## Abstract

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 were created, accumulating over 400 million page views 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 content is 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 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 consideration. 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 that is highly cited, widely shared on social media, and peer-reviewed.

## 1. INTRODUCTION

Alongside the primary health crisis, the COVID-19 pandemic has been recognized as an information crisis, or an “infodemic” (Cinelli, Quattrociocchi, et al., 2020; Ioannidis, 2020; Xie, He, et al., 2020). Widespread misinformation (Swire-Thompson & Lazer, 2020) and low levels of health literacy (Paakkari & Okan, 2020) are two of the main issues. In an effort to deal with them, the World Health Organization maintains a list of relevant research updated daily (Zarocostas, 2020), as well as a portal to provide information to the public (World Health Organization, 2020a); the European Commission does similarly (European Commission, 2020), as do many other countries and organizations. The need to convey accurate, reliable, and understandable medical information online has never been so pressing.

Wikipedia plays a fundamental role as a public source of information on the Web, striving to provide “neutral” and unbiased content (Mesgari, Okoli, et al., 2015). Wikipedia is particularly important for access trusted medical information (Smith, 2020; Swire-Thompson & Lazer, 2020). Fortunately, Wikipedia biomedical articles have repeatedly been found to be highly visible and of high quality (Adams, Montgomery, et al., 2020; Maggio, Steinberg, et al., 2020). Wikipedia’s verifiability policy mandates that readers can check the sources of information contained in Wikipedia, and that reliable sources should be secondary and published.2 These guidelines are particularly strict with respect to biomedical content, where the preferred sources are, in order, systematic reviews, reviews, books, and other scientific literature3.

The COVID-19 pandemic has put Wikipedia under stress, with a large amount of new, often nonpeer-reviewed, research being published in parallel with a surge in interest for information related to the pandemic (Wikimedia Foundation, 2020). The response of Wikipedia’s editor community has been rapid: Since March 17, 2020, all COVID-19-related Wikipedia pages have been put under indefinite sanctions, entailing restricted edit access, to allow for better vetting of their contents4. In parallel, a COVID-19 WikiProject has been established and a content creation campaign is ongoing (Jung, Geng, et al., 2020; Wikimedia Foundation, 2020)5. While this effort is commendable, it also raises questions about the capacity of editors to find, select, and integrate scientific information on COVID-19 at such a rapid pace, while keeping quality high. As an illustration of the speed at which events are happening, in Figure 1 we show the average time in number of months from publication to a first citation in Wikipedia for a large set of COVID-19-related articles (see Data and Methods). In 2020, this time has gone to zero: Articles on COVID-19 are frequently cited in Wikipedia immediately after (or even before) their official publication date, based on early access versions of articles.

Figure 1.

Number of months elapsed from publication to the first Wikipedia citation (mean and median binned by year) of COVID-19-related research. In 2020, the average number of months from (official) publication to the first citation in Wikipedia has gone to zero, likely due to the effect of early releases by some journals. As this figure shows censored data, it should only be taken as illustrative of the fact that Wikipedia editors are citing very recent or even unpublished research.

Figure 1.

Number of months elapsed from publication to the first Wikipedia citation (mean and median binned by year) of COVID-19-related research. In 2020, the average number of months from (official) publication to the first citation in Wikipedia has gone to zero, likely due to the effect of early releases by some journals. As this figure shows censored data, it should only be taken as illustrative of the fact that Wikipedia editors are citing very recent or even unpublished research.

In this work, we pose the following general question: Is Wikipedia relying on a representative and reliable sample of COVID-19-related research? We break this question down into the following two research questions:

• 1.

RQ1: Is the literature cited in Wikipedia representative of the broader topics discussed in COVID-19-related research?

• 2.

RQ2: Is Wikipedia citing COVID-19-related research during the pandemic following the same inclusion criteria adopted before and in general?

We approach the first question by clustering COVID-19-related publications using text and citation data and comparing Wikipedia’s coverage of different clusters before and during the pandemic. The second question is instead approached using regression analysis. In particular, we model whether an article is cited in Wikipedia or not, and how many citations it receives from Wikipedia. We then again compare results for articles cited before and during the pandemic.

Our main finding is that Wikipedia contents rely on representative and high-impact COVID-19-related research. (RQ1) During the past few months, Wikipedia editors have successfully integrated COVID-19 and coronavirus research, keeping apace with the rapid growth of related literature by including a representative sample of each of the topics it contains. (RQ2) The inclusion criteria used by Wikipedia editors to integrate COVID-19-related research during the pandemic are consistent with those from before, and appear reasonable in terms of source reliability. Specifically, editors prefer articles from specialized journals or mega journals over preprints, and focus on highly cited and/or highly socially visible literature. Altmetrics such as Twitter shares, mentions in news and blogs, and the number of Mendeley readers complement citation counts from the scientific literature as an indicator of impact positively correlated with citations from Wikipedia. After controlling for these article-level impact indicators and for publication venue, time, and size effects, there is no indication that the topic of research matters with respect to receiving citations from Wikipedia. This indicates that Wikipedia is currently neither over- nor underrelying on any specific COVID-19-related scientific topic.

## 2. RELATED WORK

Wikipedia articles are created, improved, and maintained by the efforts of the community of volunteer editors (Chen & Roth, 2012; Priedhorsky, Chen, et al., 2007), and they are used in a variety of ways by a wide user base (Lemmerich, Sáez-Trumper, et al., 2019; Piccardi, Redi, et al., 2020; Singer, Lemmerich, et al., 2017). The information Wikipedia contains is generally considered to be of high quality and up to date (Adams et al., 2020; Geiger & Halfaker, 2013; Keegan, Gergle, & Contractor, 2011; Kumar, West, & Leskovec, 2016; Piscopo & Simperl, 2019; Priedhorsky et al., 2007; Smith, 2020), notwithstanding room for improvement and the need for constant knowledge maintenance (Chen & Roth, 2012; Forte, Andalibi, et al., 2018; Lewoniewski, We˛cel, & Abramovich, 2017).

Following Wikipedia’s editorial guidelines, the community of editors creates content often relying on scientific and scholarly literature (Arroyo-Machado, Torres-Salinas, et al., 2020; Halfaker, Mansurov, et al., 2018; Nielsen, Mietchen, & Willighagen, 2017), and therefore Wikipedia can be considered a mainstream gateway to scientific information (Heilman, Kemmann, et al., 2011; Laurent & Vickers, 2009; Lewoniewski et al., 2017; Maggio, Willinsky, et al., 2019; Piccardi et al., 2020; Shafee, Masukume, et al., 2017). Unfortunately, few studies have considered the representativeness and reliability of Wikipedia’s scientific sources. The evidence on what scientific and scholarly literature is cited in Wikipedia is slim. Early studies point to a relative low overall coverage, indicating that between 1% and 5% of all published journal articles are cited in Wikipedia (Priem, Piwowar, & Hemminger, 2012; Shuai, Jiang, et al., 2013; Zahedi, Costas, & Wouters, 2014). Previous studies have shown that the subset of scientific literature cited from Wikipedia is more likely on average to be published in popular, high-impact-factor journals, and to be available via open access (Arroyo-Machado et al., 2020; Nielsen, 2007; Teplitskiy, Lu, & Duede, 2017).

Wikipedia is particularly relevant as a means to access medical information online (Heilman et al., 2011; Laurent & Vickers, 2009; Smith, 2020; Swire-Thompson & Lazer, 2020). Wikipedia’s medical content is of very high quality on average (Adams et al., 2020) and is primarily written by a core group of medical professionals who are part of the nonprofit Wikipedia Medicine (Shafee et al., 2017). Articles that are part of WikiProject Medicine “are longer, possess a greater density of external links, and are visited more often than other articles on Wikipedia” (Maggio et al., 2020). 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.

## 3. DATA AND METHODS

### 3.1. COVID-19-Related Research

COVID-19-related research is not trivial to delimit (Colavizza, Costas, et al., 2020). Our approach is to consider two public and regularly updated lists of publications:

• •

The Dimensions COVID-19 Publications list (Dimensions, 2020).

• •

The COVID-19 Open Research Dataset (CORD-19): a collection of COVID-19 and coronavirus-related research, including publications from PubMed Central, Medline, arXiv, bioRxiv, and medRxiv (Wang, Lo, et al., 2020). CORD-19 also includes publications from the World Health Organization COVID-19 Database (World Health Organization, 2020b).

Publications from these three lists are merged, and duplicates removed using publication identifiers, including DOI, PMID, PMCID, and Dimensions ID. Publications without at least one identifier among these are discarded. As of July 1, 2020, the resulting list of publications contains 160,656 entries with a valid identifier, of which 72,795 were released in 2020, as can be seen from Figure 2. Research on coronaviruses, and therefore the accumulation of this corpus over time, has been clearly influenced by the SARS (2003+), MERS (2012+), and COVID-19 outbreaks. We use this list of publications to represent COVID-19 and coronavirus research in what follows. More details are given in the online repositories.

Figure 2.

COVID-19-related literature over time, binned by publication year. (a) Overall; (b) Since 2000 included.

Figure 2.

COVID-19-related literature over time, binned by publication year. (a) Overall; (b) Since 2000 included.

### 3.2. Auxiliary Data Sources

In order to study Wikipedia’s coverage of this list of COVID-19-related publications, we use data from Altmetric (Ortega, 2018; Robinson-García, Torres-Salinas, et al., 2014). Altmetric provides Wikipedia citation data relying on known identifiers6. Despite this limitation, Altmetric data have been previously used to map Wikipedia’s use of scientific articles (Arroyo-Machado et al., 2020; Torres-Salinas, Romero-Frías, & Arroyo-Machado, 2019; Zahedi et al., 2014), especially because citations from Wikipedia are considered a possible measure of impact (Kousha & Thelwall, 2017; Sugimoto, Work, et al., 2017). Publications from the full list above are queried using the Altmetric API by DOI or PMID. In this way, 101,662 publications could be retrieved. After merging for duplicates by summing Altmetric indicators, we have a final set of 94,600 distinct COVID-19-related publications with an Altmetric entry.

Furthermore, we use data from Dimensions (Herzog, Hook, & Konkiel, 2020; Martín-Martín, Thelwall, & López-Cózar, 2020) in order to get citation counts for COVID-19-related publications. The Dimensions API is also queried by DOI and PMID, resulting in 141,783 matches. All auxiliary data sources were queried on July 1, 2020 too.

### 3.3. Methods

We approach our two research questions with the following methods:

• 1.

RQ1: To assess whether the literature cited in Wikipedia is representative of the broader topics discussed in COVID-19-related research, we first cluster COVID-19 literature using text and citation data. Clusters of related literature allow us to identify broad distributions over topics within our COVID-19 corpus. We then assess to what extent the literature cited from Wikipedia follows the same distribution over topics of the entire corpus.

• 2.

RQ2: To ascertain the inclusion criteria of Wikipedia editors, we use linear regression to model whether an article is cited from Wikipedia or not (logistic regression) and the number of Wikipedia citations it receives (linear regression).

In this section, we detail the experimental choices made for clustering analysis using publication text and citation data. Details of the regression analyses are given in the corresponding section.

Text-based clustering of publications was performed in two ways: topic modeling and k-means relying on SPECTER embeddings. Both methods made use of the titles and abstracts of available publications by concatenating them into a single string. We detected 152,247 articles in English out of 160,656 total articles (8,409 less than the total). Of these, 33,301 have no abstract; thus we only used their title, as the results did not change significantly when excluding articles without an abstract. Before performing topic modeling, we applied a preprocessing pipeline using scispaCy’s en_core_sci_md model (Neumann, King, et al., 2019) to convert each document into a bag of words representation, which includes the following steps: entity detection and inclusion in the bag-of-words for entities strictly longer than one token; lemmatization; removal of isolated punctuation, stop words, and tokens consisting of a single character; and inclusion of frequent bigrams. SPECTER embeddings were instead retrieved from the API without any preprocessing7.

Topic modeling is a family of methods to learn statistical patterns of keywords frequently occurring together in the same documents. Formally, a topic is defined as a probability distribution over a vocabulary. Multiple topics can be learned from a corpus of documents and then used to cluster it (Blei, 2012). While topic models are useful given that they require no annotated data, they also provide a way to look at a certain corpus of documents. As such, they have been previously used for bibliometric analysis (Leydesdorff & Nerghes, 2017; Yau, Porter, et al., 2014). We trained and compared topic models using Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003), Correlated Topic Models (CTM; Blei & Lafferty, 2007), Hierarchical Dirichlet Process (HDP; Teh, Jordan, et al., 2006) and a range of topics between five and 50. We found similar results in terms of topic contents and their Wikipedia coverage (see Results) across models and over multiple runs, and a reasonable value of the number of topics to be between 15 and 25 from a topic coherence analysis (Mimno, Wallach, et al., 2011). Therefore, in what follows we discuss an LDA model with 15 topics8. The top words for each topic of this model are given in the  Appendix, while topic intensities over time are plotted as a heat map in Figure A2. SPECTER is a novel method to generate document-level embeddings of scientific documents based on a transformer language model and the network of citations (Cohan, Feldman, et al., 2020). SPECTER does not require citation information at inference time, and performs well without any further training on a variety of tasks. We embed every paper and cluster them using k-means with k = 20. The number of clusters was established using the elbow and silhouette methods; different values of k could well be chosen, so we again decided to pick the smallest reasonable value of k.

We then turned our attention to citation network clustering. We constructed a bibliographic coupling citation network (Kessler, 1963) based on all publications with references provided by Dimensions; these amount to 118,214. Edges were weighted using fractional counting (Perianes-Rodriguez, Waltman, & van Eck, 2016), hence dividing the number of references in common between any two publications by the length of the union of their reference lists (thus, the maximum possible weight is 1.0). We used only the giant weakly connected component, which amounts to 114,829 nodes (3,385 less than the total) and 70,091,752 edges with a median weight of 0.0217. We clustered the citation network using the Leiden algorithm (Traag, Waltman, & van Eck, 2019) with a resolution parameter of 0.05 and the Constant Potts Model (CPM) quality function (Traag, Van Dooren, & Nesterov, 2011). With this configuration, we found that the largest 43 clusters account for half the nodes in the network, and the largest cluster is composed of 15,749 nodes.

