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2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2014)
China
Aug. 17, 2014 to Aug. 20, 2014
ISBN: 978-1-4799-5877-1
pp: 188-195
Srijan Kumar , Dept. of Computer Science& UMIACS, University of Maryland, College Park, 20742, USA
Francesca Spezzano , Dept. of Computer Science& UMIACS, University of Maryland, College Park, 20742, USA
V.S. Subrahmanian , Dept. of Computer Science& UMIACS, University of Maryland, College Park, 20742, USA
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.
INDEX TERMS
Social network services, Encyclopedias, Electronic publishing, Internet, Radiation detectors, Thumb
CITATION

S. Kumar, F. Spezzano and V. Subrahmanian, "Accurately detecting trolls in Slashdot Zoo via decluttering," 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), China, 2014, pp. 188-195.
doi:10.1109/ASONAM.2014.6921581
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