of the meta-analysis. Consequently, future research is likely to further undermine our confidence in the effect estimate we observed and report considerably different estimates.

Strengths and Limitations

Our systematic review and meta-analysis has a number of strengths: (1) we included only randomized controlled studies to ensure that the included studies possessed a rigorous research design, (2) we focused specifically on cannabis (rather than combining multiple substances), (3) we assessed the effectiveness of 3 different digital interventions on CU frequency among community-living young adults, and (4) we performed an exhaustive synthesis and comparison of the BCTs used in the 9 digital interventions examined in the 19 studies included in our review based on the BCTTv1.

Admittedly, this systematic review and meta-analysis has limitations that should be recognized. First, although we searched a range of bibliographic databases, the review was limited to articles published in peer-reviewed journals in English or French. This may have introduced publication bias given that articles reporting positive effects are more likely to be published than those with negative or equivocal results. Consequently, the studies included in this review may have overrepresented the statistically significant effects of digital CU interventions.

Second, only a small number of studies were included in the meta-analyses because many studies did not provide adequate statistical information for calculating and synthesizing effect sizes, although significant efforts were made to contact the authors in case of missing data. Because of the small sample size used in the meta-analysis, the effect size estimates may not be highly reflective of the true effects of digital interventions on CU frequency among young adults. Furthermore, synthesizing findings across studies that evaluated different modalities of web-based intervention programs (eg, fully self-guided vs with therapist guidance) and types of intervention approaches (eg, CBT, MI, and personalized feedback) may have introduced bias in the meta-analytical results due to the heterogeneity of the included studies, although heterogeneity was controlled for using a random-effects model and our results indicated low between-study heterogeneity.

Third, we took various measures to ensure that BCT coding was carried out rigorously throughout the data extraction and analysis procedures: (1) all coders received training on how to use the BCTTv1; (2) all the included articles were read line by line so that coders became familiar with intervention descriptions before initiating BCT coding; (3) the intervention description of each included article was double coded after a pilot calibration exercise with all coders, and any disagreements regarding the presence or absence of a BCT were discussed and resolved with a third party; and (4) we contacted the article authors when necessary and possible for further details on the BCTs they used. However, incomplete reporting of intervention content is a recognized issue [ 136 ], which may have resulted in our coding BCTs incorrectly as present or absent. Reliably specifying the BCTs used in interventions allows their active ingredients to be identified, their evidence to be synthesized, and interventions to be replicated, thereby providing tangible guidance to programmers and researchers to develop more effective interventions.

Finally, although this review identified the BCTs used in digital interventions, our approach did not allow us to draw conclusions regarding their effectiveness. Coding BCTs simply as present or absent does not consider the frequency, intensity, and quality with which they were delivered. For example, it is unclear how many individuals should self‐monitor their CU. In addition, the quality of BCT implementation may be critical in digital interventions where different graphics and interface designs and the usability of the BCTs used can have considerable influence on the level of user engagement [ 137 ]. In the future, it may be necessary to develop new methods to evaluate the dosage of individual BCTs in digital health interventions and characterize their implementation quality to assess their effectiveness [ 128 , 138 ]. Despite its limitations, this review suggests that digital interventions represent a promising avenue for preventing, reducing, or ceasing CU among community-living young adults.

Conclusions

The results of this systematic review and meta-analysis lend support to the promise of digital interventions as an effective means of reducing recreational CU frequency among young adults. Despite the advent and popularity of smartphones, web-based interventions remain the most common mode of delivery for digital interventions. The active ingredients of digital interventions are varied and encompass a number of clusters of the BCTTv1, but a significant number of BCTs remain underused. Additional research is needed to further investigate the effectiveness of these interventions on CU and key outcomes at later time points. Finally, a detailed assessment of user engagement with digital interventions for CU and understanding which intervention components are the most effective remain important research gaps.