These three methods differ in which data they use and how, and thus provide for complementary results. While topic models focus on word co-occurrences and are easier to interpret, bibliographic coupling networks rely on the explicit citation links among publications. Finally, SPECTER combines both kinds of data and modern deep learning techniques.

## 4. RESULTS

Intense editorial work was carried out over the early weeks of 2020 to include scientific information on COVID-19 and coronaviruses into Wikipedia (Jung, Geng, et al., 2020). From Figure 3(a), we can appreciate the surge in new citations added in Wikipedia to COVID-19 research. Importantly, these citations were not only added not only to cope with the growing amount of new literature but also to fill gaps by including literature published before 2020, as shown in Figure 3(b). The total fraction of COVID-19-related articles that are cited at least once in Wikipedia compared with the total is 1.9%. Yet, this number is uneven over languages and over time. Articles in English have a 2.0% chance of being cited in Wikipedia, while articles in other languages have only a 0.24% chance. To be sure, the whole corpus is English dominated, as we discussed above. This might be an artifact of the coverage of the data sources, as well as the way the corpus was assembled. The coverage of articles over time is instead given in Figure 4, starting from 2003 when the first surge of publications happens due to SARS. We can appreciate that the coverage seems to be uneven, and less pronounced for the past few years (2017–2020), yet this needs to be considered in view of the high growth of publications in 2020. Hence, while 2020 is a relatively low-coverage year (1.2%), it is already the year with the most publications cited in Wikipedia in absolute number (Figure 3b).

Figure 3.

Timing of new citations from Wikipedia, and publication years of the articles they refer to. See Figure A1 for the full timeline. (a) Number of citations in Wikipedia to COVID-19 literature, per month from January 2018. (b) Publication year of COVID-19 articles cited from Wikipedia, distinguishing between citations added before 2020 and in 2020.

Figure 3.

Timing of new citations from Wikipedia, and publication years of the articles they refer to. See Figure A1 for the full timeline. (a) Number of citations in Wikipedia to COVID-19 literature, per month from January 2018. (b) Publication year of COVID-19 articles cited from Wikipedia, distinguishing between citations added before 2020 and in 2020.

Figure 4.

Fraction of COVID-19-related articles cited from Wikipedia per year, from 2003.

Figure 4.

Fraction of COVID-19-related articles cited from Wikipedia per year, from 2003.

Citation distributions are skewed in Wikipedia, as they are in science more generally. Some articles receive a high number of citations in Wikipedia and some Wikipedia articles make a high number of citations to COVID-19-related literature. Table A1 lists the top 20 Wikipedia articles by number of citations of COVID-19-related research. These articles, largely in English, primarily focus on the recent pandemic and coronaviruses/viruses from a virology perspective, as already highlighted in a study by the Wikimedia Foundation (Jung et al., 2020). Table A2 reports instead the top 20 journal articles cited from Wikipedia. These also follow a similar pattern: Articles published before 2020 focus on virology and are made of a high proportion of review articles. Articles published in 2020, instead, have a focus on the ongoing pandemic, its origins, and its epidemiological and public health aspects. As we see next, this strongly aligns with the general trends of COVID-19-related research over time.

In order to discuss research trends in our CORD-19-related corpus at a higher level of granularity, we grouped the 15 topics from the LDA topic model into five general topics and labeled them as follows:

• •

Coronaviruses: topics 5, 8; this general topic includes research explicitly on coronaviruses (COVID-19, SARS, MERS) from a variety of perspectives (virology, epidemiology, intensive care, historical unfolding of outbreaks).

• •

Epidemics: topics 9, 11, 12; research on epidemiology, including modeling the transmission and spread of pathogens.

• •

Public health: topics 0, 1, 10; research on global health issues, healthcare.

• •

Molecular biology and immunology: topics 2, 4, 6; research on the genetics and biology of viruses, vaccines, drugs, therapies.

• •

Clinical medicine: topics 3, 7, 13, 14; research on intensive care, hospitalization, and clinical trials.

The grouping is informed by agglomerative clustering based on the Jensen-Shannon distance between topic-word distributions (Figure A5). To be sure, the labeling is a simplification of the actual publication contents. It is also worth considering that topics overlap substantially. The COVID-19 research corpus is dominated by literature on coronaviruses, public health, and epidemics, largely due to 2020 publications. COVID-19-related research did not accumulate uniformly over time. We plot the relative (yearly mean, Figure A3a) and absolute (yearly sum, Figure A3b) general topic intensity. From these plots, we confirm the periodization of COVID-19-related research as connected to known outbreaks. Outbreaks generate a shift in the attention of the research community, which is apparent when we consider the relative general topic intensity over time in Figure A3(a). The 2003 SARS outbreak generated a shift associated with a rise in publications on coronaviruses and in the management of epidemic outbreaks (public health, epidemiology). A similar shift is again happening, at a much larger scale, during the current COVID-19 pandemic. When we consider the absolute general topic intensity, which can be interpreted as the number of articles on a given topic (Figure A3b), we can appreciate how scientists are mostly focusing on topics related to public health, epidemics, and coronaviruses (COVID-19) during these first months of the current pandemic.

### 4.1. RQ1: Wikipedia Coverage of COVID-19-Related Research

We address here our first research question: Is the literature cited in Wikipedia representative of the broader topics discussed in COVID-19-related research? We start by comparing the general topic coverage of articles cited from Wikipedia with those which are not. In Figure 5, three plots are provided: the general topic intensity of articles published before 2020 (Figure 5a), in 2020 (Figure 5b) and overall (Figure 5c). The general topic intensity is averaged and 95% confidence intervals are provided. From Figure 5(c) we can see that Wikipedia seems to cover COVID-19-related research well. The general topics on immunology, molecular biology, and epidemics seem slightly over represented, where clinical medicine and public health are slightly under represented. A comparison between publications from 2020 and from before highlights further trends. In particular, in 2020, Wikipedia editors have focused more on recent literature on coronaviruses, thus directly related to COVID-19 and the current pandemic, and proportionally less on literature on public health, which is also dominating 2020 publications. The traditional slight over representation of immunology and molecular biology literature persists. Detailed Kruskal–Wallis H test statistics for significant differences (Kruskal & Wallis, 1952) and Cohen’s d for their effect sizes (Cohen, 1988) are provided in the  Appendix (Figure A6 and Tables A3A5). While the distributions are significantly different for most general topics and periodizations, the effect sizes are often small. The coverage of COVID-19-related literature from Wikipedia appears therefore to be reasonably balanced from this first analysis, and to remain so in 2020. The topical differences we found, especially around coronaviruses and the current COVID-19 outbreak, might in part be explained by the criterion of notability, which led to the creation or expansion of Wikipedia articles on the ongoing pandemic9.

Figure 5.

Average general topic intensity of COVID-19-related publications cited in Wikipeda (green) or not (blue). 95% bootstrapped confidence intervals are given. See Figure A6 and Tables A3A5 for significance tests and effect sizes. (a) Published before 2020. Note: this plot also considers as cited in Wikipedia those publications published before 2020 and cited for the first time in 2020. (b) Published in 2020. (c) All publications.

Figure 5.

Average general topic intensity of COVID-19-related publications cited in Wikipeda (green) or not (blue). 95% bootstrapped confidence intervals are given. See Figure A6 and Tables A3A5 for significance tests and effect sizes. (a) Published before 2020. Note: this plot also considers as cited in Wikipedia those publications published before 2020 and cited for the first time in 2020. (b) Published in 2020. (c) All publications.

A complementary way to address the same research question is to investigate Wikipedia’s coverage of publication clusters. We consider here both SPECTER k-means clusters and bibliographic network clusters. While we use all 20 SPECTER clusters, we limit ourselves to the top n network clusters that are necessary in order to cover at least 50% of the nodes in the network. In this way, we consider 41 clusters for the citation network, all of size above 300. In Figure 6 we plot the percentage of articles cited in Wikipedia per cluster, and the clusters’ size in number of publications they contain. There is no apparent size effect in either of the two clustering solutions.

Figure 6.

Proportion of articles cited from Wikipedia (y-axis) per cluster size (number of articles in the cluster, x-axis). (a) SPECTER k-means (all). (b) Bibliographic coupling (top 41).

Figure 6.

Proportion of articles cited from Wikipedia (y-axis) per cluster size (number of articles in the cluster, x-axis). (a) SPECTER k-means (all). (b) Bibliographic coupling (top 41).

When we characterize clusters using general topic intensities, some clear patterns emerge. Starting with SPECTER k-means clusters, the most cited clusters are numbers 6 and 8 (main macrotopics: molecular biology) and 5 (main macrotopics: coronaviruses and public health, especially focusing on COVID-19 characteristics, detection, and treatment). The least cited clusters include number 18 (containing preprints) and 13 (focused on the social sciences, and especially economics, such as from SSRN journals). Considering citation network clusters, the largest but not most cited are numbers 0 (containing 2020 research on COVID-19) and 1 (with publications on molecular biology and immunology). The other clusters are smaller and hence more specialized. The reader can explore all clusters using the accompanying repository.

We have seen so far that Wikipedia relies on a reasonably representative sample of COVID-19-related literature when assessed using topic models. During 2020, the main effort of editors has focused on catching up with abundant new research (and some backlog) on the ongoing pandemic and, to a lower extent, on public health and epidemiology literature. When assessing coverage using different clustering methods, we do not find a size effect by which larger clusters are proportionally more cited from Wikipedia. Yet we also find that, in particular with citation network clusters, smaller clusters can be either highly or lowly cited from Wikipedia on average. Lastly, we find an underrepresentation of preprint and social science research. Despite this overall encouraging result, differences in coverage persist. In the next section, we further assess whether these differences can be explained away by considering article-level measures of impact.

### 4.2. RQ2: Predictors of Citations from Wikipedia

In this section, we address our second research question: Is Wikipedia citing COVID-19-related research during the pandemic following the same criteria adopted before and in general? We use regression analysis in two forms: a logistic regression to model if a paper is cited in Wikipedia or not, and a linear regression to model the number of citations a paper receives in Wikipedia. While the former model captures the suitability of an article to provide encyclopedic evidence, the latter captures its relevance to multiple Wikipedia articles.

#### 4.2.1. Dependent variables

Wikipedia citation counts for each article are taken from Altmetric. If this count is 1 or more, an article is considered as cited in Wikipedia. We consider citation counts from Altmetric at the time of the data collection for this study. We focus on the articles with a match from Dimensions, and consider an article to have zero citations in Wikipedia if it is not found in the Altmetric database.

#### 4.2.2. Independent variables

We focus our study on three groups of independent variables at the article level capturing impact, topic, and timing respectively. Previous studies have shown how literature cited in Wikipedia tends to be published in prestigious journals and available via open access (Arroyo-Machado et al., 2020; Nielsen, 2007; Teplitskiy et al., 2017). We are interested in assessing some of these known patterns for COVID-19-related research, complementing them by considering citation counts and the topics discussed in the literature, and eventually understanding whether there has been any change in 2020.

Article-level variables include citation counts from Dimensions and a variety of altmetric indicators (Robinson-García et al., 2014), which have been found to correlate with later citation impact of COVID-19 research (Kousha & Thelwall, 2020). Altmetrics include the number of Mendeley readers, Twitter interactions (unique users), Facebook shares, mentions in news and blog posts (summed due to their high correlation), mentions in policy documents, and the expert ratio in user engagement10. We also include the top 20 publication venues by number of articles in the corpus using dummy coding, taking as reference level a generic category “other,” which includes articles from all other venues. It is worth clarifying that article-level variables were also calculated at the time of the data collection for this study. This might seem counterintuitive, especially for the classification task, as one might prefer to calculate variables at the time when an article was first cited in Wikipedia. We argue that this is not the case, because Wikipedia can always be edited and citations removed as easily as added. As a consequence, a citation in Wikipedia (or its absence) is a continued rather than a discrete action, justifying calculating all counts at the same time for all articles in the corpus.

Topic-level variables capture the topics discussed in the articles, as well as their relative importance in terms of size (size effects). They include the macrotopic intensities for each article, the size of the SPECTER cluster an article belongs to, and the size of its bibliographic coupling network cluster (for the 41 largest clusters with more than 300 articles each, setting it to zero for articles belonging to other clusters. In this way, the variable accounts for both size and thresholding effects). Cluster identities for both SPECTER and citation network clusters were also tested, but did not contribute significantly to the models. Several other measures were considered, such as the semantic centrality of an article to its cluster centroid (SPECTER k-means) and network centralities, but because these all strongly correlate to size indicators, they were discarded to avoid multicollinearity.

Lastly, we include the year of publication using dummy coding and 2020 as the reference level. Several other variables were tested. The proposed selection removes highly correlated variables while preserving the information required by the research question. The Pearson’s correlations for the selected transformed variables are shown in Figure A4. More details, along with a full profiling of variables, are provided in the accompanying repository.

#### 4.2.3. Model

We consider two models: a logistic model on being cited in Wikipedia (1) or not (0) and an ordinary least squares (OLS) model on citation counts from Wikipedia. Both models use the same set of independent variables and transformations described in Table 1.

