title: 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.
authors.affiliation: 1 Administration of Justice, Pennsylvania State University , Schuylkill Haven, Pennsylvania.
authors.forename: Juyoung
authors.initials: J
authors.lastname: Song
authors.affiliation: 2 Research Center on Big Data, Korea Institute for Health and Social Affairs , Seoul, South Korea .
authors.forename: Tae Min
authors.initials: TM
authors.lastname: Song
authors.affiliation: 3 Department of Applied Health Science, Indiana University School of Public Health , Bloomington, Indiana.
authors.forename: Dong-Chul
authors.initials: DC
authors.lastname: Seo
authors.affiliation: 2 Research Center on Big Data, Korea Institute for Health and Social Affairs , Seoul, South Korea .
authors.forename: Dal-Lae
authors.initials: DL
authors.lastname: Jin
authors.affiliation: 4 SK Telecom Smart Insight , Seoul, South Korea .
authors.forename: Jung Sun
authors.initials: JS
authors.lastname: Kim
mesh_terms: D018352:Coronavirus Infections / Q000453:epidemiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D006801:Humans; D033181:Information Dissemination / Q000379:methods*; D065207:Middle East Respiratory Syndrome Coronavirus; D056910:Republic of Korea / Q000453:epidemiology; D061108:Social Media
keywords: MERS; social big data; social networking