Twitter, a popular communications platform, is identified as contributing to improved mortality and morbidity outcomes resulting from the 2013 Hattiesburg, Mississippi EF-4 Tornado. This study describes the methodology by which Twitter was investigated as a potential disaster risk reduction and management tool at the community level and the process by which the at-risk population was identified from the broader Twitter user population. By understanding how various factors contribute to the superspreading of messages, one can better optimize Twitter as an essential communications and risk reduction tool. This study introduces Parts II, III and IV which further define the technological and scientific knowledge base necessary for developing future competency base curriculum and content for Twitter assisted disaster management education and training at the community level.
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Italo Subbarao
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Background: This article describes a novel triangulation methodological approach for identifying twitter activity of regional active twitter users during the 2013 Hattiesburg EF-4 Tornado.
Methodology: A data extraction and geographically centered filtration approach was utilized to generate Twitter data for 48 hrs pre- and post-Tornado. The data was further validated using six sigma approach utilizing GPS data. Results: The regional analysis revealed a total of 81,441 tweets, 10,646 Twitter users, 27,309 retweets and 2637 tweets with GPS coordinates.
Conclusions: Twitter tweet activity increased 5 fold during the response to the Hattiesburg Tornado. Retweeting activity increased 2.2 fold. Tweets with a hashtag increased 1.4 fold. Twitter was an effective disaster risk reduction tool for the Hattiesburg EF-4 Tornado 2013.
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.
Twitter can be an effective tool for disaster risk reduction but gaps in education and training exist in current public health and disaster management educational competency standards. Eleven core public health and disaster management competencies are proposed that incorporate Twitter as a tool for effective disaster risk reduction. Greater funding is required to promote the education and training of this tool for those in professional schools and in the current public health and disaster management workforce.