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They continue to be popular throughout the pandemic. From March 1st and onward, however, the “region” category Wiki pages get larger aggregate views. This shows how the attention shifted between these two content types, i.e., people continue to learn more information about the virus and, at the same time, track regional progress of transmissions. The “people” category content becomes popular from mid-March, even leading to larger aggregate views during days in April. Wiki pages on celebrity and public figures are generally popular within
Knowledge (XXG). As the virus progresses, more public figures become associated with COVID-19, diverging the public interest. The structured nature of Wikidata even allows us to understand how/why these people are associated with the disease. By using a
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other that might come in the future. We analyzed the complex and diverse attention of the public during the COVID-19 pandemic from the browsing logs of
English Knowledge (XXG) pages. This post will feature findings on English content and patterns from other languages such as Korean, Italian, and Spanish will be revealed in our next post.
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leading the World Health
Organization (WHO) to declare it a pandemic. Countries like Iran, Italy, Spain, and the United States had seen over 50,000 confirmed cases (Fig 1). As of April 14, less than 90 days since the lockdown in China, COVID-19 has infected over 1,880,000 people and has killed more than 117,000 patients worldwide.
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Next, we check how attention is divided across the four topical categories — virus, region, people, and others — by examining the aggregate view counts on these categories. The “virus” category Wiki pages altogether receive the most views during the initial phase until the end of
February (Figure 4).
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What kinds of information did people most seek on COVID-19? How did their attention change over time, as the number of patients quickly rose globally? How quickly were pages tracking regional cases updated? These are critical questions that help us better cope with the current pandemic as well as any
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The speed at which these regional pages are updated is unprecedented, it is as quick as any local CDC reports (which we will look at in future reports). Overall, the increasing attention from the virus to regional and people pages indicate that
Knowledge (XXG) has served multiple purposes during the
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Top 5 content Wiki pages. “2019–20 coronavirus pandemic” was viewed far more than the other pages. “2020 coronavirus pandemic in the United States” are getting more views with the spreading in the US. “Tom Hanks” had a sharp peak on March 12 because of his infection, but it soon lost attention. Most
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English
Knowledge (XXG) Views on Two Pandemic Pages. “Pandemic” as a general term had high attention only on March 11–12 when WHO declared the coronavirus outbreak a global pandemic. “2019–20 coronavirus pandemic” has been viewed steadily since January. The view counts had the steepest increase in a
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Note that this page is not linked to the COVID-19 Wikidata item and hence is considered “not relevant” in our analysis. Nonetheless, many people might arrived at this page either by searching for “pandemic” or by following the hyperlinks that lead to this page. There are other examples (particularly
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Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The first case was observed in China in late 2019, quickly followed by an outbreak in nearby East Asian countries like South Korea. In a few weeks, outbreaks could be seen throughout Europe and
America,
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Stacked Chart and Ratio Chart by
Topical Category. (a) The total number of views on COVID-19-related pages had exponentially increased from late February to early March. In March, the daily views were varying in the range of 6–8 millions. “Virus” category was dominant at the beginning of spreading,
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The open data available on
Knowledge (XXG) allows researchers and the community, in general, to cope with the urgent information needs during crises. The huge demand for local and regional-specific content highlights the importance of having a distributed community of editors who can generate such
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Case
Statistics of COVID-19 in China, South Korea, Spain, and the US (right axis — log scale). These countries have outbreaks at different times. While the patient count increases at a smaller rate for China and South Korea by early March, Spain and the US show a sharp rise. On gray the number of
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English Knowledge (XXG) Views by Topical Category. We divide COVID-19 related Knowledge (XXG) pages into 4 categories; Virus, Region, People, and Others. “Virus” category was dominant at the beginning of spreading, but the “Region” category became dominant on March 1. “Region” category has its
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pandemic, from a reliable source to collect scientific facts about the virus as well as to serve regional breaking news, and to other less critical updates related to the people’s category. Meeting such dynamic attention would not have been possible without the dedicated participation of
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highest peak on March 23. “People” category has a sharp peak on March 12 because of Tom Hanks’s infection. Interestingly, the other categories all have peaked on that day. ‘People’ fluctuated when the news of the confirmed famous spread. Most categories except “People” show decreasing
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The shift of attention from the virus Wiki pages to regional tracker pages suggests that internet users are most interested in first gaining knowledge about the disease, but their attention shifts to more geographically constrained information (that likely have immediate impact on
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Central to any content we observe on Knowledge (XXG) lies Wikidata, a type of structured data that links all Wiki projects. Most of the articles in Knowledge (XXG) link to a Wikidata Item, which among themselves are linked. For example, there is a link between the
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This remark followed large-scale outbreaks in several countries in Europe and the Middle East, especially in Italy, Iran, and France. Since this point, the aggregate attention has stayed at a similar level, reaching over 6,000,000 views a day.