Acknowledgments

The authors would like to thank Bénédicte Nauche, Miguel Chagnon, and Paul Di Biase for their valuable support with the search strategy development, statistical analysis, and linguistic revision, respectively. This work was supported by the Ministère de la Santé et des Services sociaux du Québec as part of a broader study aimed at developing and evaluating a digital intervention for young adult cannabis users. Additional funding was provided by the Research Chair in Innovative Nursing Practices. The views and opinions expressed in this manuscript do not necessarily reflect those of these funding entities.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

JC contributed to conceptualization, methodology, formal analysis, writing—original draft, supervision, and funding acquisition. GC contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, visualization, and project administration. BV contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, and visualization. PA contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, visualization, and project administration. GR contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—review and editing. GF contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—review and editing. DJA contributed to conceptualization, methodology, formal analysis, writing—review and editing, and funding acquisition.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Detailed search strategies for each database.

Population, intervention, comparison, outcome, and study design strategy.

Excluded studies and reasons for exclusion.

Study and participant characteristics.

Description of intervention characteristics in the included articles.

Summary of methodological characteristics and major findings of the included studies categorized by intervention name.

Behavior change techniques (BCTs) coded in each included study summarized by individual BCT and BCT cluster.

Risk-of-bias assessment of each included study for cannabis use and cannabis consequences.

Excluded studies and reasons for exclusion from the meta-analysis.

Summary of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation tool.

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Abbreviations

Edited by T Leung, G Eysenbach; submitted 30.11.23; peer-reviewed by H Sedrati; comments to author 02.01.24; revised version received 09.01.24; accepted 08.03.24; published 17.04.24.

©José Côté, Gabrielle Chicoine, Billy Vinette, Patricia Auger, Geneviève Rouleau, Guillaume Fontaine, Didier Jutras-Aswad. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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For more open and equitable public discussions on social media, try “meronymity”

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Have you ever felt reluctant to share ideas during a meeting because you feared judgment from senior colleagues? You’re not alone. Research has shown this pervasive issue can lead to a lack of diversity in public discourse, especially when junior members of a community don’t speak up because they feel intimidated.

Anonymous communication can alleviate that fear and empower individuals to speak their minds, but anonymity also eliminates important social context and can quickly skew too far in the other direction, leading to toxic or hateful speech.

MIT researchers addressed these issues by designing a framework for identity disclosure in public conversations that falls somewhere in the middle, using a concept called “meronymity.”

Meronymity (from the Greek words for “partial” and “name”) allows people in a public discussion space to selectively reveal only relevant, verified aspects of their identity.

The researchers implemented meronymity in a communication system they built called LiTweeture, which is aimed at helping junior scholars use social media to ask research questions.

In LiTweeture, users can reveal a few professional facts, such as their academic affiliation or expertise in a certain field, which lends credibility to their questions or answers while shielding their exact identity.

Users have the flexibility to choose what they reveal about themselves each time they compose a social media post. They can also leverage existing relationships for endorsements that help queries reach experts they otherwise might be reluctant to contact.

During a monthlong study, junior academics who tested LiTweeture said meronymous communication made them feel more comfortable asking questions and encouraged them to engage with senior scholars on social media.

And while this study focused on academia, meronymous communication could be applied to any community or discussion space, says electrical engineering and computer science graduate student Nouran Soliman.

“With meronymity, we wanted to strike a balance between credibility and social inhibition. How can we make people feel more comfortable contributing and leveraging this rich community while still having some accountability?” says Soliman, lead author of a paper on meronymity .

Soliman wrote the paper with her advisor and senior author David Karger, professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), as well as others at the Semantic Scholar Team at Allen Institute for AI, the University of Washington, and Carnegie Mellon University. The research will be presented at the ACM Conference on Human Factors in Computing Systems.

Breaking down social barriers

The researchers began by conducting an initial study with 20 scholars to better understand the motivations and social barriers they face when engaging online with other academics.

They found that, while academics find X (formerly called Twitter) and Mastodon to be key resources when seeking help with research, they were often reluctant to ask for, discuss, or share recommendations.

Many respondents worried asking for help would make them appear to be unknowledgeable about a certain subject or feared public embarrassment if their posts were ignored.

The researchers developed LiTweeture to enable scholars to selectively present relevant facets of their identity when using social media to ask for research help.

But such identity markers, or “meronyms,” only give someone credibility if they are verified. So the researchers connected LiTweeture to Semantic Scholar, a web service which creates verified academic profiles for scholars detailing their education, affiliations, and publication history.

