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Understanding the uncertainty of disaster tweets and its effect on retweeting: The perspectives of uncertainty reduction theory and information entropy
Author(s) -
Son Jaebong,
Lee Jintae,
Larsen Kai R.,
Woo Jiyoung
Publication year - 2020
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24329
Subject(s) - entropy (arrow of time) , computer science , information theory , data science , disaster response , emergency management , mathematics , statistics , physics , quantum mechanics , political science , law
The rapid and wide dissemination of up‐to‐date, localized information is a central issue during disasters. Being attributed to the original 140‐character length, Twitter provides its users with quick‐posting and easy‐forwarding features that facilitate the timely dissemination of warnings and alerts. However, a concern arises with respect to the terseness of tweets that restricts the amount of information conveyed in a tweet and thus increases a tweetʼs uncertainty. We tackle such concerns by proposing entropy as a measure for a tweetʼs uncertainty. Based on the perspectives of Uncertainty Reduction Theory (URT), we theorize that the more uncertain information of a disaster tweet, the higher the entropy, which will lead to a lower retweet count. By leveraging the statistical and predictive analyses, we provide evidence supporting that entropy validly and reliably assesses the uncertainty of a tweet. This study contributes to improving our understanding of information propagation on Twitter during disasters. Academically, we offer a new variable of entropy to measure a tweetʼs uncertainty, an important factor influencing disaster tweetsʼ retweeting. Entropy plays a critical role to better comprehend URLs and emoticons as a means to convey information. Practically, this research suggests a set of guidelines for effectively crafting disaster messages on Twitter.