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  • GroupIdentities_CriadoRashidLeite

    Rights statement: This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 62, 2016 DOI: 10.1016/S0370-1573(02)00269-7

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Flash mobs, Arab Spring and protest movements: can we analyse group identities in online conversations?

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>15/11/2016
<mark>Journal</mark>Expert Systems with Applications
Volume62
Number of pages13
Pages (from-to)212-224
<mark>State</mark>Published
Early online date16/06/16
<mark>Original language</mark>English

Abstract

The Internet has provided people with new ways of expressing not only their individuality but also their collectivity i.e., their group affiliations. These group identities are the shared sense of belonging to a group. Online contact with others who share the same group identity can lead to cooperation and, even, coordination of social action initiatives both online and offline. Such social actions may be for the purposes of positive change, e.g., the Arab Spring in 2010, or disruptive, e.g., the England Riots in 2011. Stylometry and authorship attribution research has shown that it is possible to distinguish individuals based on their online language. In contrast, this work proposes and evaluates a model to analyse group identities online based on textual conversations amongst groups. We argue that textual features make it possible to automatically distinguish between different group identities and detect whether group identities are salient (i.e., most prominent) in the context of a particular conversation. We show that the salience of group identities can be detected with 95% accuracy and group identities can be distinguished from others with 84% accuracy. We also identify the most relevant features that may enable mal-actors to manipulate the actions of online groups. This has major implications for tools and techniques to drive positive social actions online or safeguard society from disruptive initiatives. At the same time, it poses privacy challenges given the potential ability to persuadeor dissuade large groups online to move from rhetoric to action.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 62, 2016 DOI: 10.1016/S0370-1573(02)00269-7