Science through Wikipedia: A novel representation of open knowledge through co-citation networks

Citations in Wikipedia for understanding research reach

Objective

Wikipedia is the most frequently accessed online health information resource and is well positioned as a valuable tool for public health communication and knowledge translation. The authors aimed to explore their institution's health and medical research reach by analyzing its presence in Wikipedia articles.

Methods

In October 2022, a comprehensive database search was constructed in PubMed to retrieve clinical evidence syntheses published by at least one author affiliated with McMaster University from 2017 to 2022, inclusive. Altmetric Explorer was queried using PubMed Identifiers and article titles to access metadata and Wikipedia citation data. 3,582 health evidence syntheses from at least one McMaster University affiliated author were analyzed.

Results

Six percent (n=219) of health evidence syntheses from the authors' institution were cited 568 times in 524 unique Wikipedia articles across 28 different language editions. 45% of citations appeared in English Wikipedia, suggesting a broad global reach for the institutions' research outputs. When adjusted for open access publications, 8% of McMaster University's health evidence syntheses appear in Wikipedia.

Conclusion

Altmetric Explorer is a valuable tool for exploring the reach of an institution's research outputs. Isolating Altmetric data to focus on Wikipedia citations has value for any institution wishing to gain more insight into the global, community-level reach of its contributions to the latest health and medical evidence.

Wikipedia as a tool for contemporary history of science: A case study on CRISPR

Rapid developments and methodological divides hinder the study of how scientific knowledge accumulates, consolidates and transfers to the public sphere. Our work proposes using Wikipedia, the online encyclopedia, as a historiographical source for contemporary science. We chose the high-profile field of gene editing as our test case, performing a historical analysis of the English-language Wikipedia articles on CRISPR. Using a mixed-method approach, we qualitatively and quantitatively analyzed the CRISPR article's text, sections and references, alongside 50 affiliated articles. These, we found, documented the CRISPR field's maturation from a fundamental scientific discovery to a biotechnological revolution with vast social and cultural implications. We developed automated tools to support such research and demonstrated its applicability to two other scientific fields-coronavirus and circadian clocks. Our method utilizes Wikipedia as a digital and free archive, showing it can document the incremental growth of knowledge and the manner scientific research accumulates and translates into public discourse. Using Wikipedia in this manner compliments and overcomes some issues with contemporary histories and can also augment existing bibliometric research.

Evaluative altmetrics: is there evidence for its application to research evaluation?

Introduction

Altmetrics have been demonstrated as a promising tool for analyzing scientific communication on social media. Nevertheless, its application for research evaluation remains underdeveloped, despite the advancement of research in the study of diverse scientific interactions.

Methods

This paper develops a method for applying altmetrics in the evaluation of researchers, focusing on a case study of the Environment/Ecology ESI field publications by researchers at the University of Granada. We considered Twitter as a mirror of social attention, news outlets as media, and Wikipedia as educational, exploring mentions from these three sources and the associated actors in their respective media, contextualizing them using various metrics.

Results

Our analysis evaluated different dimensions such as the type of audience, local attention, engagement generated around the mention, and the profile of the actor. Our methodology effectively provided dashboards that gave a comprehensive view of the different instances of social attention at the author level.

Discussion

The use of altmetrics for research evaluation presents significant potential, as shown by our case study. While this is a novel method, our results suggest that altmetrics could provide valuable insights into the social attention that researchers garner. This can be an important tool for research evaluation, expanding our understanding beyond traditional metrics.

A diachronic perspective on citation latency in Wikipedia articles on CRISPR/Cas-9: an exploratory case study

This paper analyzes Wikipedia's representation of the Nobel Prize winning CRISPR/Cas9 technology, a method for gene editing. We propose and evaluate different heuristics to match publications from several publication corpora against Wikipedia's central article on CRISPR and against the complete Wikipedia revision history in order to retrieve further Wikipedia articles relevant to the topic and to analyze Wikipedia's referencing patterns. We explore to what extent the selection of referenced literature of Wikipedia's central article on CRISPR adheres to scientific standards and inner-scientific perspectives by assessing its overlap with (1) the Web of Science (WoS) database, (2) a WoS-based field-delineated corpus, (3) highly-cited publications within this corpus, and (4) publications referenced by field-specific reviews. We develop a diachronic perspective on citation latency and compare the delays with which publications are cited in relevant Wikipedia articles to the citation dynamics of these publications over time. Our results confirm that a combination of verbatim searches by title, DOI, and PMID is sufficient and cannot be improved significantly by more elaborate search heuristics. We show that Wikipedia references a substantial amount of publications that are recognized by experts and highly cited, but that Wikipedia also cites less visible literature, and, to a certain degree, even not strictly scientific literature. Delays in occurrence on Wikipedia compared to the publication years show (most pronounced in case of the central CRISPR article) a dependence on the dynamics of both the field and the editor's reaction to it in terms of activity.

