On December 31, 2019, the World Health Organization (WHO) was alerted to the occurrence of a series of cases of pneumonia with unknown etiology in Wuhan City, China, which were subsequently linked to exposures at a seafood and live animal market (World Health Organization, 2020) . Chinese researchers identified the cause of the disease to be a new type of coronavirus (Ren et al., 2020) , later named COVID-19 by the WHO(World Health Organization, 2020). Between January and mid-March, the disease spread from its epicenter to other Chinese CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 7, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 for Disease Control and Prevention, 2018 Edition) . In the US, people seek and receive news information from numerous different sources, including newspapers, radio, television, and increasingly, social media (Shearer, 2018, December 10) . During outbreaks of novel infectious diseases, an understanding of the disease builds with time and thus initial knowledge gaps may exist among scientists, medical and public health professionals. These gaps are fertile grounds for spread of misinformation in news media sources (Lee & Basnyat, 2013) . For traditional media, journalists may misreport information, from misunderstanding scientific facts, receiving wrong information, or through sensationalized reporting (Larsson et al., 2003) . For social media, users can participate semi-or fully anonymously and spread false information without fear of repercussions (Kling et al., 1999) . The prevalence of misinformation could itself be a source of risk in pandemic situations (Smallman, 2015) , with numerous options for news sources presenting a challenge for communication. Further, topics emphasized or underemphasized by media potentially pose risks to the public; for example, the media may lend credibility to unproven cures, or underreport ways to prevent spread of the disease. A question arises as to whether more effective strategies with news media would help to achieve public health objectives towards prevention and control in pandemic situations like COVID-19.
A variety of studies have previously examined media coverage of pandemics. A content analysis of British media coverage of SARS in 2003, concluded that media tended to characterize SARS as of Chinese origin, and conveyed that the superiority of "Western scientific biomedicine" would contain its spread (Washer, 2004 ). An investigation of how the H1N1 pandemic was portrayed found corporate organizations including the National Pork Producers
Council and National Pork Board adopted a more reassuring tone in response to the crisis than governmental organizations such as the Centers for Disease Control and Prevention (CDC) and . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted July 7, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 the Department of Health and Human Services (Liu & Kim, 2011) . Other researchers faulted the WHO and CDC's response to the H1N1 pandemic for enabling stigmatization of "Mexicans and other Latinos in the US … as carriers of the virus" (McCauley et al., 2013) . A mixed methods study of Dutch media coverage of H1N1 implicated both media and expert sources for overstating the threat of the virus (Vasterman & Ruigrok, 2013) . The majority of existing research on media coverage of pandemics to date have relied on qualitative methods for analysis.
As the volume of information accumulating on the internet on news topics expands, more sophisticated methods of analyses are needed.
The value of machine learning techniques such as Latent Dirichlet Allocation (LDA) in understanding health-related discussions on social media has been previously demonstrated. Data posted on social media has been used to aid disease surveillance during the 2011 Escherichia coli outbreak in Germany (Diaz-Aviles et al., 2012) , the 2010 influenza epidemic in the US (Achrekar et al., 2011) , and the 2009 H1N1 pandemic (Al-garadi et al., 2016) . Topic analysis has been employed to discover major health and disease topics of interest discussed on Twitter (Paul & Dredze, 2014) . Further, in online healthcare communities, users generally post about symptoms, exams, medications, procedures, and complications relating to critical illnesses (Lu et al., 2013) . While previous research shows the growing importance of social media in responding to emerging health crises, studies are needed on the nature of pandemic-related information. The flow of information on social media originates initially within smaller, specific subcommunities before spreading to a wider audience on the internet (Weninger et al., 2013) . Coverage of pandemics is not restricted to health-related information like symptoms and treatments (Vasterman & Ruigrok, 2013) . Furthermore, social media has become pervasive in everyday life, giving a voice to people on important issues.
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The copyright holder for this preprint this version posted July 7, 2020. . https://doi.org/10.1101/2020.07.05.20146894 doi: medRxiv preprint COVID-19 may be the most disruptive international health issue in modern times, and is dominating news media. This paper explores the nature of COVID-19 coverage in traditional news and social media. Our objectives are first, to characterize the nature of information received by news consumers of newspapers, television, and social media using Latent Dirichlet Allocation (LDA) without making any a priori hypotheses, in order to discover topics associated with COVID-19 on different platforms. Our second objective is to compare topic configurations across platforms to examine potential differences. Finally, based on our observations, we discuss implications for communication strategies by public health entities that are relevant to an initial pandemic response.