LiTweeture uses someone’s Semantic Scholar profile to automatically generate a set of meronyms they can choose to include with each social media post they compose. A meronym might be something like, “third-year graduate student at a research institution who has five publications at computer science conferences.”

A user writes a query and chooses the meronyms to appear with this specific post. LiTweeture then posts the query and meryonyms to X and Mastodon.

The user can also identify desired responders — perhaps certain researchers with relevant expertise — who will receive the query through a direct social media message or email. Users can personalize their meronyms for these experts, perhaps mentioning common colleagues or similar research projects.

Sharing social capital

They can also leverage connections by sharing their full identity with individuals who serve as public endorsers, such as an academic advisor or lab mate. Endorsements can encourage experts to respond to the asker’s query.

“The endorsement lets a senior figure donate some of their social capital to people who don’t have as much of it,” Karger says.

In addition, users can recruit close colleagues and peers to be helpers who are willing to repost their query so it reaches a wider audience.

Responders can answer queries using meronyms, which encourages potentially shy academics to offer their expertise, Soliman says.

The researchers tested LiTweeture during a field study with 13 junior academics who were tasked with writing and responding to queries. Participants said meronymous interactions gave them confidence when asking for help and provided high-quality recommendations.

Participants also used meronyms to seek a certain kind of answer. For instance, a user might disclose their publication history to signal that they are not seeking the most basic recommendations. When responding, individuals used identity signals to reflect their level of confidence in a recommendation, for example by disclosing their expertise.

“That implicit signaling was really interesting to see. I was also very excited to see that people wanted to connect with others based on their identity signals. This sense of relation also motivated some responders to make more effort when answering questions,” Soliman says.

Now that they have built a framework around academia, the researchers want to apply meronymity to other online communities and general social media conversations, especially those around issues where there is a lot of conflict, like politics. But to do that, they will need to find an effective, scalable way for people to present verified aspects of their identities.

“I think this is a tool that could be very helpful in many communities. But we have to figure out how to thread the needle on social inhibition. How can we create an environment where everyone feels safe speaking up, but also preserve enough accountability to discourage bad behavior? says Karger.

“Meronymity is not just a concept; it's a novel technique that subtly blends aspects of identity and anonymity, creating a platform where credibility and privacy coexist. It changes digital communications by allowing safe engagement without full exposure, addressing the traditional anonymity-accountability trade-off. Its impact reaches beyond academia, fostering inclusivity and trust in digital interactions,” says Saiph Savage, assistant professor and director of the Civic A.I. Lab in the Khoury College of Computer Science at Northeastern University, and who was not involved with this work.

This research was funded, in part, by Semantic Scholar.

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• How to Cite a Website | MLA, APA & Chicago Examples

How to Cite a Website | MLA, APA & Chicago Examples

Published on March 5, 2021 by Jack Caulfield . Revised on January 17, 2024.

To cite a page from a website, you need a short in-text citation and a corresponding reference stating the author’s name, the date of publication, the title of the page, the website name, and the URL.

This information is presented differently in different citation styles. APA , MLA , and Chicago are the most commonly used styles.

Use the interactive example generator below to explore APA and MLA website citations.

Note that the format is slightly different for citing YouTube and other online video platforms, or for citing an image .

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Table of contents

Citing a website in mla style, citing a website in apa style, citing a website in chicago style, frequently asked questions about citations.

An MLA Works Cited entry for a webpage lists the author’s name , the title of the page (in quotation marks), the name of the site (in italics), the date of publication, and the URL.

The in-text citation usually just lists the author’s name. For a long page, you may specify a (shortened) section heading to locate the specific passage. Don’t use paragraph numbers unless they’re specifically numbered on the page.

The same format is used for blog posts and online articles from newspapers and magazines.

You can also use our free MLA Citation Generator to generate your website citations.

Generate accurate MLA citations with Scribbr

Citing a whole website.

When you cite an entire website rather than a specific page, include the author if one can be identified for the whole site (e.g. for a single-authored blog). Otherwise, just start with the site name.

List the copyright date displayed on the site; if there isn’t one, provide an access date after the URL.