Table 1.
Regression variables, their description, typology, and transformations. ln(x + 1) means one is added to the value and then the natural logarithm is taken
VariableDescriptionTypeTransformations
in_wikipedia Whether an article is cited from Wikipedia (1) or not (0) Indicator
n_cit_w Number of citations from Wikipedia Numeric ln(x + 1)
publication_year Publication year of the article Categorical
times_cited Number of citations (Dimensions) Numeric ln(x + 1)
counts_mendeley Number of Mendeley readers (Altmetric) Numeric ln(x + 1)
counts_policy Number of mentions in policy documents (Altmetric) Numeric ln(x + 1)
counts_twitter_unique Number of engagements with unique Twitter users (Altmetric) Numeric ln(x + 1)
counts_blogs_news Number of mentions in news and blogs (Altmetric) Numeric ln(x + 1)
expert_ratio Ratio of engagements with experts (Altmetric) Numeric (0 to 1)
top_journal Journal Categorical
tm_coronaviruses Topic intensity: Coronaviruses Numeric (0 to 1)
tm_epidemics Topic intensity: Epidemics Numeric (0 to 1)
tm_ph Topic intensity: Public health Numeric (0 to 1)
tm_mbi Topic intensity: Molecular biology and immunology Numeric (0 to 1)
tm_clinical_medicine Topic intensity: Clinical medicine Numeric (0 to 1)
spectre_cluster_size Size of SPECTER cluster the article belongs to Numeric ln(x + 1)
network_cluster_size Size of bib. coupling cluster the article belongs to Numeric ln(x + 1)
VariableDescriptionTypeTransformations
in_wikipedia Whether an article is cited from Wikipedia (1) or not (0) Indicator
n_cit_w Number of citations from Wikipedia Numeric ln(x + 1)
publication_year Publication year of the article Categorical
times_cited Number of citations (Dimensions) Numeric ln(x + 1)
counts_mendeley Number of Mendeley readers (Altmetric) Numeric ln(x + 1)
counts_policy Number of mentions in policy documents (Altmetric) Numeric ln(x + 1)
counts_twitter_unique Number of engagements with unique Twitter users (Altmetric) Numeric ln(x + 1)
counts_blogs_news Number of mentions in news and blogs (Altmetric) Numeric ln(x + 1)
expert_ratio Ratio of engagements with experts (Altmetric) Numeric (0 to 1)
top_journal Journal Categorical
tm_coronaviruses Topic intensity: Coronaviruses Numeric (0 to 1)
tm_epidemics Topic intensity: Epidemics Numeric (0 to 1)
tm_ph Topic intensity: Public health Numeric (0 to 1)
tm_mbi Topic intensity: Molecular biology and immunology Numeric (0 to 1)
tm_clinical_medicine Topic intensity: Clinical medicine Numeric (0 to 1)
spectre_cluster_size Size of SPECTER cluster the article belongs to Numeric ln(x + 1)
network_cluster_size Size of bib. coupling cluster the article belongs to Numeric ln(x + 1)

All count variables are transformed by adding one and taking the natural logarithm, while the remaining variables are either indicators or range between 0 and 1 (such as general topic intensities, beginning with a tm_ prefix; for example, tm_ph is “public health”). OLS models including log transform and the addition of 1 for count variables such as citation counts, have been found to perform well in practice when compared to more involved alternatives (Thelwall, 2016; Thelwall & Wilson, 2014). Furthermore, all missing values were set to zero, except for the publication year, venue (journal), and general topic intensities as removing rows with missing values yielded comparable results.

#### 4.2.4. Discussion

We discuss results for three models: two logistic regression models on articles published and first cited up to and including in 2020, and one on articles published and first cited up to and including 2019. The 2019 model only considers articles published in 2019 or earlier and cited for the first time from Wikipedia in 2019 or earlier, or articles never cited from Wikipedia, discarding articles published in 2020 or cited from Wikipedia in 2020 irrespective of their publication time. We also discuss an OLS model predicting (the log of ) citation counts including all data up to and including 2020. We do not discuss a 2019 OLS model because it would require Wikipedia citation counts calculated at the end of 2019, which were not available to us. Regression tables for these three models are provided in the  Appendix, while Figure A7 shows the distribution of some variables distinguishing between articles cited in Wikipedia or not. Logistic regression tables provide marginal effects, while the OLS table provides the usual coefficients. The actual number of data points used to fit each model, after removing those that contained any null value, is given in the regression tables.

Considering the logistic models first, we can show some significant effects11. First of all, the year of publication is mostly negatively correlated with being cited from Wikipedia, compared with the reference category 2020. This seems largely due to publication size effects, as the fraction of 2020 articles cited from Wikipedia is quite low (see Figure 4). The 2019 model indeed shows positive correlations for all years when compared to the reference category 2019, and indeed 2019 is the year with lowest coverage since 2000. Secondly, some of the most popular venues are positively correlated with citations in Wikipedia, when compared to an “other” category (which includes all venues except the top 20). In the 2020 model, these venues include mega journals (Nature, Science) and specialized journals (The Lancet, BMJ). Negative correlations occur for preprint servers (medRxiv and bioRxiv in particular).

When we consider indicators of impact, we see a significant positive effect for citation counts, Mendeley readers, Twitter, and news and blog mentions; we see instead no effect for policy document mentions and Facebook engagements. This is consistent in the 2019 model, except for Facebook having a positive effect and Twitter a lack of correlation. This result, on the one hand, highlights the importance of academic indicators of impact such as citations, and on the other hand suggests the possible complementarity of altmetrics in this respect. As certain altmetrics can accumulate more rapidly than citations (Fang & Costas, 2020), they could complement them effectively when needed (Kousha & Thelwall, 2020). Furthermore, the expert ratio in altmetrics engagement is negatively correlated with being cited from Wikipedia in 2020. This might be due to the high altmetrics engagement with COVID-19 research in 2020, but it could also hint at the possibility that social media impact need not be driven by experts in order to be correlated with scientific impact. We can further see how cluster size effects are not or very marginally correlated with being cited in Wikipedia.

Lastly, general topic intensities are never correlated with being cited in Wikipedia in either model, underlining that Wikipedia appears to be proportionally representing all COVID-19-related research and that residual topical differences in coverage are due to article-level effects.

The 2020 OLS model largely confirms these results, except that mentions in policy documents and Facebook engagements become positively correlated with the number of citations from Wikipedia. It is important to underline that, for all these results, there is no attempt to establish causality. For example, the positive correlation between the number of Wikipedia articles citing a scientific article and the number of policy documents mentioning it might be due to policy document editors using Wikipedia, Wikipedia editors using policy documents, both, or neither. The fact is, more simply, that some articles are picked up by both.

## 5. CONCLUSION

The results of this study provide some reassuring evidence. It appears that Wikipedia’s editors are well able to keep track of COVID-19-related research. Of 141,783 articles in our corpus, 3,083 (∼2%) are cited in Wikipedia: a share comparable to what was found in previous studies. Wikipedia editors are relying on scientific results representative of the several topics included in a large corpus of COVID-19-related research. They have been effectively able to cope with new, rapidly growing literature. The minor discrepancies in coverage that persist, with slightly more Wikipedia-cited articles on topics such as molecular biology and immunology and slightly fewer on clinical medicine and public health, are fully explained away by article-level effects. Wikipedia editors rely on impactful and visible research, as evidenced by largely positive citation and altmetrics correlations. Importantly, Wikipedia editors also appear to be following the same inclusion standards in 2020 as before: In general, they rely on specialized and highly cited results from reputable journals, avoiding, for example, preprints.

The main limitation of this study is that it is purely observational, and thus does not explain why some articles are cited in Wikipedia or not. While in order to assess the coverage of COVID-19-related research from Wikipedia this is of secondary importance, it remains relevant when attempting to predict and explain it. A second limitation is that this study is based on citations from Wikipedia to scientific publications, and no Wikipedia content analysis is performed. Citations of scientific literature, while informative, do not completely address the interrelated questions of Wikipedia’s knowledge representativeness and reliability. Therefore, some directions for future work include comparing Wikipedia coverage with expert COVID-19 review articles, as well as studying Wikipedia edit and discussion history in order to assess editor motivations. Another interesting direction for future work is the assessment of all Wikipedia citations of any source from COVID-19 Wikipedia pages, because here we only focused on the fraction directed at COVID-19-related scientific articles. Lastly, future work can address the engagement of Wikipedia users with cited COVID-19-related sources.

Wikipedia is a fundamental source of free knowledge, open to all. The capacity of its editor community to respond quickly to a crisis and provide high-quality content is, therefore, critical. Our results here are encouraging in this respect.

## COMPETING INTERESTS

The author has no competing interests.

## FUNDING INFORMATION

This research was not funded.

## DATA AND CODE AVAILABILITY

All the analyses can be replicated using code and following the instructions given in the accompanying repository: https://github.com/Giovanni1085/covid-19_wikipedia. The preparation of the data follows the steps detailed in this repository instead: https://github.com/CWTSLeiden/cwts_covid (Colavizza et al., 2020). Analyses based on Altmetric and Dimensions data require access to these services.

## Notes

6

The identifiers considered by Altmetric in order to establish a citation from Wikipedia to an article currently include DOI, URI from a domain white list, PMID, PMCID, and arXiv ID. https://help.altmetric.com/support/solutions/articles/6000060980-how-does-altmetric-track-mentions-on-wikipedia (accessed April 27, 2020).

8

We used gensim’s implementation for LDA (Řhůřek & Sojka, 2010) and tomotopy for CTM and HTM, https://bab2min.github.io/tomotopy (version 0.7.0). The reader can find more results and the code to replicate all experiments in the accompanying repository.

10

Calculated using Altmetric data, which distinguishes among the number of researchers (r), experts (e), practitioners (p) and members of the public (m) engaging with an article. The expert ratio is defined as $r+e+pr+e+p+m$.

11

Marginal effect coefficients should be interpreted as follows. For binary discrete variables (0/1), they represent the discrete rate of change in the probability of the outcome, everything else kept fixed; therefore, a change from 0 to 1 with a significant coefficient of 0.01 entails an increase in the probability of the outcome of 1%. For categorical variables with more than two outcomes, they represent the difference in the predicted probabilities of any one category relative to the reference category. For continuous variables, they represent the instantaneous rate of change. It might be the case that this can also be interpreted linearly (e.g., a significant change of 1 in the variable entails a change proportional to the marginal effect coefficient in the probability of the outcome). Yet, this rests on the assumption that the relationship between independent and dependent variables is linear, irrespective of the orders of magnitude under consideration. This might not be the case in practice.