LDA, an unsupervised machine learning technique (Blei et al., 2003) , is an exploratory algorithm useful for discovering underlying topics within large bodies of text commonly referred to as a corpus. LDA is a generative probabilistic model in that it simulates the random process by which a given document within the corpus could have been generated (Blei, 2012) . This inductive approach identifies topics that one might not anticipate. LDA has been shown to perform better than other topic modeling techniques in health-related text mining (Sarioglu et al., 2012) . The goal of LDA is to compute the posterior probability given evidence, i.e. the conditional distribution of topics given documents within the corpus (Blei, 2012) . Calculating this posterior probability requires computation of the joint probability distribution of β, θ, and z across all w ( Figure 1 ) and dividing it by the probability of observing the corpus across all possible topic models. Algorithmically, the procedure begins with random guesses of β and θ and a pre-specified number of topics K. Each word in a document is randomly assigned to a topic, . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 7, 2020. . https://doi.org/10.1101/2020.07.05.20146894 doi: medRxiv preprint and this process repeats conditioned on the current topic distribution. A word is reassigned to another topic if the topic rarely appears within the document, or if the word rarely appears in the current topic. The algorithm converges when there are no new reassignments, or when the number of iterations is reached, resulting in a per document topic distribution and per topic word distribution for a corpus.
[ Figure 1 near here]
Data from newspaper, television, and social media sources (Table 1) were obtained for the nine-day period of March 4 -12, 2020, selected for being early in the pandemic. We employed searches with keywords "coronavirus" and "COVID" in bodies of Twitter posts, newspaper articles, and television transcripts. The same keyword searches were used for titles in Reddit submissions, specifically the r/all subreddit which aggregates the most popular submissions across the Reddit community. Raw data were collected and saved in text files for analyses ( Figure 2 ).
Topic analysis requires preprocessing of raw data to a specific format (Zhao et al., 2011) .
We used the process of lemmatization to convert individual words to their root word. Stopwords which occur very frequently in the English language bring noise into the topic discovery process, so were similarly removed. We employed term frequency-inverse document frequency (TF-IDF) to derive the most important words for each corpus (Ramos, 2003) . TF-IDF is a term weighting scheme that allocates higher importance for a word appearing frequently within a document, while controlling for the word's appearance across all documents.
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Consistent with the spirit of LDA (Blei, 2012) , we prioritized human interpretability of resultant topics and settled on K=3, to ensure adequate coverage of interpretable topics across traditional and social media (DiMaggio et al., 2013) . Having retrieved the top words defining each of the topics across corpora, we scored individual components of each corpus against generated topics, and ranked them to identify which topics defined each component. Two authors (WC & NMG) derived labels based on content loading highest on individual topics, and then discussed to resolve any differences. This methodological approach is consistent with previous research employing topic modeling(DiMaggio et al., 2013) . IRB approval was not obtained as the research did not involve human subjects.
The top words defining coverage of COVID-19 across traditional and social media over the selected time period were case, trump, travel, virus, china, world, test, health, and public.
Topics discovered varied based on the category of the media source (Table 2 ) and individual articles were frequently reflected in more than one topic. For example, an article about a football team cancelling an autograph session citing fears of the virus loaded on the global topic (which included the sports term) in the newspaper corpus, but was also a mixture of the epidemic and economy topics.
[ Table 2 near here]
The "epidemic" topic was comprised of terms relating to the disease outbreak and its
spread. An article in the Wall Street Journal which loaded on this topic reported that at least 100,000 people worldwide were infected with the virus, and estimated a case fatality rate of 2-
4%. An Associated Press article explained how to distinguish between flu and COVID-19
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The copyright holder for this preprint this version posted July 7, 2020. . https://doi.org/10.1101/2020.07.05.20146894 doi: medRxiv preprint symptoms. Citing a medical doctor and the WHO, it explained that while flu symptoms (e.g. high fever, extreme exhaustion) manifest suddenly, symptoms for COVID-19 appear more gradually and include dry coughs and shortness of breath. Another article loading highly on this topic from the New York Times advocated parents not inform their 6-year-old children about the virus to prevent unnecessary anxiety.