Social Media: Flattening Hierarchies for Women and Black, Indigenous, People Of Color (BIPOC) to Enter the Room Where It Happens

Social media platforms are widely used to connect people across multiple settings, including country of origin, profession, race/ethnicity, sexual orientation, gender identity, seniority, and training. Groups that have been marginalized or historically excluded from decision-making encounters may lack formal mentors/sponsors because of a lack of representation of women and Black, Indigenous, People Of Color (BIPOC) in senior leadership positions. This can serve as a barrier to professional advancement at all stages of career development. Identifying and connecting with these potential mentors/sponsors outside of one's institutional space can be challenging. For this reason, leveraging social media to develop these professional relationships through flattened hierarchies can allow for professional networking beyond traditional mechanisms. Here we aim to describe how individuals can connect through social media to advance their careers and scientific and clinical expertise, advocate for communities, and provide high-quality communication to the public.

A scholarly network of AI research with an information science focus: Global North and Global South perspectives

This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research.

Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics

Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize communities based on the research topics in which they are interested as social and geographic proximity also influence interactions. This paper proposes a new method which profiles social media users based on their interest on research topics using altmetric data. Social media users are clustered based on the topics related to the research publications they share in social media. This allows removing linkages which respond to social or personal proximity and identifying disconnected users who may have similar research interests. We test this method for users tweeting publications from the fields of Information Science & Library Science, and Microbiology. We conclude by discussing the potential application of this method and how it can assist information professionals, policy managers and academics to understand and identify the main actors discussing research literature in social media.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

Wikipedia citations: A comprehensive data set of citations with identifiers extracted from English Wikipedia

Wikipedia’s content is based on reliable and published sources. To this date, relatively little is known about what sources Wikipedia relies on, in part because extracting citations and identifying cited sources is challenging. To close this gap, we release Wikipedia Citations, a comprehensive data set of citations extracted from Wikipedia. We extracted29.3 million citations from 6.1 million English Wikipedia articles as of May 2020, and classified as being books, journal articles, or Web content. We were thus able to extract 4.0 million citations to scholarly publications with known identifiers—including DOI, PMC, PMID, and ISBN—and further equip an extra 261 thousand citations with DOIs from Crossref. As a result, we find that 6.7% of Wikipedia articles cite at least one journal article with an associated DOI, and that Wikipedia cites just 2% of all articles with a DOI currently indexed in the Web of Science. We release our code to allow the community to extend upon our work and update the data set in the future.

How the Field of Infectious Diseases Can Leverage Digital Strategy and Social Media Use During a Pandemic

Rapid information dissemination is critical in a world changing rapidly due to global threats. Ubiquitous internet access has created new methods of information dissemination that are rapid, far-reaching, and universally accessible. However, inaccuracies may accompany rapid information dissemination, and rigorous evaluation of primary data through various forms of peer review is crucial. In an era in which high-quality information can save lives, it is critical that infectious diseases specialists are well versed in digital strategy to effectively disseminate information to colleagues and the community and diminish voices spreading misinformation. In this study, we review how social media can be used for rapid dissemination of quality information, benefits and pitfalls of social media use, and general recommendations for developing a digital strategy as an infectious diseases specialist. We will describe how the Infectious Diseases Society of America has leveraged digital strategy and social media and how individuals can amplify these resources to disseminate information, provide clinical knowledge, community guidance, and build their own person brand. We conclude in providing guidance to infectious diseases specialists in aiming to build and preserve public trust, consider their audience and specific goals, and use social media to highlight the value of the field of infectious diseases.

COVID-19 research in Wikipedia

Wikipedia is one of the main sources of free knowledge on the Web. During the first few months of the pandemic, over 5,200 new Wikipedia pages on COVID-19 were created, accumulating over 400 million page views by mid-June 2020. 1 At the same time, an unprecedented amount of scientific articles on COVID-19 and the ongoing pandemic have been published online. Wikipedia’s content is based on reliable sources, such as scientific literature. Given its public function, it is crucial for Wikipedia to rely on representative and reliable scientific results, especially in a time of crisis. We assess the coverage of COVID-19-related research in Wikipedia via citations to a corpus of over 160,000 articles. We find that Wikipedia editors are integrating new research at a fast pace, and have cited close to 2% of the COVID-19 literature under consideration. While doing so, they are able to provide a representative coverage of COVID-19-related research. We show that all the main topics discussed in this literature are proportionally represented from Wikipedia, after accounting for article-level effects. We further use regression analyses to model citations from Wikipedia and show that Wikipedia editors on average rely on literature that is highly cited, widely shared on social media, and peer-reviewed.

Measuring the quality of scientific references in Wikipedia: an analysis of more than 115M citations to over 800 000 scientific articles

Wikipedia is a widely used online reference work which cites hundreds of thousands of scientific articles across its entries. The quality of these citations has not been previously measured, and such measurements have a bearing on the reliability and quality of the scientific portions of this reference work. Using a novel technique, a massive database of qualitatively described citations, and machine learning algorithms, we analyzed 1 923 575 Wikipedia articles which cited a total of 824 298 scientific articles in our database and found that most scientific articles cited by Wikipedia articles are uncited or untested by subsequent studies, and the remainder show a wide variability in contradicting or supporting evidence. Additionally, we analyzed 51 804 643 scientific articles from journals indexed in the Web of Science and found that similarly most were uncited or untested by subsequent studies, while the remainder show a wide variability in contradicting or supporting evidence.