Paper Type
ERF
Abstract
Twitter has become one of the widely used crisis communication tools. In the recent coronavirus outbreak, social media users exchange millions of messages ranging from crisis response to awareness creating messages. We conduct an exploratory analysis using tweets collected over initial one month of its outbreak to unveil useful trends and gain insights into managing crisis. Our findings from the analysis present that both alarming and reassuring messages surface during crisis situations, however, it is the reassurance messages that build confidence in individuals to overcome the distress that an epidemic creates. Following the guidelines of Crisis Management, we establish a linguistic based approach to explore the tweets text. Our findings provide insights that the gap between the number of alarming and reassuring messages widen as the epidemic spreads. Our findings also show that significantly, there exists a cluster of similar messages among the deluge of information. Identifying these clusters help manage the crisis situation.
Recommended Citation
Vemprala, Naga; Akello, Patricia; Valecha, Rohit; and Rao, H. Raghav, "An Exploratory Analysis of Alarming and Reassuring Messages in Twitterverse during the Coronavirus Epidemic" (2020). AMCIS 2020 Proceedings. 4.
https://aisel.aisnet.org/amcis2020/social_computing/social_computing/4
An Exploratory Analysis of Alarming and Reassuring Messages in Twitterverse during the Coronavirus Epidemic
Twitter has become one of the widely used crisis communication tools. In the recent coronavirus outbreak, social media users exchange millions of messages ranging from crisis response to awareness creating messages. We conduct an exploratory analysis using tweets collected over initial one month of its outbreak to unveil useful trends and gain insights into managing crisis. Our findings from the analysis present that both alarming and reassuring messages surface during crisis situations, however, it is the reassurance messages that build confidence in individuals to overcome the distress that an epidemic creates. Following the guidelines of Crisis Management, we establish a linguistic based approach to explore the tweets text. Our findings provide insights that the gap between the number of alarming and reassuring messages widen as the epidemic spreads. Our findings also show that significantly, there exists a cluster of similar messages among the deluge of information. Identifying these clusters help manage the crisis situation.
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