Twitter Disaster Behavior
dc.contributor.author | Thackaberry, Taylor | en |
dc.contributor.author | Bogemann, Kayley | en |
dc.contributor.author | Burchard, Shane | en |
dc.contributor.author | Butler, Jessie | en |
dc.contributor.author | Spencer, Austin | en |
dc.date.accessioned | 2020-05-13T19:31:55Z | en |
dc.date.available | 2020-05-13T19:31:55Z | en |
dc.date.issued | 2020-05-05 | en |
dc.description.abstract | The purpose of the Twitter Disaster Behavior project is to identify patterns in online behavior during natural disasters by analyzing Twitter data. The main goal is to better understand the needs of a community during and after a disaster, to aid in recovery. The datasets analyzed were collections of tweets about Hurricane Maria, and recent earthquake events, in Puerto Rico. All tweets pertaining to Hurricane Maria are from the timeframe of September 15 through October 14, 2017. Similarly, tweets pertaining to the Puerto Rico earthquake from January 7 through February 6, 2020 were collected. These tweets were then analyzed for their content, number of retweets, and the geotag associated with the author of the tweet. We counted the occurrence of key words in topics relating to preparation, response, impact, and recovery. This data was then graphed using Python and Matplotlib. Additionally, using a Twitter crawler, we extracted a large dataset of tweets by users that used geotags. These geotags are used to examine location changes among the users before, during, and after each natural disaster. Finally, after performing these analyses, we developed easy to understand visuals and compiled these figures into a poster. Using these figures and graphs, we compared the two datasets in order to identify any significant differences in behavior and response. The main differences we noticed stemmed from two key reasons: hurricanes can be predicted whereas earthquakes cannot, and hurricanes are usually an isolated event whereas earthquakes are followed by aftershocks. Thus, the Hurricane Maria dataset experienced the highest amount of tweet activity at the beginning of the event and the Puerto Rico earthquake dataset experienced peaks in tweet activity throughout the entire period, usually corresponding to aftershock occurrences. We studied these differences, as well as other important trends we identified. | en |
dc.description.notes | ISCRAMposterTwitterDisasterBehavior - Poster submission for the ISCRAM 2020 conference. TwitterDisasterBehavior_FinalPresentation - Powerpoint slides for the final presentation of the research TwitterDisasterBehavior_FinalReport - Final Report on the work done and conclusions drawn from the research TwitterDisasterBehavior_Figures - A collection of additional figures and graphs generated during research | en |
dc.description.sponsorship | National Science Foundation | en |
dc.description.sponsorship | CMMI-1638207 | en |
dc.description.sponsorship | CRISP: Collaborative Research: Coordinated, Behaviorally-Aware Recovery for Transportation and Power Disruptions | en |
dc.description.sponsorship | Global Event and Trend Archive Research (GETAR) | en |
dc.description.sponsorship | NSF IIS-1619028 | en |
dc.identifier.uri | http://hdl.handle.net/10919/98251 | en |
dc.language.iso | en_US | en |
dc.publisher | Virginia Tech | en |
dc.relation.ispartof | 2020 International Association for Information Systems for Crisis Response and Management | en |
dc.rights | In Copyright | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
dc.subject | Puerto Rico | en |
dc.subject | earthquake | en |
dc.subject | Hurricane Maria | en |
dc.subject | Topic Analysis | en |
dc.subject | geotag | en |
dc.subject | geolocation | en |
dc.subject | social media | en |
dc.subject | en | |
dc.subject | disaster | en |
dc.subject | behavior | en |
dc.title | Twitter Disaster Behavior | en |
dc.type | Image | en |
dc.type | Poster | en |
dc.type | Presentation | en |
dc.type | Report | en |
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