## REFERENCES

,
C. E.
,
Montgomery
,
A. A.
,
Aburrow
,
T.
,
Bloomfield
,
S.
Briley
,
P. M.
, …
Xia
,
J.
(
2020
).
.
BMJ Open
,
10
(
2
),
e033655
.
,
W.
,
Torres-Salinas
,
D.
,
Herrera-Viedma
,
E.
, &
Romero-Frías
,
E.
(
2020
).
Science through Wikipedia: A novel representation of open knowledge through co-citation networks
.
PLOS ONE
,
15
(
2
),
e0228713
.
Blei
,
D. M.
(
2012
)
Probabilistic topic models
.
Communications of the ACM
,
55
(
4
),
77
84
.
Blei
,
D. M.
, &
Lafferty
,
J. D.
(
2007
).
A correlated topic model of Science
.
Annals of Applied Statistics
,
1
(
1
),
17
35
.
Blei
,
D. M.
,
Ng
,
A. Y.
, &
Jordan
,
M. I.
(
2003
).
Latent Dirichlet allocation
.
Journal of Machine Learning Research
,
3
,
993
1022
.
Brezar
,
A.
, &
Heilman
,
J.
(
2019
).
Readability of English Wikipedia’s health information over time
.
WikiJournal of Medicine
,
6
(
1
),
7
.
Chen
,
C.-C.
, &
Roth
,
C.
(
2012
).
{{Citation needed}}: The dynamics of referencing in Wikipedia
. In
Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration
.
New York, NY
:
ACM Press
.
Cinelli
,
M.
,
Quattrociocchi
,
W.
,
Galeazzi
,
A.
,
Valensise
,
C. M.
,
Brugnoli
,
E.
, …
Scala
,
A.
(
2020
).
The COVID-19 social media infodemic
.
Scientific Reports
,
10
,
16598
.
Cohan
,
A.
,
Feldman
,
S.
,
Beltagy
,
I.
,
Downey
,
D.
, &
Weld
,
D. S.
(
2020
).
SPECTER: Document-level representation learning using citation-informed transformers
.
arXiv
,
2004.07180
. http://arxiv.org/abs/2004.07180.
Cohen
,
J.
(
1988
).
Statistical power analysis for the behavioral sciences
.
London
:
Routledge
.
Colavizza
,
G.
,
Costas
,
R.
,
Traag
,
V. A.
,
van Eck
,
N. J.
,
van Leeuwen
,
T.
, &
Waltman
,
L.
(
2020
).
A scientometric overview of CORD-19
.
bioRxiv
,
2020.04.20.046144
.
Dimensions
. (
2020
).
Dimensions COVID-19 publications, datasets and clinical trials
. https://dimensions.figshare.com/articles/Dimensions_COVID-19_publications_datasets_and_clinical_trials/11961063
European Commission
. (
2020
).
Fighting disinformation: EU actions to tackle COVID-19 disinformation
. https://www.consilium.europa.eu/en/policies/coronavirus/fighting-disinformation/
Fang
,
Z.
, &
Costas
,
Z.
(
2020
).
Studying the accumulation velocity of altmetric data tracked by Altmetric.com
.
Scientometrics
,
123
,
1077
1101
.
Forte
,
A.
,
Andalibi
,
N.
,
Gorichanaz
,
T.
,
Kim
,
M. C.
,
Park
,
T.
, &
Halfaker
,
A.
(
2018
).
Information fortification: An online citation behavior
. In
Proceedings of the 2018 ACM Conference on Supporting Groupwork – GROUP ’18
(pp.
83
92
).
New York, NY
:
ACM Press
.
Geiger
,
R. S.
, &
Halfaker
,
A.
(
2013
).
When the levee breaks: Without bots, what happens to Wikipedia’s quality control processes?
In
Proceedings of the 9th International Symposium on Open Collaboration
(pp.
1
6
).
New York, NY
:
ACM Press
.
Halfaker
,
A.
,
Mansurov
,
B.
,
Redi
,
M.
, &
Taraborelli
,
D.
(
2018
).
Citations with identifiers in Wikipedia
. https://figshare.com/articles/Citations_with_identifiers_in_Wikipedia/1299540/1
Heilman
,
J. M.
,
Kemmann
,
E.
,
Bonert
,
M.
,
Chatterjee
,
A.
,
Ragar
,
B.
, …
Laurent
,
M. R.
(
2011
).
Wikipedia: A key tool for global public health promotion
.
Journal of Medical Internet Research
,
13
(
1
),
e14
.
Herzog
,
C.
,
Hook
,
D.
, &
Konkiel
,
S.
(
2020
).
Dimensions: Bringing down barriers between scientometricians and data
.
Quantitative Science Studies
,
1
(
1
),
387
395
.
Ioannidis
,
J. P.
(
2020
).
Coronavirus disease 2019: The harms of exaggerated information and non-evidence-based measures
.
European Journal of Clinical Investigation
,
50
(
4
),
e13222
.
Jung
,
C.
,
Geng
,
S.
,
Cha
,
M.
,
Hong
,
I.
, &
Sáez-Trumper
,
D.
(
2020
).
Open data and COVID-19: Wikipedia as an informational resource during the pandemic
. https://medium.com/@diegosaeztrumper/open-data-and-covid-19-wikipedia-as-an-informational-resource-during-the-pandemic-dcca6a
Keegan
,
B.
,
Gergle
,
D.
, &
Contractor
,
N
. (
2011
).
Hot off the wiki: Dynamics, practices, and structures in Wikipedia’s coverage of the To¯hoku catastrophes
. In
Proceedings of the 7th International Symposium on Wikis and Open Collaboration – WikiSym ’11
.
New York, NY
:
ACM Press
.
Kessler
,
M. M.
(
1963
).
Bibliographic coupling between scientific papers
.
American Documentation
,
14
(
1
),
10
25
.
Kousha
,
K.
, &
Thelwall
,
M.
(
2017
).
Are Wikipedia citations important evidence of the impact of scholarly articles and books?
Journal of the Association for Information Science and Technology
,
68
(
3
),
762
779
.
Kousha
,
K.
, &
Thelwall
,
M.
(
2020
).
COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts
.
Quantitative Science Studies
,
1
(
3
),
1068
1091
.
Kruskal
,
W. H.
, &
Wallis
,
W. A.
(
1952
).
Use of ranks in one-criterion variance analysis
.
Journal of the American Statistical Association
,
47
(
260
),
583
621
.
Kumar
,
S.
,
West
,
R.
, &
Leskovec
,
J.
(
2016
).
Disinformation on the Web: Impact, characteristics, and detection of Wikipedia hoaxes
. In
Proceedings of the 25th International Conference on World Wide Web
(pp.
591
602
).
New York, NY
:
ACM Press
.
Laurent
,
M. R.
, &
Vickers
,
T. J.
(
2009
).
Seeking health information online: Does Wikipedia matter?
Journal of the American Medical Informatics Association
,
16
(
4
),
471
479
.
Lemmerich
,
F.
,
Sáez-Trumper
,
D.
,
West
,
R.
, &
Zia
,
L.
(
2019
).
Why the world reads Wikipedia: Beyond English speakers
. In
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
(pp.
618
626
).
New York, NY
:
ACM Press
.
Lewoniewski
,
W.
,
We˛cel
,
K.
, &
Abramowicz
,
W.
(
2017
).
Analysis of references across Wikipedia languages
. In
R.
Damaševicˇius
&
V.
Mikašyte˙
(Eds.)
Information and Software Technologies
,
Vol. 756
, pp.
561
573
.
Cham
:
Springer
.
Leydesdorff
,
L.
, &
Nerghes
,
A.
(
2017
).
Co-word maps and topic modeling: A comparison using small and medium-sized corpora (N < 1,000)
.
Journal of the Association for Information Science and Technology
,
68
(
4
),
1024
1035
.
Maggio
,
L. A.
,
Willinsky
,
J. M.
,
Steinberg
,
R. M.
,
Mietchen
,
D.
,
Wass
,
J. L.
, &
Dong
,
T.
(
2019
).
Wikipedia as a gateway to biomedical research: The relative distribution and use of citations in the English Wikipedia
.
PLOS ONE
,
12
(
12
),
e0190046
.
Maggio
,
L. A.
,
Steinberg
,
R. M.
,
Piccardi
,
T.
, &
Willinsky
,
J. M.
(
2020
).
Reader engagement with medical content on Wikipedia
.
eLife
,
9
,
e52426
.
Martín-Martín
,
A.
,
Thelwall
,
M.
, &
López-Cózar
,
E. D.
(
2020
).
Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: A multidisciplinary comparison of coverage via citations
.
arXiv
,
2004.14329
. https://arxiv.org/abs/2004.14329.
Mesgari
,
M.
,
Okoli
,
C.
,
Mehdi
,
M.
,
Nielsen
,
F. A.
, &
Lanamäki
,
A.
(
2015
).
“The sum of all human knowledge”: A systematic review of scholarly research on the content of Wikipedia
.
Journal of the Association for Information Science and Technology
,
66
(
2
),
219
245
.
Mimno
,
D.
,
Wallach
,
H.
,
Talley
,
E.
,
Leenders
,
M.
, &
McCallum
,
A.
(
2011
).
Optimizing semantic coherence in topic models
. In
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing
(pp.
262
272
).
New York, NY
:
ACM
.
Neumann
,
M.
,
King
,
D.
,
Beltagy
,
I.
, &
Ammar
,
W.
(
2019
).
ScispaCy: Fast and robust models for biomedical natural language processing
.
arXiv
,
1902.07669
. https://arxiv.org/pdf/1902.07669.
Nielsen
,
F. A.
(
2007
).
Scientific citations in Wikipedia
.
First Monday
,
12
(
8
). https://firstmonday.org/ojs/index.php/fm/article/view/1997/1872.
Nielsen
,
F. A.
,
Mietchen
,
D.
, &
Willighagen
,
E.
(
2017
).
Scholia, Scientometrics and Wikidata
. In
E.
Blomqvist
,
K.
Hose
,
H.
Paulheim
,
A.
Ławrynowicz
,
F.
Ciravegna
, &
O.
Hartig
(Eds.)
The Semantic Web: ESWC 2017 Satellite Events
,
Vol. 10577
, pp.
237
259
.
Cham
:
Springer
.
Ortega
,
J. L.
(
2018
).
Reliability and accuracy of altmetric providers: A comparison among Altmetric.com, PlumX and Crossref Event Data
.
Scientometrics
,
116
(
3
),
2123
2138
.
Paakkari
,
L.
, &
Okan
,
O.
(
2020
).
COVID-19: Health literacy is an underestimated problem
.
The Lancet Public Health
,
5
(
5
),
e249
e250
.
Perianes-Rodriguez
,
A.
,
Waltman
,
L.
, &
van Eck
,
N. J.
(
2016
).
Constructing bibliometric networks: A comparison between full and fractional counting
.
Journal of Informetrics
,
10
(
4
),
1178
1195
.
Piccardi
,
T.
,
Redi
,
M.
,
Colavizza
,
G.
, &
West
,
R.
(
2020
).
Quantifying engagement with citations on Wikipedia
. In
Proceedings of The Web Conference 2020
(pp.
2365
2376
).
New York, NY
:
ACM
.
Piscopo
,
A.
, &
Simperl
,
E
. (
2019
).
What we talk about when we talk about Wiki-data quality: A literature survey
. In
Proceedings of the 15th International Symposium on Open Collaboration
.
New York, NY
:
ACM Press
.
Priedhorsky
,
R.
,
Chen
,
J.
,
Lam
,
S. T. K.
,
Panciera
,
K.
,
Terveen
,
L.
, &
Riedl
,
J
. (
2007
).
Creating, destroying, and restoring value in Wikipedia
. In
Proceedings of the 2007 international ACM conference on Conference on Supporting Group Work
.
New York, NY
:
ACM Press
.
Priem
,
J.
,
Piwowar
,
H. A.
, &
Hemminger
,
B. M.
(
2012
).
Altmetrics in the wild: Using social media to explore scholarly impact
.
arXiv
,
1203.4745v1
. https://arxiv.org/abs/1203.4745v1
Řhůřek
,
R.
, &
Sojka
,
P.
(
2010
).
Software framework for topic modelling with large corpora
. In
Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks
(pp.
45
50
). http://is.muni.cz/publication/884893/en
Robinson-García
,
N.
,
Torres-Salinas
,
D.
,
Zahedi
,
Z.
, &
Costas
,
R.
(
2014
).
New data, new possibilities: Exploring the insides of Altmetric.com
.
El Profesional de la Informacion
,
23
(
4
),
359
366
.
Shafee
,
T.
,
Masukume
,
G.
,
Kipersztok
,
L.
,
Das
,
D.
,
Häggström
,
M.
, &
Heilman
,
J.
(
2017
).
Evolution of Wikipedia’s medical content: Past, present and future
.
Journal of Epidemiology and Community Health
,
71
,
1122
1129
.
Shuai
,
X.
,
Jiang
,
Z.
,
Liu
,
X.
, &
Bollen
,
J
. (
2013
).
A comparative study of academic and Wikipedia ranking
. In
Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries – JCDL ’13
.
New York, NY
:
ACM Press
.
Singer
,
P.
,
Lemmerich
,
F.
,
West
,
R.
,
Zia
,
L.
,
Wulczyn
,
E.
,
Strohmaier
,
M.
, &
Leskovec
,
J.
(
2017
).
. In
Proceedings of the 26th International Conference on World Wide Web
(pp.
1591
1600
).
New York, NY
:
ACM Press
.
Smith
,
D. A.
(
2020
).
Situating Wikipedia as a health information resource in various contexts: A scoping review
.
PLOS ONE
,
15
(
2
),
e0228786
.
Sugimoto
,
C. R.
,
Work
,
S.
,
Larivière
,
V.
, &
Haustein
,
S.
(
2017
).
Scholarly use of social media and altmetrics: A review of the literature
.
Journal of the Association for Information Science and Technology
,
68
(
9
),
2037
2062
.
Swire-Thompson
,
B.
, &
Lazer
,
D.
(
2020
).
Public health and online misinformation: Challenges and recommendations
.
Annual Review of Public Health
,
41
(
1
),
433
451
.
Teh
,
Y. W.
,
Jordan
,
M. I.
,
Beal
,
M. J.
, &
Blei
,
D. M.
(
2006
).
Hierarchical Dirichlet processes
.
Journal of the American Statistical Association
,
101
(
476
),
1566
1581
.
Teplitskiy
,
M.
,
Lu
,
G.
, &
Duede
,
E.
(
2017
).
Amplifying the impact of open access: Wikipedia and the diffusion of science
.
Journal of the Association for Information Science and Technology
,
68
(
9
),
2116
2127
.
Thelwall
,
M.
(
2016
).
The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression
.
Journal of Informetrics
,
10
(
2
),
336
346
.
Thelwall
,
M.
, &
Wilson
,
P.
(
2014
).
Regression for citation data: An evaluation of different methods
.
Journal of Informetrics
,
8
(
4
),
963
971
.
Torres-Salinas
,
D.
,
Romero-Frías
,
E.
, &
,
W.
(
2019
).
Mapping the backbone of the Humanities through the eyes of Wikipedia
.
Journal of Informetrics
,
13
(
3
),
793
803
.
Traag
,
V. A.
,
Van Dooren
,
P.
, &
Nesterov
,
Y.
(
2011
).
Narrow scope for resolution-limit-free community detection
.
Physical Review E
,
84
(
1
),
016114
.
Traag
,
V. A.
,
Waltman
,
L.
, &
van Eck
,
N. J.
(
2019
).
From Louvain to Leiden: Guaranteeing well-connected communities
.
Scientific Reports
,
9
(
1
),
5233
.
Wang
,
L. L.
,
Lo
,
K.
,
Chandrasekhar
,
Y.
,
Reas
,
R.
,
Yang
,
J.
,
Eide
,
D.
, …
Kohlmeier
,
S.
(
2020
).
CORD-19: The Covid-19 Open Research Dataset
.
arXiv
,
2004.10706
. http://arxiv.org/abs/2004.10706
Wikimedia Foundation
. (
2020
).
Responding to COVID-19. How we can help in this time of uncertainty
. https://wikimediafoundation.org/covid19
World Health Organization
. (
2020a
).
EPI-WIN: WHO information network for epidemics
. https://www.who.int/teams/risk-communication
Xie
,
B.
,
He
,
D.
,
Mercer
,
T.
,
Wang
,
Y.
,
Wu
,
D.
, …
Lee
,
M. K.
(
2020
).
Global health crises are also information crises: A call to action
.
Journal of the Association for Information Science and Technology
,
1
5
.
Yau
,
C.-K.
,
Porter
,
A.
,
Newman
,
N.
, &
Suominen
,
A.
(
2014
).
Clustering scientific documents with topic modeling
.
Scientometrics
,
100
(
3
),
767
786
.
Zahedi
,
Z.
,
Costas
,
R.
, &
Wouters
,
P.
(
2014
).
How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications
.
Scientometrics
,
101
(
2
),
1491
1513
.
Zarocostas
,
J.
(
2020
).
How to fight an infodemic
.
Lancet
,
395
(
10225
).

### APPENDIX

#### A.1. TOPICS

Refer to Figures A2 and A3 for topic intensities over time. See Figure A5 for the topic clustering. The topic label is given next to the topic number, for reference.

• •

Topic #0, Public health: “method”, “system”, “use”, “drug”, “application”, “approach”, “image”, “design”, “test”, “develop”, “technology”, “provide”, “technique”, “new”, “tool”, “potential”, “base”, “device”, “allow”, “result”.

• •

Topic #1, Public health: “health”, “pandemic”, “covid-19”, “COVID-19”, “public”, “country”, “outbreak”, “social”, “care”, “covid-19_pandemic”, “measure”, “policy”, “people”, “public_health”, “Health”, “impact”, “response”, “risk”, “medical”, “need”.

• •

Topic #2, Molecular biology and immunology: “cell”, “infection”, “response”, “mouse”, “immune”, “expression”, “lung”, “induce”, “disease”, “cat”, “role”, “tissue”, “system”, “increase”, “level”, “receptor”, “study”, “gene”, “cytokine”, “human”.

• •

Topic #3, Clinical medicine: “group”, “patient”, “day”, “study”, “year”, “result”, “rate”, “age”, “method”, “compare”, “conclusion”, “total”, “time”, “period”, “mean”, “respectively”, “high”, “month”, “significantly”.

