Content area
Purpose
The purpose of this paper is to examine the Houston Police Department (HPD)’s public engagement efforts using Twitter during Hurricane Harvey, which was a large-scale urban crisis event.
Design/methodology/approachThis study harvested a corpus of over 13,000 tweets using Twitter’s streaming API, across three phases of the Hurricane Harvey event: preparedness, response and recovery. Both text and social network analysis (SNA) techniques were employed including word clouds, n-gram analysis and eigenvector centrality to analyze data.
FindingsFindings indicate that departmental tweets coalesced around topics of protocol, reassurance and community resilience. Twitter accounts of governmental agencies, such as regional police departments, local fire departments, municipal offices, and the personal accounts of city’s police and fire chiefs were the most influential actors during the period under review, and Twitter was leveraged as de facto a 9-1-1 dispatch.
Practical implicationsEmergency management agencies should consider adopting a three-phase strategy to improve communication and narrowcast specific types of information corresponding to relevant periods of a crisis episode.
Originality/valuePrevious studies on police agencies and social media have largely overlooked discrete periods, or phases, in crisis events. To address this gap, the current study leveraged text and SNA to investigate Twitter communications between HPD and the public. This analysis advances understanding of information flows on law enforcement social media networks during crisis and emergency events.
Details
Application programming interface;
Emergency preparedness;
Law enforcement;
Social networks;
Information flow;
Social network analysis;
Text analysis;
Emergency management;
Electronic government;
Researchers;
Network analysis;
Informatics;
Graphical representations;
Police;
Public participation;
Government agencies;
Digital media;
Disaster management;
Eigenvectors;
Communication;
Interdisciplinary aspects;
Information science;
Data mining;
Reassurance;
Discourse strategies;
Resilience;
Computer mediated communication;
Social media;
Police departments;
Mass media;
Crises;
Fires;
Fire departments;
Verbal accounts;
N-Gram language models

