Assessing Public Interest Based on Wikipedia’s Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis

Abstract

Background In the current era of widespread access to the internet, we can monitor public interest in a topic via information-targeted web browsing. We sought to provide direct proof of the global population’s altered use of Wikipedia medical knowledge resulting from the new COVID-19 pandemic and related global restrictions. Objective We aimed to identify temporal search trends and quantify changes in access to Wikipedia Medicine Project articles that were related to the COVID-19 pandemic. Methods We performed a retrospective analysis of medical articles across nine language versions of Wikipedia and country-specific statistics for registered COVID-19 deaths. The observed patterns were compared to a forecast model of Wikipedia use, which was trained on data from 2015 to 2019. The model comprehensively analyzed specific articles and similarities between access count data from before (ie, several years prior) and during the COVID-19 pandemic. Wikipedia articles that were linked to those directly associated with the pandemic were evaluated in terms of degrees of separation and analyzed to identify similarities in access counts. We assessed the correlation between article access counts and the number of diagnosed COVID-19 cases and deaths to identify factors that drove interest in these articles and shifts in public interest during the subsequent phases of the pandemic. Results We observed a significant (P<.001) increase in the number of entries on Wikipedia medical articles during the pandemic period. The increased interest in COVID-19–related articles temporally correlated with the number of global COVID-19 deaths and consistently correlated with the number of region-specific COVID-19 deaths. Articles with low degrees of separation were significantly similar (P<.001) in terms of access patterns that were indicative of information-seeking patterns. Conclusions The analysis of Wikipedia medical article popularity could be a viable method for epidemiologic surveillance, as it provides important information about the reasons behind public attention and factors that sustain public interest in the long term. Moreover, Wikipedia users can potentially be directed to credible and valuable information sources that are linked with the most prominent articles.

Undergraduate Students’ Online Health Information-Seeking Behavior during the COVID-19 Pandemic

“…The number of medical articles published on the internet increased significantly during the COVID-19 pandemic [ 28 ]; however, at the same time, the amount of fake news and disinformation skyrocketed to several dozen times the previous level [ 29 ]. As the internet booms and health information spreads, the World Wide Web has become a major source for the public to search for information about medical and health risks.…”

Representing COVID-19 information in collaborative knowledge graphs: The case of Wikidata

“…Additionally, the bottom-up approach allows for a better optimization of topics, by relying on "free market" spontaneous forces of individual editors. It is already known that the search habits of users seeking medical content changed dramatically as a result of the COVID pandemic [24]. However, the exact dynamics of how this peer network responded to the challenge, in particular to the urgent need for new taxonomies and knowledge graphs, has not been a topic of systematic analysis.…”

“…The support of other natural languages can also be explained by the use of bots that extract multilingual terms representing clinical concepts based on natural language processing techniques and machine learning 24 [63] and by the involvement of research institutions and scientists speaking these languages, particularly German and Dutch, in adding biomedical information to Wikidata [64,65]. The near-100% coverage for properties with COVID-19 as the subject in the most spoken languages (Fig.…”

“…https://www.wikidata.org/wiki/Wikidata:WikiProject_Labels_and_ descriptions24 An example of such a Wikidata bot can be Edoderoobot 2, which is specifically working on labelling, thereby translating structured data into prose in the respective language. Further information about this bot can be found at https://www.wikidata.org/wiki/Wikidata:Requests_for_permissions /Bot/Edoderoobot_2 23.…”

Volunteer contributions to Wikipedia increased during COVID-19 mobility restrictions

“…Amidst the COVID-19 pandemic and the “infodemic” 4 that ensued, Wikipedia played and continues to play an important role in supplying information about the COVID-19 crisis 5 – 8 . Notably, although readers accessed medical articles more frequently than in non-pandemic times 9 , the increase in readership for all kinds of articles—not only those related to the pandemic—suggests that Wikipedia’s role in this time of crisis transcends mere COVID-19-related information seeking 10 . However, page views are but a single aspect of the pandemic’s impact on Wikipedia, which ignores the fundamental contribution of editors who perform unpaid volunteer work to maintain and develop content on the website.…”