Accurately detecting trolls in Slashdot Zoo via decluttering

Srijan Kumar, Francesca Spezzano, V. S. Subrahmanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations

Abstract

Online social networks like Slashdot bring valuable information to millions of users - but their accuracy is based on the integrity of their user base. Unfortunately, there are many 'trolls' on Slashdot who post misinformation and compromise system integrity. In this paper, we develop a general algorithm called TIA (short for Troll Identification Algorithm) to classify users of an online 'signed' social network as malicious (e.g. trolls on Slashdot) or benign (i.e. normal honest users). Though applicable to many signed social networks, TIA has been tested on troll detection on Slashdot Zoo under a wide variety of parameter settings. Its running time is faster than many past algorithms and it is significantly more accurate than existing methods.

Original languageEnglish (US)
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
EditorsXindong Wu, Xindong Wu, Martin Ester, Guandong Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages188-195
Number of pages8
ISBN (Electronic)9781479958771
DOIs
StatePublished - Oct 10 2014
Externally publishedYes
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: Aug 17 2014Aug 20 2014

Publication series

NameASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Other

Other2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
Country/TerritoryChina
CityBeijing
Period8/17/148/20/14

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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