User Behavior Modelling for Fake Information Mitigation on Social Web

The propagation of fake information on social networks is now a societal problem. Design of mitigation and intervention strategies for fake information has received less attention in social media research, mainly due to the challenge of designing relevant user behavior models. In this paper we lay the groundwork towards such models and present a novel, data-driven approach for user behavior analysis and characterization. We leverage unsupervised learning to define user behavioral categories over key behavior dimensions. We then relate these categories to content-based, user-based, and network-based features that can be extracted in near-real time and identify the most discriminative features. Finally, we build predictive models via supervised learning that leverage these features to determine a user’s behavior category. Rigorous evaluation indicates that the constructed models can be valuable in predicting user behavior from recent activity. These models can be employed to rapidly identify users for intervention in mitigation strategies, crisis communication, and brand management.

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  Hemant Purohit,et al.  Intent Mining for the Good, Bad, and Ugly Use of Social Web: Concepts, Methods, and Challenges , 2018, Lecture Notes in Social Networks.

[3]  Huan Liu,et al.  Social Cyber-Security , 2018, SBP-BRiMS.

[4]  Michel Cukier,et al.  Application of Routine Activity Theory to Cyber Intrusion Location and Time , 2017, 2017 13th European Dependable Computing Conference (EDCC).

[5]  M. Gentzkow,et al.  Social Media and Fake News in the 2016 Election , 2017 .

[6]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.

[7]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[8]  Christos Faloutsos,et al.  Suspicious Behavior Detection: Current Trends and Future Directions , 2016, IEEE Intelligent Systems.

[9]  Chin-Teng Lin,et al.  A review of clustering techniques and developments , 2017, Neurocomputing.

[10]  Filippo Menczer,et al.  Online Human-Bot Interactions: Detection, Estimation, and Characterization , 2017, ICWSM.

[11]  Le Song,et al.  Fake News Mitigation via Point Process Based Intervention , 2017, ICML.

[12]  Margrit Betke,et al.  Crowd-O-Meter: Predicting if a Person Is Vulnerable to Believe Political Claims , 2017, HCOMP.

[13]  Kate Starbird,et al.  Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter , 2017, ICWSM.

[14]  Nitin Agarwal,et al.  Social Cyber Forensics Approach to Study Twitter's and Blogs' Influence on Propaganda Campaigns , 2017, SBP-BRiMS.

[15]  Athanasios V. Vasilakos,et al.  Understanding user behavior in online social networks: a survey , 2013, IEEE Communications Magazine.