Citation

Sadri AM, Hasan S, Ukkusuri SV, Cebrian M. Transp. Res. Rec. 2018; 2672(1): 125-137.

Affiliation

1Moss School of Construction, Infrastructure and Sustainability, Florida International University, Miami, FL 2Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 3Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 4Media Lab, Massachusetts Institute of Technology, Cambridge, MA Corresponding Author: Address correspondence to Arif Mohaimin Sadri: sadri.buet@gmail.com

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118773896

PMID

unavailable

Abstract

Hurricane Sandy was one of the deadliest and costliest of hurricanes of the past few decades. Many states experienced significant power outage; however, many people used social media to communicate while having limited or no access to traditional information sources. Using machine learning techniques, this study explored the evolution of various communication patterns and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ∼52M tweets coming from ∼13M users between October 14, 2012 and November 12, 2012. A topic model was run on ∼763K tweets from the top 4,029 most frequent users who tweeted about Sandy at least 100 times. Some 250 well-defined communication patterns based on perplexity were identified. Conversations of the most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. Also presented is each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information-spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach the target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real-time user needs in future crises.

Language: en