JMIR Publications

ABSTRACT

Background:

At the end of February 2020, the virus spread rapidly in South Korea, following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current Coronavirus pandemic have often been criticized, little systematic research has yet 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 the Coronavirus on Twitter in Korea. Overcoming the crisis requires diverse forms of data and more complex models. 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 Coronavirus outbreak.

Methods:

Korea’s Coronavirus Twitter data were collected on February 29, 2020. Our final sample comprised 43,832 users and 78,233 conversations. We generated four networks in terms of key issues regarding the Coronavirus in Korea. This study comparatively investigates how Coronavirus-related issues circulated on Twitter by conducting network analysis. Next, we classify top news channels shared in tweets. Lastly, we conducted content analysis of news frames used in the top shared sources.

Results:

The network analysis (78,233 conversations generated by 43,832 users) suggest 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 is the lowest than did those who used other terms. Many news items appeared highlighting the positive role of individuals and groups, directing readers’ attention to the epidemic crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity’s donation also often emerged. There was significant difference in using non-portal (n = 14) or portal news (n = 26) sites between the four network types (N= 40). News frames used in top sources were similar across the networks (p = .89). Tweets containing medically framed news articles were found to be more popular than tweets that included news articles adopting non-medical frames (N = 40, p = .03).

Conclusions:

We find that most of the popular news on the Twitter had non-medical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about Coronavirus was greater than that of news with non-medical frames. While social media network analytics cannot replace the work of public health officials, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes. Clinical Trial: As this study did not conduct an experiment, an RCT is not available.