Department of Biostatistics and Translational Medicine, Medical University of Łódź, Łódź, Poland.
Department of Pathology, Medical University of Łódź, Łódź, Poland.
J Med Internet Res. 2021 Apr 12;23(4):e26331. doi: 10.2196/26331.
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.
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.
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.
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.
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.
在当前互联网广泛普及的时代,我们可以通过信息定向浏览来监测公众对某个主题的兴趣。我们旨在提供直接证据,证明由于新的 COVID-19 大流行和相关的全球限制,全球人口对维基百科医学知识的使用发生了改变。
我们旨在确定与 COVID-19 大流行相关的维基百科医学项目文章访问量的时间趋势,并对其进行量化。
我们对 9 种语言版本的维基百科的医学文章和特定国家的 COVID-19 死亡登记进行了回顾性分析。将观察到的模式与基于 2015 年至 2019 年数据训练的维基百科使用预测模型进行比较。该模型全面分析了特定文章以及在 COVID-19 大流行之前(即几年前)和期间访问计数数据之间的相似性。评估了与大流行直接相关的文章的链接,分析了访问计数的相似性。我们评估了文章访问计数与确诊 COVID-19 病例和死亡人数之间的相关性,以确定推动这些文章兴趣的因素以及大流行后续阶段公众兴趣的变化。
我们观察到大流行期间维基百科医学文章的条目数量显著增加(P<.001)。对 COVID-19 相关文章的兴趣增加与全球 COVID-19 死亡人数时间上相关,并且与特定地区 COVID-19 死亡人数一致相关。低分离度的文章在访问模式上非常相似(P<.001),表明存在信息搜索模式。
分析维基百科医学文章的受欢迎程度可能是一种可行的流行病学监测方法,因为它提供了有关公众关注背后原因和长期维持公众兴趣的因素的重要信息。此外,维基百科用户可能会被引导至与最突出文章相关的可信且有价值的信息源。