Presentation Title
The communication network of Hurricane María victims on Twitter
Abstract
Hurricane María, a category 4 tropical storm that struck many Caribbean islands in September 2017, left the residents of Puerto Rico without power or clean running water for at months at the shortest, and up to nearly a year for some. Lack of preparation on the part of the United States government lead to a much longer recovery than initially anticipated by most. Despite major disruption to the mobile network, many Puerto Ricans found ways to connect with other individuals on social media. Using data from Twitter, we construct a proxy for the network of communication between victims of the hurricane and their loved ones. We use information theoretic tools to compare the lexical divergence of different topological and temporal subgroups within the network. Lastly, we observe how different users within this network rose and fell from prominence as the hurricane came and passed, and as the aftermath progressed. We look at these temporal trends for individual nodes and node categories. Within this time-series, we point out several temporally dynamic trends that could not have been predicted ahead of the storm. Our findings provide insight toward how social media could be optimally utilized to disseminate relief information during similar events in the future.
Primary Faculty Mentor Name
Chris Danforth
Secondary Mentor Name
Peter Dodds, Meredith Niles
Faculty/Staff Collaborators
Meredith Niles, Chris Danforth, Peter Dodds
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Complex Systems
Primary Research Category
Social Sciences
The communication network of Hurricane María victims on Twitter
Hurricane María, a category 4 tropical storm that struck many Caribbean islands in September 2017, left the residents of Puerto Rico without power or clean running water for at months at the shortest, and up to nearly a year for some. Lack of preparation on the part of the United States government lead to a much longer recovery than initially anticipated by most. Despite major disruption to the mobile network, many Puerto Ricans found ways to connect with other individuals on social media. Using data from Twitter, we construct a proxy for the network of communication between victims of the hurricane and their loved ones. We use information theoretic tools to compare the lexical divergence of different topological and temporal subgroups within the network. Lastly, we observe how different users within this network rose and fell from prominence as the hurricane came and passed, and as the aftermath progressed. We look at these temporal trends for individual nodes and node categories. Within this time-series, we point out several temporally dynamic trends that could not have been predicted ahead of the storm. Our findings provide insight toward how social media could be optimally utilized to disseminate relief information during similar events in the future.