The "economy" topic illustrated economic effects of the pandemic including reduced revenues for various companies due to lower economic activities, bankruptcy of airlines, and efforts of US legislators to increase funding. The third topic demonstrated the global impact of the pandemic with content in articles underscoring effects of COVID-19 beyond the US. As examples, the South Korean government opposition to Japan's decision to quarantine South Korean visitors; a France-Ireland rugby match was postponed to prevent coronavirus from spreading; and a European and Russian joint mission to launch a rover to Mars was postponed because of COVID-19-related travel restrictions.
The "testing" topic included coverage of the cruise ship off the California coast, and the Vice President's announcement that passengers would be tested and quarantined if necessary. On multiple programs, cable news personalities advocated the need for more coronavirus testing in the US, and South Korea was noted as an example of how to aggressively test to understand the scope of the outbreak. One commentator noted the country's overall mitigation strategies, including the number of tests conducted each day and different treatments being tried including . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The "politics" topic included coverage highlighting four members of the US Congress who had self-quarantined after possible contact with COVID-19 positive individuals (All In With Chris Hayes, 3/9/20). Some hosts critiqued China's actions in responding to the outbreak, with one claiming that it might "shut off the supply of pharmaceuticals" to the US (Tucker Carlson Tonight, 3/11/20). The same host also criticized the WHO recommendation to refer to the virus by its scientific name, insisting that it should be referred to by its place of origin. The third topic underscored the severe effects of the pandemic. When commentators talked about the virus, they also tended to talk about home and work life. Some linked the virus to disruptions of the supply chain, school closures, increased unemployment, and increased need of telemedicine and working from home. Discussions centered around the prospect of the healthcare system being overwhelmed by the number of patients suffering from COVID-19 disease. Discourse also brought attention to the global economic devastation wrought by the pandemic. The possibility that the impact on the US economy would have implications on the 2020 presidential election was also highlighted.
Based on terms contained in the first topic, associated tweets emphasized how politics and public policy should address the pandemic. Representative tweets which loaded highly on this topic included "Once a vaccine for coronavirus is developed it should be free." The second topic underscored the global nature of the crisis. Representative tweets included from the WHO:
"Of the 118000 #COVID19 cases reported globally in 114 countries more than 90 percent of cases are in just four countries…" The third topic emphasized the need for testing and the backlash to actions required to slow spread of the virus. Tweets included "Severe shortage of tests blunts coronavirus response Boston doctors say," and "The global pandemic of our time isn't the Coronavirus. The global pandemic of our time is FEAR". This topic demonstrates . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 7, 2020. . https://doi.org/10.1101/2020.07.05.20146894 doi: medRxiv preprint concerns regarding lack of testing capacity in the US, and shows that a significant number of people worried that authorities were overreacting while themselves not taking the pandemic seriously.
Based on terms defining the first topic, posts reflected information-seeking behavior on the part of users wanting to know more about COVID-19. Comments were in response to posts with titles such as "My father is 76 and still works as a doctor... How can my mom and me convince him to stop working?" and "Is corona virus like chicken pox…you get it once and done or can you get it over and over again?" There was substantial discussion on whether fatalities varied by age; the consensus was that for young children and healthy adults, the disease was not "that dangerous."
The second topic emphasized the impact of COVID-19 on everyday life, with references to home, work, and school life. Comments loading highly on this topic alerted of the possibility of school closures, working from home, and also staying indoors for an indefinite period. Other users referenced restaurants limiting their hours of service, staying home with children, and hoarding of supplies like food and toilet paper. The third topic articulated serious consequences of COVID-19 with references to severity of illness among patients, sports leagues in various countries canceling their seasons, and people losing jobs and incomes. Some users referenced plans for freezing mortgage and rent payments. Others were not convinced that the US government's response to the pandemic was adequate.