Webpages with no author or date

When no author is listed, cite the organization as author only if it differs from the website name.

If the organization name is also the website name, start the Works Cited entry with the title instead, and use a shortened version of the title in the in-text citation.

When no publication date is listed, leave it out and include an access date at the end instead.

Prevent plagiarism. Run a free check.

An APA reference for a webpage lists the author’s last name and initials, the full date of publication, the title of the page (in italics), the website name (in plain text), and the URL.

The in-text citation lists the author’s last name and the year. If it’s a long page, you may include a locator to identify the quote or paraphrase (e.g. a paragraph number and/or section title).

Note that a general reference to an entire website doesn’t require a citation in APA Style; just include the URL in parentheses after you mention the site.

You can also use our free APA Citation Generator to create your webpage citations. Search for a URL to retrieve the details.

Generate accurate APA citations with Scribbr

Blog posts and online articles.

Blog posts follow a slightly different format: the title of the post is not italicized, and the name of the blog is.

The same format is used for online newspaper and magazine articles—but not for articles from news sites like Reuters and BBC News (see the previous example).

When a page has no author specified, list the name of the organization that created it instead (and omit it later if it’s the same as the website name).

When it doesn’t list a date of publication, use “n.d.” in place of the date. You can also include an access date if the page seems likely to change over time.

In Chicago notes and bibliography style, footnotes are used to cite sources. They refer to a bibliography at the end that lists all your sources in full.

A Chicago bibliography entry for a website lists the author’s name, the page title (in quotation marks), the website name, the publication date, and the URL.

Chicago also has an alternative author-date citation style . Examples of website citations in this style can be found here .

For blog posts and online articles from newspapers, the name of the publication is italicized. For a blog post, you should also add the word “blog” in parentheses, unless it’s already part of the blog’s name.

When a web source doesn’t list an author , you can usually begin your bibliography entry and short note with the name of the organization responsible. Don’t repeat it later if it’s also the name of the website. A full note should begin with the title instead.

When no publication or revision date is shown, include an access date instead in your bibliography entry.

The main elements included in website citations across APA , MLA , and Chicago style are the author, the date of publication, the page title, the website name, and the URL. The information is presented differently in each style.

In APA , MLA , and Chicago style citations for sources that don’t list a specific author (e.g. many websites ), you can usually list the organization responsible for the source as the author.

If the organization is the same as the website or publisher, you shouldn’t repeat it twice in your reference:

• In APA and Chicago, omit the website or publisher name later in the reference.
• In MLA, omit the author element at the start of the reference, and cite the source title instead.

If there’s no appropriate organization to list as author, you will usually have to begin the citation and reference entry with the title of the source instead.

When you want to cite a specific passage in a source without page numbers (e.g. an e-book or website ), all the main citation styles recommend using an alternate locator in your in-text citation . You might use a heading or chapter number, e.g. (Smith, 2016, ch. 1)

In APA Style , you can count the paragraph numbers in a text to identify a location by paragraph number. MLA and Chicago recommend that you only use paragraph numbers if they’re explicitly marked in the text.

For audiovisual sources (e.g. videos ), all styles recommend using a timestamp to show a specific point in the video when relevant.

Check if your university or course guidelines specify which citation style to use. If the choice is left up to you, consider which style is most commonly used in your field.

• APA Style is the most popular citation style, widely used in the social and behavioral sciences.
• MLA style is the second most popular, used mainly in the humanities.
• Chicago notes and bibliography style is also popular in the humanities, especially history.
• Chicago author-date style tends to be used in the sciences.

Other more specialized styles exist for certain fields, such as Bluebook and OSCOLA for law.

The most important thing is to choose one style and use it consistently throughout your text.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2024, January 17). How to Cite a Website | MLA, APA & Chicago Examples. Scribbr. Retrieved April 15, 2024, from https://www.scribbr.com/citing-sources/cite-a-website/

Is this article helpful?

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Other students also liked, how to cite an image | photographs, figures, diagrams, how to cite a lecture | apa, mla & chicago examples, how to cite a youtube video | mla, apa & chicago, scribbr apa citation checker.

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Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

CSCI 5980/8980 

Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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