Readability of English Wikipedia's health information over time

Brezar, Aleksandar; Heilman, James

doi:10.15347/wjm/2019.007

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Perhaps not surprisingly, the fields of research that receive most citations from Wikipedia are "Medicine (32.58%)" and "Biochemistry, Genetics and Molecular Biology (31.5%)" (Arroyo-Machado et al, 2020); Wikipedia's medical pages also contain more citations to scientific literature than the average Wikipedia page (Maggio et al, 2019). Scope for improvement remains, as, for example, the readability of medical content in Wikipedia remains difficult for the nonexpert (Brezar & Heilman, 2019). Given Wikipedia's medical content's high quality and high visibility, our work is concerned with understanding whether the Wikipedia editor community has been able to maintain the same standards for COVID-19-related research.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 research in Wikipedia

Colavizza

2020

Quantitative Science Studies

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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 have been created and have accumulated over 400M pageviews 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 contents are 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 so 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 considera- tion. 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 which is highly cited, widely shared on social media, and has been peer-reviewed.

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Section: Related Workmentioning

confidence: 99%

COVID-19 research in Wikipedia

Colavizza

2020

Quantitative Science Studies

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“…This investigation can potentially provide insight into who Wikipedia is helping and who it has left behind. This knowledge can build upon work already done to situate Wikipedia as a valuable health information resource (Smith, 2020) in the promotion of global health (Heilman et al, 2011), a growing resource of reliable health information (Brezar and Heilman, 2019;Heilman and West, 2015;Shafee et al, 2017a), and attract more contributors who wish to improve its health and medical pages. For LIS scholars, this study will be novel in the fact that a HIB model has not yet been applied to the study of Wikipedia's health content.…”

Section: Future Researchmentioning

confidence: 97%

Wikipedia: an unexplored resource for understanding consumer health information behaviour in library and information science scholarship

Smith

2021

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Purpose To date, health information behaviour (HIB) models have not been applied to an exploration of Wikipedia as a consumer health information resource. Wikipedia has been situated and is well established as a valuable resource for the general layperson wishing to learn more about their health or the health of a loved one. This paper aims to identify an approach to exploring the role of Wikipedia in consumer health information behaviour (CHIB) that is grounded in a conceptual framework from the library and information science (LIS) discipline. Design/methodology/approach The author draws on current HIB models and relevant theories from existing LIS literature and applies them to propose a new definition of CHIB. The author uses this definition to frame Wikipedia as an unexplored consumer health information resource in the LIS scholarship and suggests future directions for placing such investigations within a conceptual framework from LIS. Findings The paper finds that Longo's expanded conceptual model of health information-seeking behaviour (ECMHISB) could be valuable and useful for the exploration of CHIB in relation to Wikipedia's health and medical content. Due to Wikipedia's online nature, research framed by these models must acknowledge and take under consideration the digital divide phenomenon and various factors that influence an individual's place within it. Research limitations/implications This work builds a foundation upon which future research into the role of Wikipedia's health and medical content in CHIB can be grounded. Using Longo's model, future research might provide insight into who Wikipedia is helping and who it has left behind. LIS scholars, practicing health librarians and perhaps health workers stand to gain a deeper understanding of the potential influence of Wikipedia's health information on its consumers. Originality/value For LIS scholars, this paper is novel in the fact that a HIB model has not yet been applied to the study of Wikipedia's health content. This paper provides a foundation for this research.

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“…Perhaps not surprisingly, the fields of research that receive most citations from Wikipedia are “Medicine (32.58%)” and “Biochemistry, Genetics and Molecular Biology (31.5%)” [6]; Wikipedia medical pages also contain more citations to scientific literature than the average Wikipedia page [34]. Margins for improvement remain, as for example the readability of medical content in Wikipedia remains difficult for the non-expert [9]. Given Wikipedia’s medical contents high quality and high visibility, our work is concerned with understanding whether the Wikipedia editor community has been able to maintain the same standards for COVID-19-related research.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 research in Wikipedia

Colavizza

2020

Preprint

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Wikipedia is one of the main sources of free knowledge on the Web. During the first few months of the pandemic, over 4,500 new Wikipedia pages on COVID-19 have been created and have accumulated close to 250M pageviews by early April 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 contents are based on reliable sources, primarily scientific literature. Given its public function, it is crucial for Wikipedia to rely on representative and reliable scientific results, especially so in a time of crisis. We assess the coverage of COVID-19-related research in Wikipedia via citations. We find that Wikipedia editors are integrating new research at an unprecedented fast pace. While doing so, they are able to provide a largely representative coverage of COVID-19related 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, despite the pressure to keep up with novel results, Wikipedia editors rely on literature which is highly cited, widely shared on social media, and has been peer-reviewed.

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Readability of English Wikipedia's health information over time

Cited by 6 publications

References 15 publications

COVID-19 research in Wikipedia

COVID-19 research in Wikipedia

Wikipedia: an unexplored resource for understanding consumer health information behaviour in library and information science scholarship

COVID-19 research in Wikipedia

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