• •

Topic #4, Molecular biology and immunology: “protein”, “virus”, “cell”, “rna”, “viral”, “coronavirus”, “activity”, “replication”, “gene”, “antiviral”, “study”, “human”, “membrane”, “domain”, “binding”, “structure”, “sequence”, “target”, “infection”, “inhibitor”.

• •

Topic #5, Coronaviruses: “respiratory”, “infection”, “acute”, “virus”, “syndrome”, “SARS”, “severe”, “respiratory_syndrome”, “severe_acute”, “influenza”, “child”, “case”, “patient”, “viral”, “acute respiratory syndrome”, “cause”, “coronavirus”, “clinical”, “sars”, “pneumonia”.

• •

Topic #6, Molecular biology and immunology: “virus”, “antibody”, “strain”, “sample”, “detect”, “sequence”, “assay”, “isolate”, “coronavirus”, “detection”, “test”, “gene”, “calf ”, “result”, “serum”, “positive”, “analysis”, “study”, “bovine”, “ibv”.

• •

Topic #7, Clinical medicine: “patient”, “surgery”, “laparoscopic”, “surgical”, “procedure”, “cancer”, “complication”, “perform”, “technique”, “undergo”, “postoperative”, “case”, “tumor”, “result”, “method”, “repair”, “time”, “patient undergo”, “resection”, “hernia”.

• •

Topic #8, Coronaviruses: “covid-19”, “COVID-19”, “sars-cov-2”, “coronavirus”, “case”, “disease”, “patient”, “2019”, “2020”, “infection”, “severe”, “clinical”, “China”, “novel”, “confirm”, “coronavirus_disease”, “report”, “symptom”, “novel_coronavirus”, “Wuhan”.

• •

Topic #9, Epidemics: “model”, “datum”, “number”, “analysis”, “epidemic”, “case”, “time”, “network”, “study”, “different”, “result”, “rate”, “dynamic”, “base”, “paper”, “estimate”, “propose”, “population”, “spread”, “individual”.

• •

Topic #10, Public health: “study”, “review”, “trial”, “include”, “clinical”, “treatment”, “search”, “evidence”, “literature”, “result”, “datum”, “intervention”, “quality”, “report”, “systematic”, “use”, “outcome”, “method”, “research”, “article”.

• •

Topic #11, Epidemics: “disease”, “vaccine”, “infectious”, “human”, “review”, “virus”, “new”, “infectious_disease”, “emerge”, “development”, “animal”, “infection”, “pathogen”, “recent”, “potential”, “cause”, “vaccination”, “infectious diseases”, “outbreak”, “include”.

• •

Topic #12, Epidemics: “risk”, “factor”, “associate”, “associated with”, “mortality”, “high”, “analysis”, “increase”, “study”, “95_ci”, “risk_factor”, “death”, “age”, “patient”, “rate”, “ratio”, “outcome”, “regression”.

• •

Topic #13, Clinical medicine: “effect”, “increase”, “group”, “study”, “level”, “concentration”, “control”, “blood”, “change”, “pressure”, “result”, “high”, “low”, “decrease”, “compare”, “measure”, “temperature”, “significantly”, “weight”, “reduce”.

• •

Topic #14, Clinical medicine: “patient”, “treatment”, “clinical”, “acute”, “lung”, “therapy”, “chest”, “aneurysm”, “outcome”, “treat”, “ventilation”, “care”, “case”, “artery”, “stroke”, “failure”, “lesion”, “pulmonary”, “diagnosis”.

#### A.2. TABLES

Table A1.
Top 20 citing Wikipedia articles
# citationsWikipedia idWikipedia article titleLang
62 201983 Coronavirus en
59 62786585 Severe acute respiratory syndrome coronavirus 2 en
53 62750956 2019–20 Wuhan coronavirus outbreak en
49 63030231 Coronavirus disease 2019 en
39 63676463 2019–20 coronavirus pandemic en
36 63430824 COVID-19 drug repurposing research en
33 63895130 Paediatric multisystem inflammatory syndrome en
32 211547 Severe acute respiratory syndrome-related coro… en
31 63319438 COVID-19 vaccine en
30 63435931 COVID-19 drug development en
34 39532251 Middle East respiratory syndrome en
28 19572217 Influenza en
25 63204759 COVID-19 testing en
23 2717089 Angiotensin-converting enzyme 2 en
24 22693252 Feline coronavirus en
22 64144585 Management of COVID-19 en
22 4354646 Emergent virus en
21 10849236 Antibody-dependent enhancement en
20 196741 Severe acute respiratory syndrome en
20 64144627 Prognosis of COVID-19 en
# citationsWikipedia idWikipedia article titleLang
62 201983 Coronavirus en
59 62786585 Severe acute respiratory syndrome coronavirus 2 en
53 62750956 2019–20 Wuhan coronavirus outbreak en
49 63030231 Coronavirus disease 2019 en
39 63676463 2019–20 coronavirus pandemic en
36 63430824 COVID-19 drug repurposing research en
33 63895130 Paediatric multisystem inflammatory syndrome en
32 211547 Severe acute respiratory syndrome-related coro… en
31 63319438 COVID-19 vaccine en
30 63435931 COVID-19 drug development en
34 39532251 Middle East respiratory syndrome en
28 19572217 Influenza en
25 63204759 COVID-19 testing en
23 2717089 Angiotensin-converting enzyme 2 en
24 22693252 Feline coronavirus en
22 64144585 Management of COVID-19 en
22 4354646 Emergent virus en
21 10849236 Antibody-dependent enhancement en
20 196741 Severe acute respiratory syndrome en
20 64144627 Prognosis of COVID-19 en

Table A2.
Top 20 cited journal articles. The first column gives the number of distinct citing Wikipedia articles, while the last column gives the number of citations to these articles from the scientific literature (data from Dimensions)
# citationsDOITitlePublication yearJournalTimes cited
67 10.3390/info11050263 Modeling Popularity and Reliability of Sources … 2020 Information
18 10.1007/s00705-012-1299-6 Ratification vote on taxonomic proposals to … 2012 Archives of Virology 210
15 10.1007/978-1-4939-2438-7_1 Coronaviruses: An Overview of Their Replication … 2015 Methods in Molecular Biology 395
15 10.3390/v2081803 Coronavirus Genomics and Bioinformatics Analysis 2010 Viruses 143
13 10.1016/s0140-6736(20)30183-5 Clinical features of patients infected with 20 … 2020 The Lancet 5508
12 10.3390/su12104295 An Integrated Planning Framework for Sustainability … 2020 Sustainability
12 10.1002/med.20081 The regulation of HIV-1 transcription … 2006 Medicinal Research Reviews 93
11 10.1056/nejmoa2001316 Early Transmission Dynamics in Wuhan, China, … 2020 New England Journal of Medicine 2399
11 10.3390/v11020174 Global Epidemiology of Bat Coronaviruses 2019 Viruses 39
11 10.1056/nejmoa2001191 First Case of 2019 Novel Coronavirus … 2020 New England Journal of Medicine 1127
10 10.1083/jcb.148.5.931 Pex19 Binds Multiple Peroxisomal Membrane Proteins, Is Predominantly Cytoplasmic … 2000 Journal of Cell Biology NaN
10 10.1038/s41586-020-2012-7 A pneumonia outbreak associated with a new coronavirus … 2020 Nature 2115
10.1016/s2215-0366(19)30401-8 Cannabinoids for the treatment of mental disorders … 2019 The Lancet Psychiatry 37
10.1128/jvi.06540-11 Discovery of seven novel Mammalian and avian … 2012 Journal of Virology 453
10.1038/s41591-020-0820-9 The proximal origin of SARS-CoV-2 2020 Nature Medicine 411
10.1001/jama.2016.17324 Prevalence of Depression, Depressive Symptoms, … 2016 JAMA 398
10.1016/s0140-6736(20)31180-6 Hydroxychloroquine or chloroquine with or with … 2020 The Lancet 84
10.1016/j.pnpbp.2006.01.008 Human brain evolution and the “Neuroevolutionary … 2006 Progress in Neuro-Psychopharmacology & Biological Psychiatry 63
10.1016/s0140-6736(20)30567-5 How will country-based mitigation measures … 2020 The Lancet 303
10.1086/511159 Infectious Diseases Society of America/America … 2007 Clinical Infectious Diseases 3967
# citationsDOITitlePublication yearJournalTimes cited
67 10.3390/info11050263 Modeling Popularity and Reliability of Sources … 2020 Information
18 10.1007/s00705-012-1299-6 Ratification vote on taxonomic proposals to … 2012 Archives of Virology 210
15 10.1007/978-1-4939-2438-7_1 Coronaviruses: An Overview of Their Replication … 2015 Methods in Molecular Biology 395
15 10.3390/v2081803 Coronavirus Genomics and Bioinformatics Analysis 2010 Viruses 143
13 10.1016/s0140-6736(20)30183-5 Clinical features of patients infected with 20 … 2020 The Lancet 5508
12 10.3390/su12104295 An Integrated Planning Framework for Sustainability … 2020 Sustainability
12 10.1002/med.20081 The regulation of HIV-1 transcription … 2006 Medicinal Research Reviews 93
11 10.1056/nejmoa2001316 Early Transmission Dynamics in Wuhan, China, … 2020 New England Journal of Medicine 2399
11 10.3390/v11020174 Global Epidemiology of Bat Coronaviruses 2019 Viruses 39
11 10.1056/nejmoa2001191 First Case of 2019 Novel Coronavirus … 2020 New England Journal of Medicine 1127
10 10.1083/jcb.148.5.931 Pex19 Binds Multiple Peroxisomal Membrane Proteins, Is Predominantly Cytoplasmic … 2000 Journal of Cell Biology NaN
10 10.1038/s41586-020-2012-7 A pneumonia outbreak associated with a new coronavirus … 2020 Nature 2115
10.1016/s2215-0366(19)30401-8 Cannabinoids for the treatment of mental disorders … 2019 The Lancet Psychiatry 37
10.1128/jvi.06540-11 Discovery of seven novel Mammalian and avian … 2012 Journal of Virology 453
10.1038/s41591-020-0820-9 The proximal origin of SARS-CoV-2 2020 Nature Medicine 411
10.1001/jama.2016.17324 Prevalence of Depression, Depressive Symptoms, … 2016 JAMA 398
10.1016/s0140-6736(20)31180-6 Hydroxychloroquine or chloroquine with or with … 2020 The Lancet 84
10.1016/j.pnpbp.2006.01.008 Human brain evolution and the “Neuroevolutionary … 2006 Progress in Neuro-Psychopharmacology & Biological Psychiatry 63
10.1016/s0140-6736(20)30567-5 How will country-based mitigation measures … 2020 The Lancet 303
10.1086/511159 Infectious Diseases Society of America/America … 2007 Clinical Infectious Diseases 3967

Table A3.
Test statistics for general topic intensities of articles cited in Wikipedia or not, limited to articles published before 2020. In W: cited in Wikipedia; Not in W: not cited in Wikipedia; KWH: Kruskal–Wallis H test
General topicIn WNot in WTestp-valueEffect size
MeanSDMeanSDKWHKWHCohen’s d
Coronaviruses 0.092 0.156 0.094 0.165 0.06 0.807 0.015
Epidemics 0.225 0.197 0.161 0.186 357.84 0.0 0.341
Public health 0.195 0.222 0.172 0.215 35.158 0.0 0.107
Molecular biology and immunology 0.349 0.313 0.294 0.324 102.917 0.0 0.17
Clinical medicine 0.129 0.198 0.266 0.307 475.092 0.0 0.45
General topicIn WNot in WTestp-valueEffect size
MeanSDMeanSDKWHKWHCohen’s d
Coronaviruses 0.092 0.156 0.094 0.165 0.06 0.807 0.015
Epidemics 0.225 0.197 0.161 0.186 357.84 0.0 0.341
Public health 0.195 0.222 0.172 0.215 35.158 0.0 0.107
Molecular biology and immunology 0.349 0.313 0.294 0.324 102.917 0.0 0.17
Clinical medicine 0.129 0.198 0.266 0.307 475.092 0.0 0.45

Table A4.
Test statistics for general topic intensities of articles cited in Wikipedia or not, limited to articles published in 2020. In W: cited in Wikipedia; Not in W: not cited in Wikipedia; KWH: Kruskal–Wallis H test
General topicIn WNot in WTestp-valueEffect size
MeanSDMeanSDKWHKWHCohen’s d
Coronaviruses 0.239 0.193 0.157 0.183 171.205 0.0 0.448
Epidemics 0.171 0.166 0.182 0.19 0.041 0.84 0.055
Public health 0.312 0.271 0.387 0.286 46.889 0.0 0.261
Molecular biology and immunology 0.132 0.199 0.103 0.185 15.078 0.0 0.159
Clinical medicine 0.121 0.165 0.147 0.187 19.711 0.0 0.139
General topicIn WNot in WTestp-valueEffect size
MeanSDMeanSDKWHKWHCohen’s d
Coronaviruses 0.239 0.193 0.157 0.183 171.205 0.0 0.448
Epidemics 0.171 0.166 0.182 0.19 0.041 0.84 0.055
Public health 0.312 0.271 0.387 0.286 46.889 0.0 0.261
Molecular biology and immunology 0.132 0.199 0.103 0.185 15.078 0.0 0.159
Clinical medicine 0.121 0.165 0.147 0.187 19.711 0.0 0.139

Table A5.
Test statistics for general topic intensities of articles cited in Wikipedia or not; all publications. In W: cited in Wikipedia; Not in W: not cited in Wikipedia; KWH: Kruskal–Wallis H test
General topicIn WNot in WTestp-valueEffect size
MeanSDMeanSDKWHKWHCohen’s d
Coronaviruses 0.127 0.177 0.122 0.176 0.173 0.678 0.025
Epidemics 0.212 0.191 0.17 0.188 224.428 0.0 0.221
Public health 0.223 0.24 0.268 0.271 53.051 0.0 0.167
Molecular biology and immunology 0.297 0.305 0.209 0.287 343.365 0.0 0.309
Clinical medicine 0.127 0.191 0.213 0.267 331.354 0.0 0.323
General topicIn WNot in WTestp-valueEffect size
MeanSDMeanSDKWHKWHCohen’s d
Coronaviruses 0.127 0.177 0.122 0.176 0.173 0.678 0.025
Epidemics 0.212 0.191 0.17 0.188 224.428 0.0 0.221
Public health 0.223 0.24 0.268 0.271 53.051 0.0 0.167
Molecular biology and immunology 0.297 0.305 0.209 0.287 343.365 0.0 0.309
Clinical medicine 0.127 0.191 0.213 0.267 331.354 0.0 0.323

#### A.3. FIGURES

Figure A1.

