4.5 Article

The use of Facebook for information seeking, decision support, and self-organization following a significant disaster

Journal

INFORMATION COMMUNICATION & SOCIETY
Volume 20, Issue 11, Pages 1680-1697

Publisher

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/1369118X.2016.1253762

Keywords

Social media; content analysis; tornado; collective error correction; public attention; political engagement

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The wide-spread proliferation of the Internet has revolutionized the ways that individuals obtain, interpret, and respond to information in the aftermath of disaster. An F3 tornado that impacted the community of Goderich, Ontario, on 21 August 2011 provided the opportunity to examine how people utilized Facebook and Facebook groups to seek out information, self-organize, and provide support. This mixed-methods project was conducted in two phases. First, semi-structured interviews (n=35) were conducted with Goderich area residents on their experiences using social media after the tornado. Next, all of the publicly available posts and replies on the Goderich Ontario Tornado Victims and Support (GOTVS) Facebook group were gathered and analysed using computer-assisted content analysis. The results demonstrate that Facebook was a highly influential source of information and support, particularly in the immediate aftermath of the disaster. Although misinformation and gossip occurred, the tendency was for the group to self-moderate inaccurate information. Public engagement by local officials may further reduce misinformation and encourage public confidence. The results also show that GOTVS activity significantly declined after two weeks. This abrupt decrease in public attention suggests that the window for engagement by public officials is narrow. As such, officials should have a social media plan in place so that they may engage with the public during the critical response phase when their participation may yield the greatest benefit. Finally, this research underscores the potential for analysing Facebook content using computer-assisted content analysis, a method that may be of interest across the social sciences.

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