Social Big Data Analysis of Information Spread and Perceived Infection Risk During the 2015 Middle East Respiratory Syndrome Outbreak in South Korea

Abstract

We investigated online diffusion of information, spread of fear, and perceived risk of infection to Middle East Respiratory Syndrome (MERS) as cases of MERS spread rapidly and dozens of fatalities occurred in South Korea in May-June of 2015. This study retrieved 8,671,695 MERS-related online documents from May 20 to June 18, 2015, from 171 Korean online channels and analyzed such documents by using multilevel models and data mining with Apriori algorithm association analysis. We used R software (version 3.2.1) for the association analysis data mining and visualization. Buzz with negative emotions (i.e., anxiety or fear) was more prevalent in online discussion boards, Twitter, and online cafes than news sites and blogs. News buzz (b = 0.21, p < 0.001), but not rumor buzz (b = 0.06, p = 0.308), was associated with positive MERS emotions (i.e., being calm or composed). The mention of eating immunity-boosting food in the news led to a 94 percent chance of a positive MERS emotion and that such a chance of showing a positive emotion was 4.75 times higher than that without such a mention (support of 0.001, confidence of 0.94, and lift of 4.75). Even with the same precautionary messages that were disseminated, they yielded the opposite emotional reactions to people depending on the channel through which the messages were communicated. In the face of a novel and highly contagious disease such as MERS, the government must deploy a response system that includes provision and dissemination of reliable information and inhibits online diffusion of false information.

The Effects of Social Media Use on Preventive Behaviors during Infectious Disease Outbreaks: The Mediating Role of Self-relevant Emotions and Public Risk Perception

“…In such a situation, an infectious disease outbreak can trigger the ordinary public's expression of their concerns about the outbreak, particularly through social media (Ofoghi, Mann, & Verspoor, 2016). For example, self-relevant emotions such as fear and anger were prevalent when people talked about the 2015 MERS outbreak on social media (Song, Song, Seo, Jin, & Kim, 2017). A recent study revealed that fear and anger were consistently expressed in tweets during the Ebola outbreak (Ofoghi et al, 2016).…”

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic

“…Concerning another coronavirus, Song and coworkers [18] investigated online diffusion of information, spread of fear, and perceived risk of contracting MERS infection during the MERS outbreak in South Korea in May-June of 2015. Buzz conveying negative feelings was found to be more prevalent in online discussion boards, Twitter, and online cafes than news sites and blogs.…”

The impact of social media on risk perceptions during the MERS outbreak in South Korea

“…Recent studies have used big data analytics to demonstrate that people mentioned and/or shared factual information related to MERS, such as symptoms and prevention methods, in online media platforms, including social media (Song, 2015). Moreover, negative emotions concerning the disease, such as anxiety or fear, were more prevalent than positive emotions in social media during the infectious outbreak (Song, Song, Seo, Jin, & Kim, 2017).…”