Timing of new citations from Wikipedia, and publication years of the articles they refer to. (a) Number of citations from Wikipedia to COVID-19 literature, per year, overall. (b) Publication year of the articles cited from Wikipedia, overall.

Figure A1.

Timing of new citations from Wikipedia, and publication years of the articles they refer to. (a) Number of citations from Wikipedia to COVID-19 literature, per year, overall. (b) Publication year of the articles cited from Wikipedia, overall.

Figure A2.

Heatmap of topic intensities over time.

Figure A2.

Heatmap of topic intensities over time.

Figure A3.

General topic intensities over time. (a) Average aggregate; this can be interpreted as the average topic intensity. (b) Cumulative aggregate; this can be interpreted as the number of papers per topic.

Figure A3.

General topic intensities over time. (a) Average aggregate; this can be interpreted as the average topic intensity. (b) Cumulative aggregate; this can be interpreted as the number of papers per topic.

Figure A4.

Heatmap of regression variables correlations (Pearson’s), after transformations.

Figure A4.

Heatmap of regression variables correlations (Pearson’s), after transformations.

Figure A5.

Agglomerative clustering dendrogram over topics, based on Jensen­Shannon distances. Considering a cut at 1.1, the left-most cluster (topics 3, 12, 13) focuses on viral epidemics and clinical medicine; next is a cluster on COVID-19 and its treatment in intensive care (topics 7, 8, 14); next is a clus­ ter COVID-19, public health, epidemics, and immunology (topics 0, 1, 9, 10, 11); lastly, on the right, is a cluster on molecular biology and immunology/vaccines (topics 2, 4, 5, 6).

Figure A5.

Agglomerative clustering dendrogram over topics, based on Jensen­Shannon distances. Considering a cut at 1.1, the left-most cluster (topics 3, 12, 13) focuses on viral epidemics and clinical medicine; next is a cluster on COVID-19 and its treatment in intensive care (topics 7, 8, 14); next is a clus­ ter COVID-19, public health, epidemics, and immunology (topics 0, 1, 9, 10, 11); lastly, on the right, is a cluster on molecular biology and immunology/vaccines (topics 2, 4, 5, 6).

Figure A6.

Cohen’s d-effect statistic for general topic intensity differences be­ tween ar ticles cited in Wikipedia and not. Publications published before 2020, in 2020, and overall are considered. See Table A3, A4, and A5. Effect sizes are con­ sidered very small when below 0.2, small when below 0.5 and medium when below 0.8.

Figure A6.

Cohen’s d-effect statistic for general topic intensity differences be­ tween ar ticles cited in Wikipedia and not. Publications published before 2020, in 2020, and overall are considered. See Table A3, A4, and A5. Effect sizes are con­ sidered very small when below 0.2, small when below 0.5 and medium when below 0.8.

Figure A7.

Some variables used for regression analyses. The plots distinguish variable values for articles cited from Wikipedia (green) or not (blue). (a) Citations (Dimensions). (b) Mendeley readers. (c) Mentions in blogs and news. (d) Twitter (unique) user interactions. (e) SPECTER cluster size. (f) General topic coronaviruses.

Figure A7.

Some variables used for regression analyses. The plots distinguish variable values for articles cited from Wikipedia (green) or not (blue). (a) Citations (Dimensions). (b) Mendeley readers. (c) Mentions in blogs and news. (d) Twitter (unique) user interactions. (e) SPECTER cluster size. (f) General topic coronaviruses.

#### A.4. REGRESSION TABLES

Model:Logistic regression 2020
Method:Marginal effects (Logistic regression)
No. Observations:130,864
Pseudo R-squ.:0.2790
variabledx/dy coefstd errzP > |z|[0.0250.975]
C(publication_year, Treatment(2020))[T.2000.0] −0.0035 0.008 −0.457 0.648 −0.019 0.012
C(publication_year, Treatment(2020))[T.2001.0] −0.0164 0.010 −1.644 0.100 −0.036 0.003
C(publication_year, Treatment(2020))[T.2002.0] −0.0018 0.006 −0.317 0.751 −0.013 0.009
C(publication_year, Treatment(2020))[T.2003.0] −0.0030 0.004 −0.763 0.445 −0.011 0.005
C(publication_year, Treatment(2020))[T.2004.0] 0.0004 0.003 0.127 0.899 −0.005 0.006
C(publication_year, Treatment(2020))[T.2005.0] 0.0050 0.003 1.755 0.079 −0.001 0.011
C(publication_year, Treatment(2020))[T.2006.0] 0.0036 0.003 1.303 0.192 −0.002 0.009
C(publication_year, Treatment(2020))[T.2007.0] 0.0023 0.003 0.792 0.428 −0.003 0.008
C(publication_year, Treatment(2020))[T.2008.0] 0.0034 0.002 1.357 0.175 −0.002 0.008
C(publication_year, Treatment(2020))[T.2009.0] −0.0010 0.003 −0.388 0.698 −0.006 0.004
C(publication_year, Treatment(2020))[T.2010.0] 0.0010 0.002 0.392 0.695 −0.004 0.006
C(publication_year, Treatment(2020))[T.2011.0] −0.0031 0.002 −1.260 0.208 −0.008 0.002
C(publication_year, Treatment(2020))[T.2012.0] −0.0032 0.002 −1.375 0.169 −0.008 0.001
C(publication_year, Treatment(2020))[T.2013.0] −0.0080 0.002 −3.458 0.001 −0.012 −0.003
C(publication_year, Treatment(2020))[T.2014.0] −0.0085 0.002 −3.842 0.000 −0.013 −0.004
C(publication_year, Treatment(2020))[T.2015.0] −0.0123 0.002 −5.528 0.000 −0.017 −0.008
C(publication_year, Treatment(2020))[T.2016.0] −0.0119 0.002 −5.321 0.000 −0.016 −0.008
C(publication_year, Treatment(2020))[T.2017.0] −0.0116 0.002 −5.191 0.000 −0.016 −0.007
C(publication_year, Treatment(2020))[T.2018.0] −0.0112 0.002 −5.001 0.000 −0.016 −0.007
C(publication_year, Treatment(2020))[T.2019.0] −0.0067 0.002 −2.919 0.004 −0.011 −0.002
C(top_j, Treatment(‘OTHER’))[T.Arch_Virol] 0.0031 0.006 0.528 0.598 −0.008 0.015
C(top_j, Treatment(‘OTHER’))[T.ChemRxiv] −0.0010 0.011 −0.089 0.929 −0.023 0.021
C(top_j, Treatment(‘OTHER’))[T.Emerg_Infect_Dis] −0.0001 0.003 −0.051 0.960 −0.006 0.005
C(top_j, Treatment(‘OTHER’))[T.JAMA] −0.0222 0.004 −5.200 0.000 −0.031 −0.014
C(top_j, Treatment(’OTHER’))[T.JMIR_Preprints] −0.2486 10.693 −0.023 0.981 −21.207 20.710
C(top_j, Treatment(’OTHER’))[T.Journal_of_virology] −0.0013 0.003 −0.422 0.673 −0.007 0.005
C(top_j, Treatment(‘OTHER’))[T.Nature] 0.0105 0.003 3.614 0.000 0.005 0.016
C(top_j, Treatment(‘OTHER’))[T.PLoS_One] −0.0034 0.003 −1.255 0.210 −0.009 0.002
C(top_j, Treatment(‘OTHER’))[T.Research_Square] −0.3905 235.195 −0.002 0.999 −461.365 460.584
C(top_j, Treatment(‘OTHER’))[T.SSRN_Electronic_Journal] −0.0173 0.008 −2.208 0.027 −0.033 −0.002
C(top_j, Treatment(‘OTHER’))[T.Sci_Rep] −0.0071 0.005 −1.375 0.169 −0.017 0.003
C(top_j, Treatment(‘OTHER’))[T.Science] 0.0083 0.004 −2.202 0.028 0.001 0.016
C(top_j, Treatment(‘OTHER’))[T.Surgical_endoscopy] −0.0003 0.007 −0.039 0.969 −0.014 0.013
C(top_j, Treatment(‘OTHER’))[T.The_BMJ] 0.0126 0.004 3.267 0.001 0.005 0.020
C(top_j, Treatment(‘OTHER’))[T.The_Lancet] 0.0129 0.003 4.645 0.000 0.007 0.018
C(top_j, Treatment(‘OTHER’))[T.Vaccine] −0.0118 0.006 −1.909 0.056 −0.024 0.000
C(top_j, Treatment(‘OTHER’))[T.Virology] −0.0013 0.004 −0.308 0.758 −0.010 0.007
C(top_j, Treatment(‘OTHER’))[T.Viruses] 0.0091 0.003 2.639 0.008 0.002 0.016
C(top_j, Treatment(‘OTHER’))[T.bioRxiv] −0.0073 0.004 −1.824 0.068 −0.015 0.001
C(top_j, Treatment(’OTHER’))[T.medRxiv] −0.0489 0.008 −6.260 0.000 −0.064 −0.034
times_cited 0.0078 0.000 19.909 0.000 0.007 0.009
counts_mendeley 0.0066 0.000 20.967 0.000 0.006 0.007
counts_policy −0.0013 0.001 −1.319 0.187 −0.003 0.001
counts_twitter_unique 0.0050 0.000 13.822 0.000 0.004 0.006
counts_blogs_news 0.0051 0.000 11.343 0.000 0.004 0.006
counts_facebook 0.0008 0.001 1.115 0.265 −0.001 0.002
expert_ratio −0.0067 0.002 −3.679 0.000 −0.010 −0.003
tm_coronaviruses 0.0138 0.018 0.760 0.447 −0.022 0.050
tm_epidemics 0.0241 0.018 1.343 0.179 −0.011 0.059
tm_ph 0.0197 0.018 1.086 0.278 −0.016 0.055
tm_mbi 0.0219 0.018 1.226 0.220 −0.013 0.057
tm_clinical_medicine −0.0064 0.018 −0.362 0.717 −0.041 0.028
spectre_cluster_size 0.0022 0.001 1.483 0.138 −0.001 0.005
network_cluster_size −2.14e−05 0.000 −0.132 0.895 −0.000 0.000
Model:Logistic regression 2020
Method:Marginal effects (Logistic regression)
No. Observations:130,864
Pseudo R-squ.:0.2790
variabledx/dy coefstd errzP > |z|[0.0250.975]
C(publication_year, Treatment(2020))[T.2000.0] −0.0035 0.008 −0.457 0.648 −0.019 0.012
C(publication_year, Treatment(2020))[T.2001.0] −0.0164 0.010 −1.644 0.100 −0.036 0.003
C(publication_year, Treatment(2020))[T.2002.0] −0.0018 0.006 −0.317 0.751 −0.013 0.009
C(publication_year, Treatment(2020))[T.2003.0] −0.0030 0.004 −0.763 0.445 −0.011 0.005
C(publication_year, Treatment(2020))[T.2004.0] 0.0004 0.003 0.127 0.899 −0.005 0.006
C(publication_year, Treatment(2020))[T.2005.0] 0.0050 0.003 1.755 0.079 −0.001 0.011
C(publication_year, Treatment(2020))[T.2006.0] 0.0036 0.003 1.303 0.192 −0.002 0.009
C(publication_year, Treatment(2020))[T.2007.0] 0.0023 0.003 0.792 0.428 −0.003 0.008
C(publication_year, Treatment(2020))[T.2008.0] 0.0034 0.002 1.357 0.175 −0.002 0.008
C(publication_year, Treatment(2020))[T.2009.0] −0.0010 0.003 −0.388 0.698 −0.006 0.004
C(publication_year, Treatment(2020))[T.2010.0] 0.0010 0.002 0.392 0.695 −0.004 0.006
C(publication_year, Treatment(2020))[T.2011.0] −0.0031 0.002 −1.260 0.208 −0.008 0.002
C(publication_year, Treatment(2020))[T.2012.0] −0.0032 0.002 −1.375 0.169 −0.008 0.001
C(publication_year, Treatment(2020))[T.2013.0] −0.0080 0.002 −3.458 0.001 −0.012 −0.003
C(publication_year, Treatment(2020))[T.2014.0] −0.0085 0.002 −3.842 0.000 −0.013 −0.004
C(publication_year, Treatment(2020))[T.2015.0] −0.0123 0.002 −5.528 0.000 −0.017 −0.008
C(publication_year, Treatment(2020))[T.2016.0] −0.0119 0.002 −5.321 0.000 −0.016 −0.008
C(publication_year, Treatment(2020))[T.2017.0] −0.0116 0.002 −5.191 0.000 −0.016 −0.007
C(publication_year, Treatment(2020))[T.2018.0] −0.0112 0.002 −5.001 0.000 −0.016 −0.007
C(publication_year, Treatment(2020))[T.2019.0] −0.0067 0.002 −2.919 0.004 −0.011 −0.002
C(top_j, Treatment(‘OTHER’))[T.Arch_Virol] 0.0031 0.006 0.528 0.598 −0.008 0.015
C(top_j, Treatment(‘OTHER’))[T.ChemRxiv] −0.0010 0.011 −0.089 0.929 −0.023 0.021
C(top_j, Treatment(‘OTHER’))[T.Emerg_Infect_Dis] −0.0001 0.003 −0.051 0.960 −0.006 0.005
C(top_j, Treatment(‘OTHER’))[T.JAMA] −0.0222 0.004 −5.200 0.000 −0.031 −0.014
C(top_j, Treatment(’OTHER’))[T.JMIR_Preprints] −0.2486 10.693 −0.023 0.981 −21.207 20.710
C(top_j, Treatment(’OTHER’))[T.Journal_of_virology] −0.0013 0.003 −0.422 0.673 −0.007 0.005
C(top_j, Treatment(‘OTHER’))[T.Nature] 0.0105 0.003 3.614 0.000 0.005 0.016
C(top_j, Treatment(‘OTHER’))[T.PLoS_One] −0.0034 0.003 −1.255 0.210 −0.009 0.002
C(top_j, Treatment(‘OTHER’))[T.Research_Square] −0.3905 235.195 −0.002 0.999 −461.365 460.584
C(top_j, Treatment(‘OTHER’))[T.SSRN_Electronic_Journal] −0.0173 0.008 −2.208 0.027 −0.033 −0.002
C(top_j, Treatment(‘OTHER’))[T.Sci_Rep] −0.0071 0.005 −1.375 0.169 −0.017 0.003
C(top_j, Treatment(‘OTHER’))[T.Science] 0.0083 0.004 −2.202 0.028 0.001 0.016
C(top_j, Treatment(‘OTHER’))[T.Surgical_endoscopy] −0.0003 0.007 −0.039 0.969 −0.014 0.013
C(top_j, Treatment(‘OTHER’))[T.The_BMJ] 0.0126 0.004 3.267 0.001 0.005 0.020
C(top_j, Treatment(‘OTHER’))[T.The_Lancet] 0.0129 0.003 4.645 0.000 0.007 0.018
C(top_j, Treatment(‘OTHER’))[T.Vaccine] −0.0118 0.006 −1.909 0.056 −0.024 0.000
C(top_j, Treatment(‘OTHER’))[T.Virology] −0.0013 0.004 −0.308 0.758 −0.010 0.007
C(top_j, Treatment(‘OTHER’))[T.Viruses] 0.0091 0.003 2.639 0.008 0.002 0.016
C(top_j, Treatment(‘OTHER’))[T.bioRxiv] −0.0073 0.004 −1.824 0.068 −0.015 0.001
C(top_j, Treatment(’OTHER’))[T.medRxiv] −0.0489 0.008 −6.260 0.000 −0.064 −0.034
times_cited 0.0078 0.000 19.909 0.000 0.007 0.009
counts_mendeley 0.0066 0.000 20.967 0.000 0.006 0.007
counts_policy −0.0013 0.001 −1.319 0.187 −0.003 0.001
counts_twitter_unique 0.0050 0.000 13.822 0.000 0.004 0.006
counts_blogs_news 0.0051 0.000 11.343 0.000 0.004 0.006
counts_facebook 0.0008 0.001 1.115 0.265 −0.001 0.002
expert_ratio −0.0067 0.002 −3.679 0.000 −0.010 −0.003
tm_coronaviruses 0.0138 0.018 0.760 0.447 −0.022 0.050
tm_epidemics 0.0241 0.018 1.343 0.179 −0.011 0.059
tm_ph 0.0197 0.018 1.086 0.278 −0.016 0.055
tm_mbi 0.0219 0.018 1.226 0.220 −0.013 0.057
tm_clinical_medicine −0.0064 0.018 −0.362 0.717 −0.041 0.028
spectre_cluster_size 0.0022 0.001 1.483 0.138 −0.001 0.005
network_cluster_size −2.14e−05 0.000 −0.132 0.895 −0.000 0.000

