Primary Faculty Mentor Name

Chris Danforth

Project Collaborators

Meredith Niles, Chris Danforth, Peter Dodds

Secondary Mentor NetID

pdodds, mtniles

Secondary Mentor Name

Peter Dodds, Meredith Niles

Status

Graduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Complex Systems

Primary Research Category

Social Sciences

Presentation Title

The communication network of Hurricane María victims on Twitter

Time

9:00 AM

Location

Silver Maple Ballroom - Social Sciences

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.

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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.