Title
A hybrid epidemic model for deindividuation and antinormative behavior in online social networks.
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.
Year
DOI
Venue
2016
10.1007/s13278-016-0321-5
Social Netw. Analys. Mining
Keywords
Field
DocType
Social Networking Site, Epidemic Model, Online Social Network, Sentiment Analysis, Abusive Behavior
Social network,Epidemic model,Sentiment analysis,Computer science,Popularity,Automaton,Deindividuation,Local regression,Artificial intelligence,Phenomenon,Machine learning
Journal
Volume
Issue
ISSN
6
1
1869-5469
Citations 
PageRank 
References 
2
0.37
16
Authors
4
Name
Order
Citations
PageRank
Cong Liao193.57
Anna Cinzia Squicciarini21301106.30
Christopher Griffin35811.43
Sarah Michele Rajtmajer43110.06