Background SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations. Objective Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak. Methods Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources. Results The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word “Coronavirus” communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers’ attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852). Conclusions Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

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In this study, we defined a Twitter network as an information channel that includes information sources containing embedded messages. We conducted stage-based comparative analyses of Twitter networks during three periods: the beginning of the COVID-19 epidemic, the period when the epidemic was becoming a global phenomenon, and the beginning of the pandemic. We also analyzed the characteristics of scientific information sources and content on Twitter during the sample period. At the beginning of the epidemic, Twitter users largely shared trustworthy news information sources about the novel coronavirus. Widely shared scientific information focused on clinical investigations and case studies of the new coronavirus as the disease became a pandemic while non-scientific information sources and messages illustrated the social and political aspects of the global outbreak, often including emotional elements. Multiple suspicious, bot-like Twitter accounts were identified as a great connector of the COVID-19 Twitterverse, particularly in the beginning of the global crisis. Our findings suggest that the information carriers, which are information channels, sources, and messages were coherently interlocked, forming an information organism. The study results can help public health organizations design communication strategies, which often require prompt decision-making to manage urgent needs under the circumstances of an epidemic.

Social media includes a copious amount of sentimentembodied sentences. Sentiment is described as "a personal belief or judgment that is not founded on proof or certainty," which may depict the emotional state of the user, such as happy, glad, terrified, miserable, or the author's viewpoint on a topic. In social science, emotions and sentiment make up a significant part of social life and are interconnected with social relationships. When experiencing emotions, people want to reveal those emotions to other people. This study seeks to validate whether the Emotional Contagion social theory holds true in microblogging data. This theory implies that related people tend to have more similar sentiments or opinions. Motivated by this sociological observation, the study explores the sentiment-semantics of the Twitter network of #prayforparis through sentiment analysis and topic extraction. Social Network Analysis was conducted using NodeXL to investigate the research questions. The study implemented R for conducting sentiment analysis and generating word clouds with the collected data. The study also conducted content analysis of tweets through topic extraction by applying the most recent version of SAS Enterprise Miner (13.2). In conclusion, the results confirmed the Emotional Contagion Theory in the Twitter network of #prayforparis.

After Russia’s malicious attempts to influence the 2016 presidential election were revealed, “fake news” gained notoriety and became a popular term in political discourses and related research areas. Empirical research about fake news in diverse settings is in the beginning phase while research has revealed limitedly that “what we know about fake news so far is predominantly based on anecdotal evidence.” The purpose of this study is to investigate fake news included in politically opposing hashtag activism, #Gunreformnow and #NRA (The National Rifle Association). This study attempted to lay out the process of identifying fake news in the hashtag activism network on Twitter as a two-step process: 1) hashtag frequency analysis, top word-pair analysis, and social network analysis and 2) qualitative content analysis. This study discovered several frames through a qualitative approach. One of the prominent fake news frames was intentionally misleading information that attacks the opposing political party and its advocators. The disinformation tweets overall presented far-right wing ideologies and included multiple hashtags and a YouTube video to promote and distribute their agendas while calling for coalition of far-right wing supporters. However, the fake news tweets often failed to provide a reliable source to back up credibility of the content.