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Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

A hybrid epidemic model for deindividuation and antinormative behavior in online social networks

Authors: Cong Liao, Anna Squicciarini, Christopher Griffin, Sarah Rajtmajer

Published in: Social Network Analysis and Mining | Issue 1/2016

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Abstract

With the increasing popularity of user-contributed sites, the phenomenon of “social pollution”, the presence of abusive posts has become increasingly prevalent. In this paper, we describe a novel approach to investigate negative behavior dynamics in online social networks as epidemic phenomena. We show that using hybrid automata, it is possible to explain the contagion of antinormative behavior in certain online commentaries. We present two variations of a finite-state machine model for time-varying epidemic dynamics, namely triggered state transition and iterative local regression, which differ with respect to accuracy and complexity.We validate the model with experiments over a dataset of 400,000 comments on 800 YouTube videos, classified by genre, and indicate how different epidemic patterns of behavior can be tied to specific interaction patterns among users.
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Footnotes
1
For simplicity, we assume that comment ratings are not the product of up-voting by trolls or spammers.
 
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Metadata
Title
A hybrid epidemic model for deindividuation and antinormative behavior in online social networks
Authors
Cong Liao
Anna Squicciarini
Christopher Griffin
Sarah Rajtmajer
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0321-5

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