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The “others” category pages that describe the socio-economic impact and other related events also track more extensive attention from mid-March. However, far less attention is paid to these pages compared to the other three categories.
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From the very start of COVID-19, when it was known just as an outbreak of an atypical pneumonia in China, people around the world have been finding and sharing information about the virus on Knowledge (XXG), a frequent
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was created in January, but only became popular in March as local outbreaks began in the US. The regional tracking sites and the accumulated view counts on those pages often mimic the outbreak pattern in that country.
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Celebrities and public figures who are related to COVID-19 either as spokespersons, doctors, or as infected patients were grouped as the people category. This category has the largest number of Wiki pages; 516 in
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Changwook Jung, Sun Geng, Meeyoung Cha are from the Institute for Basic Science, South Korea & KAIST. Inho Hong is from the Center for Humans & Machines, Max Planck Institute for Human Development,
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pages in the People category show a similar view pattern. “Severe acute respiratory syndrome coronavirus 2” had the most views at the beginning of the spreading. For the original version of Figure 3
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At first, the Pandemic Wiki page was not a frequently visited one. On March 11, however, this page showed a sharp increase in the number of page-views when the WHO declared the disease as a pandemic.
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By looking at the Knowledge (XXG) pages that link to those items, we can obtain a list of articles related to COVID-19 in each language. Constraining results to English Knowledge (XXG) results in
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are among the most visited pages from mid-January and throughout the pandemic. The second page describes SARS-Cov-2, the virus that causes COVID-19. On the other hand, the regional tracker page
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from general information about the pandemic and regional responses, to the people who have been involved in the pandemic and misinformation about the virus. You can see some of this data in a
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we can compare the number of times that each of these pages were visited. Sorting content by the number of maximum daily views, we arrive at these five Wiki pages in Figure 3:
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Internet users sought information actively through credible sources like Knowledge (XXG) during the time when not much information was available through official sources.
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It is noteworthy to observe that the total view counts on all content are prominent, reaching over 500,000 views in late January. This is faster than the time when the
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One challenge in tracking people’s attention is the dynamics in data describing the event. Figure 2 shows a live example of the daily page-views of two Wiki pages:
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All other pages that had linked to the COVID-19 Wikidata were categorized as 'others.' These pages often contained information about a specific event (such as
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Diego Saez-Trumper is a researcher employed by the WMF. This paper represents work beyond his regular duties. This article was originally published on
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This post offers an overview of the COVID-19 related data generated in Knowledge (XXG), highlighting the diversity of content that people read:
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Open data and COVID-19: Knowledge (XXG) as an informational resource during the pandemic: What COVID-19 data are available from the WMF?
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Stacked charts or ratio charts are excellent ways to visualize how the aggregate view counts by topical categories evolve over time.
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These Wiki pages covered a plethora of topics, which could be grouped into one of the four categories found by qualitative coding.
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page views on English Knowledge (XXG) COVID-19 related articles (left axis — linear scale). For the original version of Figure 1.
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This discrepancy may appear when titles of Wiki pages change over time. In this particular case, the original title had been
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for medical information. While the content and quality of the information on Knowledge (XXG) is shaped by volunteer editors (
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Many other pages on people involved in some way with the disease show a similar spike in page-views during the pandemic.
