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

  • ️Fri Jan 01 2021

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

Jędrzej Chrzanowski et al. J Med Internet Res. 2021.

Erratum in

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.

Keywords: COVID-19; Wikipedia; infodemiology; information seeking; infoveillance; interest; internet; media; online health information; pandemic; retrospective; surveillance.

©Jędrzej Chrzanowski, Julia Sołek, Dariusz Jemielniak, Dariusz Jemielniak. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.04.2021.

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Conflict of interest statement

Conflicts of Interest: Author DJ is a non-paid, volunteer member of the Board of Trustees of Wikimedia Foundation, a non-profit publisher of Wikipedia. All the other authors have no conflicts to declare.

Figures

Figure 1
Figure 1

The disruption in annual Wikipedia visit patterns. (A) The pattern of general English Wikipedia access from 2015 to 2020 (Multimedia Appendix 3). Data for other language versions of Wikipedia are provided in Multimedia Appendix 4, Supplementary Figures S1a-S9a. (B) The FBProphet prediction model was created based on data from the 2015-2019 period. The model compared expected behaviors in 2020 (ie, the blue line) to observed access (ie, the orange line). Data for the other language versions of Wikipedia are provided in Multimedia Appendix 4, Supplementary Figures S1b-S9b. (C) A summary of monthly general access to Wikipedia across all Wikipedia language versions from September 2019 to September 2020. Solid lines represent Chi-square goodness-of-fit values and the dashed line represents the cutoff value. (D) The stability of the two reference Wikipedia articles across all languages in the 2015-2019 and 2020 periods (ie, the moving SD divided by mean percent access across a 30-day window). DE: German; EN: English; ES: Spanish; FR: French; IT: Italian; NL: Dutch; PL: Polish; RU: Russian; SV: Swedish; VI: Vietnamese.

Figure 2
Figure 2

Access to COVID-19–related Wikipedia articles in 2020 and the total number of deaths resulting from SARS-CoV-2 infection. (A) The percentage of daily article access (ie, the "COVID-19 pandemic," "Spanish flu," "Leonardo da Vinci," and "Sexual intercourse" articles) to English Wikipedia and total number of global deaths resulting from SARS-CoV-2 infection (ie, per 1 million people). Data for other language versions of Wikipedia are provided in Multimedia Appendix 4, Supplementary Figures S18-S25. (B) Heatmap of Spearman absolute regression coefficients for COVID-19–related articles and the total number of global deaths resulting from SARS-CoV-2 infection across languages and months. (C) Access to COVID-19–related articles (ie, the kNN-determined relevance values across selected Wikipedia language versions) versus the total number of region-specific deaths resulting from SARS-CoV-2 infection. The graph shows correlations between region-specific deaths and COVID-19–related articles. (D) Access to COVID-19–related articles (ie, the kNN-determined relevance values across selected Wikipedia language versions) versus the total number of global deaths resulting from SARS-CoV-2 infection. DE: German; EN: English; ES: Spanish; FR: French; IT: Italian; kNN: k-nearest neighbor; NL: Dutch; PL: Polish; RU: Russian; SV: Swedish; VI: Vietnamese.

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