Model:Logistic regression 2019
Method:Marginal effects (Logistic regression)
No. Observations:69,444
Pseudo R-squ.:0.2670
variabledx/dy coefstd errzP > |z|[0.0250.975]
C(publication_year, Treatment(2019))[T.2000.0] 0.0326 0.010 3.389 0.001 0.014 0.051
C(publication_year, Treatment(2019))[T.2001.0] 0.0190 0.012 1.615 0.106 −0.004 0.042
C(publication_year, Treatment(2019))[T.2002.0] 0.0359 0.008 4.728 0.000 0.021 0.051
C(publication_year, Treatment(2019))[T.2003.0] 0.0322 0.006 5.487 0.000 0.021 0.044
C(publication_year, Treatment(2019))[T.2004.0] 0.0335 0.005 6.812 0.000 0.024 0.043
C(publication_year, Treatment(2019))[T.2005.0] 0.0415 0.005 8.825 0.000 0.032 0.051
C(publication_year, Treatment(2019))[T.2006.0] 0.0365 0.005 7.797 0.000 0.027 0.046
C(publication_year, Treatment(2019))[T.2007.0] 0.0371 0.005 7.913 0.000 0.028 0.046
C(publication_year, Treatment(2019))[T.2008.0] 0.0356 0.004 8.032 0.000 0.027 0.044
C(publication_year, Treatment(2019))[T.2009.0] 0.0297 0.004 6.776 0.000 0.021 0.038
C(publication_year, Treatment(2019))[T.2010.0] 0.0290 0.004 6.644 0.000 0.020 0.038
C(publication_year, Treatment(2019))[T.2011.0] 0.0247 0.004 5.730 0.000 0.016 0.033
C(publication_year, Treatment(2019))[T.2012.0] 0.0262 0.004 6.337 0.000 0.018 0.034
C(publication_year, Treatment(2019))[T.2013.0] 0.0201 0.004 4.874 0.000 0.012 0.028
C(publication_year, Treatment(2019))[T.2014.0] 0.0195 0.004 4.813 0.000 0.012 0.027
C(publication_year, Treatment(2019))[T.2015.0] 0.0129 0.004 3.189 0.001 0.005 0.021
C(publication_year, Treatment(2019))[T.2016.0] 0.0100 0.004 2.418 0.016 0.002 0.018
C(publication_year, Treatment(2019))[T.2017.0] 0.0110 0.004 2.668 0.008 0.003 0.019
C(publication_year, Treatment(2019))[T.2018.0] 0.0085 0.004 2.005 0.045 0.000 0.017
C(top_j, Treatment(‘OTHER’))[T.Arch_Virol] 0.0043 0.008 0.549 0.583 −0.011 0.020
C(top_j, Treatment(‘OTHER’))[T.Emerg_Infect_Dis] 0.0022 0.004 0.574 0.566 −0.005 0.010
C(top_j, Treatment(‘OTHER’))[T.JAMA] 0.0067 0.007 1.029 0.303 −0.006 0.020
C(top_j, Treatment(‘OTHER’))[T.Journal_of_virology] −0.0006 0.004 −0.153 0.879 −0.008 0.007
C(top_j, Treatment(‘OTHER’))[T.Nature] 0.0148 0.005 2.861 0.004 0.005 0.025
C(top_j, Treatment(‘OTHER’))[T.PLoS_One] −0.0070 0.004 −1.926 0.054 −0.014 0.000
C(top_j, Treatment(‘OTHER’))[T.Sci_Rep] 0.0002 0.007 0.027 0.978 −0.013 0.013
C(top_j, Treatment(‘OTHER’))[T.Science] −0.0008 0.009 −0.096 0.923 −0.018 0.016
C(top_j, Treatment(‘OTHER’))[T.Surgical_endoscopy] 0.0037 0.008 0.477 0.633 −0.011 0.019
C(top_j, Treatment(‘OTHER’))[T.The_Lancet] 0.0109 0.005 2.136 0.033 0.001 0.021
C(top_j, Treatment(‘OTHER’))[T.Vaccine] −0.0220 0.010 −2.291 0.022 −0.041 −0.003
C(top_j, Treatment(‘OTHER’))[T.Virology] −0.0048 0.006 −0.826 0.409 −0.016 0.007
C(top_j, Treatment(‘OTHER’))[T.Viruses] 0.0078 0.005 1.562 0.118 −0.002 0.018
C(top_j, Treatment(‘OTHER’))[T.bioRxiv] −0.9986 4.64e+08 −2.15e−09 1.000 −9.1e+08 9.1e+08
times_cited 0.0027 0.001 4.136 0.000 0.001 0.004
counts_mendeley 0.0164 0.001 22.952 0.000 0.015 0.018
counts_policy −0.0020 0.001 −1.377 0.168 −0.005 0.001
counts_twitter_unique 0.0006 0.001 0.915 0.360 −0.001 0.002
counts_blogs_news 0.0051 0.001 6.915 0.000 0.004 0.007
counts_facebook 0.0044 0.001 3.950 0.000 0.002 0.007
expert_ratio −0.0010 0.002 −0.408 0.683 −0.006 0.004
tm_coronaviruses −0.0251 0.034 −0.729 0.466 −0.093 0.042
tm_epidemics −0.0172 0.034 −0.505 0.613 −0.084 0.049
tm_ph −0.0118 0.034 −0.346 0.729 −0.079 0.055
tm_mbi −0.0091 0.034 −0.267 0.789 −0.076 0.057
tm_clinical_medicine −0.0356 0.034 −1.056 0.291 −0.102 0.031
spectre_cluster_size 0.0041 0.002 1.947 0.052 −2.79e−05 0.008
network_cluster_size −0.0009 0.000 −3.371 0.001 −0.001 −0.000
Model:Logistic regression 2019
Method:Marginal effects (Logistic regression)
No. Observations:69,444
Pseudo R-squ.:0.2670
variabledx/dy coefstd errzP > |z|[0.0250.975]
C(publication_year, Treatment(2019))[T.2000.0] 0.0326 0.010 3.389 0.001 0.014 0.051
C(publication_year, Treatment(2019))[T.2001.0] 0.0190 0.012 1.615 0.106 −0.004 0.042
C(publication_year, Treatment(2019))[T.2002.0] 0.0359 0.008 4.728 0.000 0.021 0.051
C(publication_year, Treatment(2019))[T.2003.0] 0.0322 0.006 5.487 0.000 0.021 0.044
C(publication_year, Treatment(2019))[T.2004.0] 0.0335 0.005 6.812 0.000 0.024 0.043
C(publication_year, Treatment(2019))[T.2005.0] 0.0415 0.005 8.825 0.000 0.032 0.051
C(publication_year, Treatment(2019))[T.2006.0] 0.0365 0.005 7.797 0.000 0.027 0.046
C(publication_year, Treatment(2019))[T.2007.0] 0.0371 0.005 7.913 0.000 0.028 0.046
C(publication_year, Treatment(2019))[T.2008.0] 0.0356 0.004 8.032 0.000 0.027 0.044
C(publication_year, Treatment(2019))[T.2009.0] 0.0297 0.004 6.776 0.000 0.021 0.038
C(publication_year, Treatment(2019))[T.2010.0] 0.0290 0.004 6.644 0.000 0.020 0.038
C(publication_year, Treatment(2019))[T.2011.0] 0.0247 0.004 5.730 0.000 0.016 0.033
C(publication_year, Treatment(2019))[T.2012.0] 0.0262 0.004 6.337 0.000 0.018 0.034
C(publication_year, Treatment(2019))[T.2013.0] 0.0201 0.004 4.874 0.000 0.012 0.028
C(publication_year, Treatment(2019))[T.2014.0] 0.0195 0.004 4.813 0.000 0.012 0.027
C(publication_year, Treatment(2019))[T.2015.0] 0.0129 0.004 3.189 0.001 0.005 0.021
C(publication_year, Treatment(2019))[T.2016.0] 0.0100 0.004 2.418 0.016 0.002 0.018
C(publication_year, Treatment(2019))[T.2017.0] 0.0110 0.004 2.668 0.008 0.003 0.019
C(publication_year, Treatment(2019))[T.2018.0] 0.0085 0.004 2.005 0.045 0.000 0.017
C(top_j, Treatment(‘OTHER’))[T.Arch_Virol] 0.0043 0.008 0.549 0.583 −0.011 0.020
C(top_j, Treatment(‘OTHER’))[T.Emerg_Infect_Dis] 0.0022 0.004 0.574 0.566 −0.005 0.010
C(top_j, Treatment(‘OTHER’))[T.JAMA] 0.0067 0.007 1.029 0.303 −0.006 0.020
C(top_j, Treatment(‘OTHER’))[T.Journal_of_virology] −0.0006 0.004 −0.153 0.879 −0.008 0.007
C(top_j, Treatment(‘OTHER’))[T.Nature] 0.0148 0.005 2.861 0.004 0.005 0.025
C(top_j, Treatment(‘OTHER’))[T.PLoS_One] −0.0070 0.004 −1.926 0.054 −0.014 0.000
C(top_j, Treatment(‘OTHER’))[T.Sci_Rep] 0.0002 0.007 0.027 0.978 −0.013 0.013
C(top_j, Treatment(‘OTHER’))[T.Science] −0.0008 0.009 −0.096 0.923 −0.018 0.016
C(top_j, Treatment(‘OTHER’))[T.Surgical_endoscopy] 0.0037 0.008 0.477 0.633 −0.011 0.019
C(top_j, Treatment(‘OTHER’))[T.The_Lancet] 0.0109 0.005 2.136 0.033 0.001 0.021
C(top_j, Treatment(‘OTHER’))[T.Vaccine] −0.0220 0.010 −2.291 0.022 −0.041 −0.003
C(top_j, Treatment(‘OTHER’))[T.Virology] −0.0048 0.006 −0.826 0.409 −0.016 0.007
C(top_j, Treatment(‘OTHER’))[T.Viruses] 0.0078 0.005 1.562 0.118 −0.002 0.018
C(top_j, Treatment(‘OTHER’))[T.bioRxiv] −0.9986 4.64e+08 −2.15e−09 1.000 −9.1e+08 9.1e+08
times_cited 0.0027 0.001 4.136 0.000 0.001 0.004
counts_mendeley 0.0164 0.001 22.952 0.000 0.015 0.018
counts_policy −0.0020 0.001 −1.377 0.168 −0.005 0.001
counts_twitter_unique 0.0006 0.001 0.915 0.360 −0.001 0.002
counts_blogs_news 0.0051 0.001 6.915 0.000 0.004 0.007
counts_facebook 0.0044 0.001 3.950 0.000 0.002 0.007
expert_ratio −0.0010 0.002 −0.408 0.683 −0.006 0.004
tm_coronaviruses −0.0251 0.034 −0.729 0.466 −0.093 0.042
tm_epidemics −0.0172 0.034 −0.505 0.613 −0.084 0.049
tm_ph −0.0118 0.034 −0.346 0.729 −0.079 0.055
tm_mbi −0.0091 0.034 −0.267 0.789 −0.076 0.057
tm_clinical_medicine −0.0356 0.034 −1.056 0.291 −0.102 0.031
spectre_cluster_size 0.0041 0.002 1.947 0.052 −2.79e−05 0.008
network_cluster_size −0.0009 0.000 −3.371 0.001 −0.001 −0.000

