@inproceedings{10a9fdb36fc64baa8f5e736b284b7543,
title = "Accurately detecting trolls in Slashdot Zoo via decluttering",
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.",
author = "Srijan Kumar and Francesca Spezzano and Subrahmanian, {V. S.}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 ; Conference date: 17-08-2014 Through 20-08-2014",
year = "2014",
month = oct,
day = "10",
doi = "10.1109/ASONAM.2014.6921581",
language = "English (US)",
series = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "188--195",
editor = "Xindong Wu and Xindong Wu and Martin Ester and Guandong Xu",
booktitle = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
address = "United States",
}