An emotional step toward automated trust detection in crisis social media

an article by Shane E. Halse, Andria Tapia and Anna Squicciarini (Pennsylvania State University, State College, USA) and Cornelia Caragea (University of North Texas, Denton, USA) published in Information, Communication & Society Volume 21 Issue 2

Abstract

To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the usefulness of these messages in the crisis response domain.

In this paper, we characterize tweets, which are perceived useful or trustworthy, and determine their main features as one possible dimension to identify useful messages in case of crisis.

In addition, we examine perceived emotions of these messages and how the different emotions affect the perceived usefulness and trustworthiness.

Our analysis is carried out on two datasets gathered from Twitter concerning Hurricane Sandy in 2012 and the Boston Bombings in 2013. The results indicate that there is a high correlation between trustworthiness and usefulness, and, interestingly, that there is a significant difference in the perceived emotions that contribute to each of these.

Our findings are poised to impact how messages from social media data are analyzed for use in crisis response.

Reading the title of this piece I was prepared for personal crisis such as severe distress but it is interesting to note the impact of Tweets on environmental and terrorist crises. H.

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A social media and crowdsourcing data mining system for crime prevention during and post-crisis situations

an article by Konstantinos Domdouzis, Babak Akhgar,Simon Andrews, Helen Gibson and Laurence Hirsch (Sheffield Hallam University, UK) published in Journal of Systems and Information Technology Volume 18 Issue 4 (2016)

Abstract

Purpose
A number of crisis situations, such as natural disasters, have affected the planet over the past decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated social media and crowdsourcing data mining system for the synchronization of the police and law enforcement agencies for the prevention of criminal activities during and post a large crisis situation.

Design/methodology/approach
The paper realized qualitative research in the form of a review of the literature. This review focuses on the necessity of using social media and crowdsourcing data mining techniques in combination with advanced Web technologies for the purpose of providing solutions to problems related to criminal activities caused during and after a crisis. The paper presents the ATHENA crisis management system, which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention.

Findings
Conclusions are drawn on the significance of social media and crowdsourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system.

Originality/value
The paper shows how the integrated use of social media and data mining algorithms can contribute in the resolution of problems that are developed during and after a large crisis.

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