Model:OLS regression 2020
Method:OLS
No. Observations:130,809
R-squ.:0.115
variablecoefstd errtP > |t|[0.0250.975]
Intercept −0.0056 0.017 −0.326 0.745 −0.039 0.028
C(publication_year, Treatment(2020))[T.2000.0] −0.0201 0.009 −2.312 0.021 −0.037 −0.003
C(publication_year, Treatment(2020))[T.2001.0] −0.0262 0.008 −3.206 0.001 −0.042 −0.010
C(publication_year, Treatment(2020))[T.2002.0] −0.0105 0.005 −2.078 0.038 −0.020 −0.001
C(publication_year, Treatment(2020))[T.2003.0] −0.0117 0.004 −3.003 0.003 −0.019 −0.004
C(publication_year, Treatment(2020))[T.2004.0] −0.0058 0.003 −1.775 0.076 −0.012 0.001
C(publication_year, Treatment(2020))[T.2005.0] −0.0017 0.003 −0.532 0.594 −0.008 0.005
C(publication_year, Treatment(2020))[T.2006.0] −0.0043 0.003 −1.361 0.173 −0.010 0.002
C(publication_year, Treatment(2020))[T.2007.0] −0.0041 0.003 −1.296 0.195 −0.010 0.002
C(publication_year, Treatment(2020))[T.2008.0] −0.0044 0.003 −1.655 0.098 −0.010 0.001
C(publication_year, Treatment(2020))[T.2009.0] −0.0112 0.002 −4.486 0.000 −0.016 −0.006
C(publication_year, Treatment(2020))[T.2010.0] −0.0110 0.003 −4.380 0.000 −0.016 −0.006
C(publication_year, Treatment(2020))[T.2011.0] −0.0155 0.002 −6.441 0.000 −0.020 −0.011
C(publication_year, Treatment(2020))[T.2012.0] −0.0134 0.002 −5.809 0.000 −0.018 −0.009
C(publication_year, Treatment(2020))[T.2013.0] −0.0220 0.002 −10.165 0.000 −0.026 −0.018
C(publication_year, Treatment(2020))[T.2014.0] −0.0252 0.002 −12.212 0.000 −0.029 −0.021
C(publication_year, Treatment(2020))[T.2015.0] −0.0287 0.002 −14.416 0.000 −0.033 −0.025
C(publication_year, Treatment(2020))[T.2016.0] −0.0279 0.002 −14.181 0.000 −0.032 −0.024
C(publication_year, Treatment(2020))[T.2017.0] −0.0262 0.002 −13.631 0.000 −0.030 −0.022
C(publication_year, Treatment(2020))[T.2018.0] −0.0228 0.002 −11.885 0.000 −0.027 −0.019
C(publication_year, Treatment(2020))[T.2019.0] −0.0124 0.002 −6.694 0.000 −0.016 −0.009
C(top_j, Treatment(‘OTHER’))[T.Arch_Virol] 0.0082 0.006 1.266 0.206 −0.004 0.021
C(top_j, Treatment(‘OTHER’))[T.ChemRxiv] 0.0018 0.005 0.347 0.729 −0.009 0.012
C(top_j, Treatment(‘OTHER’))[T.Emerg_Infect_Dis] −0.0087 0.005 −1.808 0.071 −0.018 0.001
C(top_j, Treatment(‘OTHER’))[T.JAMA] −0.0297 0.006 −4.730 0.000 −0.042 −0.017
C(top_j, Treatment(‘OTHER’))[T.JMIR_Preprints] 0.0040 0.006 0.708 0.479 −0.007 0.015
C(top_j, Treatment(‘OTHER’))[T.Journal_of_virology] 0.0050 0.005 1.067 0.286 −0.004 0.014
C(top_j, Treatment(‘OTHER’))[T.Nature] 0.0403 0.005 7.789 0.000 0.030 0.050
C(top_j, Treatment(‘OTHER’))[T.PLoS_One] −0.0151 0.003 −4.651 0.000 −0.021 −0.009
C(top_j, Treatment(‘OTHER’))[T.Research_Square] 0.0031 0.003 0.983 0.326 −0.003 0.009
C(top_j, Treatment(‘OTHER’))[T.SSRN_Electronic_Journal] −0.0009 0.003 −0.340 0.734 −0.006 0.004
C(top_j, Treatment(‘OTHER’))[T.Sci_Rep] −0.0106 0.005 −2.079 0.038 −0.021 −0.001
C(top_j, Treatment(‘OTHER’))[T.Science] 0.0236 0.005 4.481 0.000 0.013 0.034
C(top_j, Treatment(‘OTHER’))[T.Surgical_endoscopy] −0.0055 0.004 −1.249 0.212 −0.014 0.003
C(top_j, Treatment(‘OTHER’))[T.The_BMJ] 0.0019 0.005 0.375 0.707 −0.008 0.012
C(top_j, Treatment(‘OTHER’))[T.The_Lancet] 0.0825 0.006 13.939 0.000 0.071 0.094
C(top_j, Treatment(‘OTHER’))[T.Vaccine] −0.0183 0.006 −3.204 0.001 −0.029 −0.007
C(top_j, Treatment(‘OTHER’))[T.Virology] −0.0051 0.005 −0.945 0.345 −0.016 0.006
C(top_j, Treatment(‘OTHER’))[T.Viruses] 0.0235 0.005 4.710 0.000 0.014 0.033
C(top_j, Treatment(‘OTHER’))[T.bioRxiv] −0.0053 0.003 −1.602 0.109 −0.012 0.001
C(top_j, Treatment(‘OTHER’))[T.medRxiv] −0.0183 0.002 −8.482 0.000 −0.022 −0.014
times_cited 0.0099 0.000 25.497 0.000 0.009 0.011
counts_mendeley 0.0096 0.000 31.320 0.000 0.009 0.010
counts_policy 0.0612 0.002 29.233 0.000 0.057 0.065
counts_twitter_unique 0.0006 0.000 1.504 0.133 −0.000 0.001
counts_blogs_news 0.0298 0.001 42.103 0.000 0.028 0.031
counts_facebook 0.0232 0.001 19.815 0.000 0.021 0.026
expert_ratio −0.0145 0.002 −8.933 0.000 −0.018 −0.011
tm_coronaviruses −0.0055 0.014 −0.399 0.690 −0.032 0.021
tm_epidemics 0.0111 0.013 0.833 0.405 −0.015 0.037
tm_ph −0.0025 0.014 −0.183 0.855 −0.029 0.024
tm_mbi 0.0048 0.013 0.362 0.717 −0.021 0.031
tm_clinical_medicine −0.0145 0.013 −1.089 0.276 −0.041 0.012
spectre_cluster_size 0.0006 0.001 0.428 0.669 −0.002 0.003
network_cluster_size −0.0005 0.000 −3.816 0.000 −0.001 −0.000
Model:OLS regression 2020
Method:OLS
No. Observations:130,809
R-squ.:0.115
variablecoefstd errtP > |t|[0.0250.975]
Intercept −0.0056 0.017 −0.326 0.745 −0.039 0.028
C(publication_year, Treatment(2020))[T.2000.0] −0.0201 0.009 −2.312 0.021 −0.037 −0.003
C(publication_year, Treatment(2020))[T.2001.0] −0.0262 0.008 −3.206 0.001 −0.042 −0.010
C(publication_year, Treatment(2020))[T.2002.0] −0.0105 0.005 −2.078 0.038 −0.020 −0.001
C(publication_year, Treatment(2020))[T.2003.0] −0.0117 0.004 −3.003 0.003 −0.019 −0.004
C(publication_year, Treatment(2020))[T.2004.0] −0.0058 0.003 −1.775 0.076 −0.012 0.001
C(publication_year, Treatment(2020))[T.2005.0] −0.0017 0.003 −0.532 0.594 −0.008 0.005
C(publication_year, Treatment(2020))[T.2006.0] −0.0043 0.003 −1.361 0.173 −0.010 0.002
C(publication_year, Treatment(2020))[T.2007.0] −0.0041 0.003 −1.296 0.195 −0.010 0.002
C(publication_year, Treatment(2020))[T.2008.0] −0.0044 0.003 −1.655 0.098 −0.010 0.001
C(publication_year, Treatment(2020))[T.2009.0] −0.0112 0.002 −4.486 0.000 −0.016 −0.006
C(publication_year, Treatment(2020))[T.2010.0] −0.0110 0.003 −4.380 0.000 −0.016 −0.006
C(publication_year, Treatment(2020))[T.2011.0] −0.0155 0.002 −6.441 0.000 −0.020 −0.011
C(publication_year, Treatment(2020))[T.2012.0] −0.0134 0.002 −5.809 0.000 −0.018 −0.009
C(publication_year, Treatment(2020))[T.2013.0] −0.0220 0.002 −10.165 0.000 −0.026 −0.018
C(publication_year, Treatment(2020))[T.2014.0] −0.0252 0.002 −12.212 0.000 −0.029 −0.021
C(publication_year, Treatment(2020))[T.2015.0] −0.0287 0.002 −14.416 0.000 −0.033 −0.025
C(publication_year, Treatment(2020))[T.2016.0] −0.0279 0.002 −14.181 0.000 −0.032 −0.024
C(publication_year, Treatment(2020))[T.2017.0] −0.0262 0.002 −13.631 0.000 −0.030 −0.022
C(publication_year, Treatment(2020))[T.2018.0] −0.0228 0.002 −11.885 0.000 −0.027 −0.019
C(publication_year, Treatment(2020))[T.2019.0] −0.0124 0.002 −6.694 0.000 −0.016 −0.009
C(top_j, Treatment(‘OTHER’))[T.Arch_Virol] 0.0082 0.006 1.266 0.206 −0.004 0.021
C(top_j, Treatment(‘OTHER’))[T.ChemRxiv] 0.0018 0.005 0.347 0.729 −0.009 0.012
C(top_j, Treatment(‘OTHER’))[T.Emerg_Infect_Dis] −0.0087 0.005 −1.808 0.071 −0.018 0.001
C(top_j, Treatment(‘OTHER’))[T.JAMA] −0.0297 0.006 −4.730 0.000 −0.042 −0.017
C(top_j, Treatment(‘OTHER’))[T.JMIR_Preprints] 0.0040 0.006 0.708 0.479 −0.007 0.015
C(top_j, Treatment(‘OTHER’))[T.Journal_of_virology] 0.0050 0.005 1.067 0.286 −0.004 0.014
C(top_j, Treatment(‘OTHER’))[T.Nature] 0.0403 0.005 7.789 0.000 0.030 0.050
C(top_j, Treatment(‘OTHER’))[T.PLoS_One] −0.0151 0.003 −4.651 0.000 −0.021 −0.009
C(top_j, Treatment(‘OTHER’))[T.Research_Square] 0.0031 0.003 0.983 0.326 −0.003 0.009
C(top_j, Treatment(‘OTHER’))[T.SSRN_Electronic_Journal] −0.0009 0.003 −0.340 0.734 −0.006 0.004
C(top_j, Treatment(‘OTHER’))[T.Sci_Rep] −0.0106 0.005 −2.079 0.038 −0.021 −0.001
C(top_j, Treatment(‘OTHER’))[T.Science] 0.0236 0.005 4.481 0.000 0.013 0.034
C(top_j, Treatment(‘OTHER’))[T.Surgical_endoscopy] −0.0055 0.004 −1.249 0.212 −0.014 0.003
C(top_j, Treatment(‘OTHER’))[T.The_BMJ] 0.0019 0.005 0.375 0.707 −0.008 0.012
C(top_j, Treatment(‘OTHER’))[T.The_Lancet] 0.0825 0.006 13.939 0.000 0.071 0.094
C(top_j, Treatment(‘OTHER’))[T.Vaccine] −0.0183 0.006 −3.204 0.001 −0.029 −0.007
C(top_j, Treatment(‘OTHER’))[T.Virology] −0.0051 0.005 −0.945 0.345 −0.016 0.006
C(top_j, Treatment(‘OTHER’))[T.Viruses] 0.0235 0.005 4.710 0.000 0.014 0.033
C(top_j, Treatment(‘OTHER’))[T.bioRxiv] −0.0053 0.003 −1.602 0.109 −0.012 0.001
C(top_j, Treatment(‘OTHER’))[T.medRxiv] −0.0183 0.002 −8.482 0.000 −0.022 −0.014
times_cited 0.0099 0.000 25.497 0.000 0.009 0.011
counts_mendeley 0.0096 0.000 31.320 0.000 0.009 0.010
counts_policy 0.0612 0.002 29.233 0.000 0.057 0.065
counts_twitter_unique 0.0006 0.000 1.504 0.133 −0.000 0.001
counts_blogs_news 0.0298 0.001 42.103 0.000 0.028 0.031
counts_facebook 0.0232 0.001 19.815 0.000 0.021 0.026
expert_ratio −0.0145 0.002 −8.933 0.000 −0.018 −0.011
tm_coronaviruses −0.0055 0.014 −0.399 0.690 −0.032 0.021
tm_epidemics 0.0111 0.013 0.833 0.405 −0.015 0.037
tm_ph −0.0025 0.014 −0.183 0.855 −0.029 0.024
tm_mbi 0.0048 0.013 0.362 0.717 −0.021 0.031
tm_clinical_medicine −0.0145 0.013 −1.089 0.276 −0.041 0.012
spectre_cluster_size 0.0006 0.001 0.428 0.669 −0.002 0.003
network_cluster_size −0.0005 0.000 −3.816 0.000 −0.001 −0.000

## Author notes

Handling Editor: Staša Milojević

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