Twitter as a Potential Disaster Risk Reduction Tool. Part III: Evaluating Variables that Promoted Regional Twitter Use for At-risk Populations During the 2013 Hattiesburg F4 Tornado

Abstract

Introduction: Study goals attempt to identify the variables most commonly associated with successful tweeted messages and determine which variables have the most influence in promoting exponential dissemination of information (viral spreading of the message) and trending (becoming popular) in the given disaster affected region.Methods: Part II describes the detailed extraction and triangulation filtration methodological approach to acquiring twitter data for the 2013 Hattiesburg Tornado. The data was then divided into two 48 hour windows before and after the tornado impact with a 2 hour pre-tornado buffer to capture tweets just prior to impact. Criteria-based analysis was completed for Tweets and users. The top 100 pre-Tornado and post-Tornado retweeted users were compared to establish the variability among the top retweeted users during the 4 day span. Results: Pre-Tornado variables that were correlated to higher retweeted rates include total user tweets (0.324), and total times message retweeted (0.530). Post-Tornado variables that were correlated to higher retweeted rates include total hashtags in a retweet (0.538) and hashtags #Tornado (0.378) and #Hattiesburg (0.254). Overall hashtags usage significantly increased during the storm. Pre-storm there were 5,763 tweets with a hashtag and post-storm there was 13,598 using hashtags.Conclusions: Twitter’s unique features allow it to be considered a unique social media tool applicable for emergency managers and public health officials for rapid and accurate two way communication. Additionally, understanding how variables can be properly manipulated plays a key role in understanding how to use this social media platform for effective, accurate, and rapid mass information communication.

Social Media Use in Emergency Response to Natural Disasters: A Systematic Review With a Public Health Perspective

“…8,[25][26][27][28][29] The outcomes can help identify common and less frequent topics that need to be emphasized during the response to events guiding the development of communication strategies and posting time. 8,10,26,[30][31][32][33][34][35][36][37][38][39][40][41] Application of Content Analysis for Natural Disasters…”

“…26,37 Consistency in hashtag use can help with information dissemination and identification of those in need. 31,38 An essential part of the health communication cycle tailoring messages for the intended audience and adopting appropriate channels for delivery. 42 SNA allowed the recognition of the preferences in social media platforms of individuals affected by natural disasters.…”

“…44 Social media data analysis was used to identify influencers in the affected area before and after events. 38 Four articles identified individuals as the most prominent users of social media; [30][31][32][33] 3 identified news or media, nonprofit organizations, and government agencies as key accounts for information dissemination during natural disasters. 10,25,40 These influential accounts can be used as a channel to share relevant information and emergency warnings to the affected population considering the trends of information changes in social media.…”

“…64 Several research articles provided evidence of the use of social media as a broadcasting tool or 1-way communication channel. 8,26,[30][31][32][33]38,39,41 A limited number of publications researched misinformation and rumor control on social media during natural disasters. 27,28,32 Rumors and misinformation in social media can spread fast and reach a broad range of users in different locations, directly affecting decision-making and actions taken by citizens and responders.…”

“…Several research articles used SNA to determine the relationship between the social media account and those who follow them. 8,10,25,[28][29][30][31][32][34][35][36][37][38] Various authors included retweets in their investigation or did a separate analysis of retweets for their research, which differs from previously published literature. 5,8,10,[25][26][27][28][30][31][32]35,37,38,41,49,50 One of the limitations of the broader landscape of the literature, as identified in this review is the focus on Twitter data in the majority of the articles.…”