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few days before the declaration. From late March, the view counts are decreasing with the slowing down growth rate. *
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Tracking pages dedicated to specific regions were quickly created as outbreaks spread globally (e.g.,
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The ratio chart could identify the day-to-day composition of public attention across diverse topics.
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WHO declared the outbreak a Public Health Emergency of International Concern (PHEIC) on January 30th
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but the “Region” category has been dominant since March. For the original version of Figure 5a.
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editors who contributed to creating and updating these English Knowledge (XXG) articles.
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Wiki page appears to have been created much earlier than the WHO’s announcement in March.
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Open data and COVID-19: Knowledge (XXG) as an informational resource during the pandemic
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You can learn more about the results and methodology used in this analysis by visiting
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how readers are especially interested in what is happening in their local regions.
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In our next post, we will show, by analyzing non-English Knowledge (XXG) pages,
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I,m so sad bad news from USA, aboutCorona Virus I hope to finished soon -
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The most significant peak occurs a few days before March 11th, when the
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language, we can see that most of the people were linked by having
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Wiki pages that directly cover topics on the virus itself (such as
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before WHO’s announcement. For the original version of Figure 2
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Seeking information during COVID-19: English Knowledge (XXG)
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By Changwook Jung, Sun Geng, Meeyoung Cha, Inho Hong, and
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So which individual pages attracted the most attention?
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WHO declared COVID-19 to be characterized as a pandemic
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The text, but not the graphs, on "Medium" are licensed
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online resources offered by the Wikimedia Foundation
776:If your comment has not appeared here, you can try
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470:Severe acute respiratory syndrome coronavirus 2
436:Severe acute respiratory syndrome coronavirus 2
301:Severe acute respiratory syndrome coronavirus 2
199:over 34K contributing to COVID-19 related pages
478:2020 coronavirus pandemic in the United States
432:2020 coronavirus pandemic in the United States
311:2020 coronavirus pandemic in New York (state)
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474:Timeline of the 2019–20 coronavirus pandemic
444:Timeline of the 2019–20 coronavirus pandemic
269:This can be easily done by clicking on the "
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893:Knowledge (XXG) Signpost archives 2020-04
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508:"Medical Condition" or "Cause of Death"
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570:Figure 5(b). To view this figure
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761:These comments are automatically
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772:add the page to your watchlist
609:All this analysis is based on
518:Figure 4. To view this figure
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466:2019–20 coronavirus pandemic
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404:2019–20 coronavirus outbreak
400:2019–20 coronavirus pandemic
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278:878 Knowledge (XXG) pages
293:Coronavirus disease 2019
213:new interactive resource
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765:from this article's
328:2020 Hubei lockdowns
788:well done by all!--
611:public information.
756:Discuss this story
741:WikiProject report
726:On the bright side
691:Arbitration report
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637:"By the numbers" →
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364:Influenza pandemic
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164:Diego Saez-Trumper
45:← Back to Contents
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780:purging the cache
681:Discussion report
442:(blue line), and
420:Using the public
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50:View Latest Issue
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763:transcluded
586:over 34,000
464:Pages like
255:Methodology
144:X (Twitter)
839:. You can
835:It's your
806:Thanks! --
572:click here
564:click here
520:click here
510:COVID-19.
459:click here
408:click here
245:click here
82:Share this
77:Contribute
22:2020-04-26
869:Subscribe
767:talk page
731:Interview
615:this page
597:content.
559:Figure 5.
485:Tom Hanks
454:Figure 3.
440:Tom Hanks
402:page was
395:Figure 2.
240:Figure 1.
201:) and by
182:"Medium".
887:Category
864:Newsroom
859:Archives
837:Signpost
826:A M Muse
736:In focus
627:Previous
489:COVID-19
349:Pandemic
263:COVID-19
175:Germany.
134:Facebook
124:LinkedIn
114:Mastodon
20: |
841:help us
721:Opinion
701:Gallery
324:Others:
317:People:
307:Region:
472:, and
320:total.
289:Virus:
154:Reddit
104:E-mail
854:About
711:Essay
16:<
849:Home
812:talk
794:talk
635:Next
351:and
186:CC0
79:—
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