CopyCatch: stopping group attacks by spotting lockstep behavior in social networks

By Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, Christos Faloutsos. The Web Conference, 2013

386 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/28a12dbd4175397dcc6b19a695440d41e479103e

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In Computer Networks and Communications

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017, background): “However, since 2013, a number of research teams independently started to formalize new approaches for detecting the coordinated and synchronized behavior that characterizes groups of automated malicious accounts [5].”
Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015, background): “Two other works, SynchoTrap [16] and CopyCatch [13] also focused on analyzing behavior of a group of malicious identities.”
False Information on Web and Social Media: A Survey (2018, methodology): “This traffic was observed from search engines, social networks such as Facebook and Twitter, and from within Wikipedia itself.”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, background): “decomposed event data in time, user, and activity dimensions to extract similar behaviors (Beutel et al. 2013).”
The rise of social bots (2014, methodology): “thods of attack: the authors recount the case of spambots embedding text in images to evade detection by content analysis and URL blacklists. Other systems implementing mixed methods, like CopyCatch [Beutel et al. 2013] and SynchroTrap [Cao et al. 2014], also score comparatively low false positive r”
CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks (2015, background): “Therefore, recent studies try to detect bot accounts in OSNs by discovering their synchronized group activities [8,11,15,16,31].”
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016, methodology): “C OPY C ATCH [2] and G ET T HE S COOP [12] use local search heuristics to ﬁnd relevant dense bipartite sub-graphs.”
Deep Neural Networks for Bot Detection (2018, methodology): “rs used clustering techniques to identify large-scale behavioral anomalies [56], while other authors adopted supervised learning to analyze all accounts of some platform and separate bots from humans [7, 17, 37, 60]. Although these approaches can be useful, for example to detect large-scale bot infi”
Judging a Book by its Description : Analyzing Gender Stereotypes in the Man Bookers Prize Winning Fiction (2018, methodology): “Another interesting technique ((Beutel et al., 2013) and (Zhang et al.”