Our analysis of media sources during March 4-12, 2020 showed that major discovered topics were not predominantly health-related, which would indicate less of a presence of public health entities in news coverage and social media. While there was substantial mention of the . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 7, 2020. . https://doi.org/10.1101/2020.07.05.20146894 doi: medRxiv preprint presidential administration, references to ventilators, social distancing, and hygiene were more limited. This lack of a prominent public health "voice" represents a missed opportunity for public health and medical experts to establish themselves as trustworthy and credible sources for information and instructions. The nine-day period selected for analysis was relatively early in the US coronavirus pandemic timeline. During this period there were 149 confirmed cases which increased to 1,663, and eleven deaths which increased to 40 (Johns Hopkins University & Medicine, 2020). (For comparison, ten days later on March 22 there were 33,276 confirmed cases and 417 deaths.) Significant news events during the nine-day period included the CDC's removal of previous restrictions on coronavirus testing, and the declaration of COVID-19 a pandemic by WHO(CNN Editorial Research, 2020) . The latter also marked the US announcement of new travel restrictions from Europe. In retrospect, the need for an authoritative voice of health to emerge during this early period was still very much unfilled. An early deficient public health presence creates opportunities for other less informed voices to dominate, or worse, to disseminate misinformation (Mian & Khan, 2020; Pan American Health Organization, 2009; World Health Organization, 2017) . This was seen in cable news content where hosts downplayed the risk of infection. A minimized presence of public health in the media could consequently be detrimental to the public being provided with educational information important to reduce spread of the virus. A limitation of our approach is that we selected a specific time frame to conduct our analyses, which was relatively early in the pandemic. Continued analysis of media coverage of the pandemic could suggest additional interpretations. Further, to understand coverage on social media, we analyzed Twitter and Reddit content; future research could explore coverage on other platforms such as Facebook and Instagram.
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The copyright holder for this preprint this version posted July 7, 2020. The perceived unknown and uncontrollable nature of the novel coronavirus and potential for severe disease resulting from its infection would place the pandemic in the low familiarity/high dread of Slovic's psychometric risk paradigm (2000) . This serves to indicate some of the challenges (Vaughan & Tinker, 2009 ) underlying risk communication. The public's perception of risk may be shaped early in a pandemic and may be difficult to change once formed (Sheppard et al., 2012) . Together these emphasize the importance for public health entities to seize early communication opportunities and follow a well-conceived communication plan. Initial communication from public health during a novel infectious disease outbreak should acknowledge the uncertainty involved with the newly identified agent and prepare the public for . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 7, 2020. . https://doi.org/10.1101/2020.07.05.20146894 doi: medRxiv preprint situations in which additional information will be forthcoming or instructions could change over time (Vaughan & Tinker, 2009 ). CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted July 7, 2020 . . https://doi.org/10.1101 /2020 particularly since social media is increasingly where information is obtained by the public and circulated. An advantage to this approach is that public health entities have more control over content posted on their social media sites compared with news media sourcing information for their own coverage. However, a mere presence on these platforms is not enough; it is important to package public health information in appealing ways. Partnerships with social media personalities adept at creating viral information could achieve this.
Communication of important health information during times of communicable disease pandemics is crucial to informing and educating the public (Naik et al., 2019 ; US Department of Health and Human Services Centers for Disease Control and Prevention, 2018 Edition).
Particularly critical for public health efforts to control an outbreak and prevent additional cases is communication during initial stages of an epidemic as disease enters a population and begins to spread. Inherent challenges occur with communicating information during these initial stages as much about the agent (i.e., incubation period, routes of transmission) and disease (treatment approaches, severity) may be unknown, as was the case with COVID-19 caused by the novel coronavirus. As more is learned and knowledge grows while a pandemic unfolds, it is anticipated that health entities will provide additional information to the public and correct previously disseminated information as necessary (Vaughan & Tinker, 2009; World Health Organization, 2017 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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The authors report no conflicts of interest.
Authors contributed to this work as follows: formulating the research question(s): WC, EA, TG, NMG; designing the study: WC, EA, TG; carrying it out: WC, EA; analyzing the data: WC, EA; writing the article: WC, EA, TG, NMG
The data that support the findings of this study are available from the corresponding author (NG) upon reasonable request.
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The copyright holder for this preprint this version posted July 7, 2020. . https://doi.org/10.1101/2020.07.05.20146894 doi: medRxiv preprint *words appearing across all corpora indicated using underlined font . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
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