Detecting "Smart" Spammers On Social Network: A Topic Model Approach
Computation and Language
2016-09-12 v2 Social and Information Networks
Abstract
Spammer detection on social network is a challenging problem. The rigid anti-spam rules have resulted in emergence of "smart" spammers. They resemble legitimate users who are difficult to identify. In this paper, we present a novel spammer classification approach based on Latent Dirichlet Allocation(LDA), a topic model. Our approach extracts both the local and the global information of topic distribution patterns, which capture the essence of spamming. Tested on one benchmark dataset and one self-collected dataset, our proposed method outperforms other state-of-the-art methods in terms of averaged F1-score.
Cite
@article{arxiv.1604.08504,
title = {Detecting "Smart" Spammers On Social Network: A Topic Model Approach},
author = {Linqing Liu and Yao Lu and Ye Luo and Renxian Zhang and Laurent Itti and Jianwei Lu},
journal= {arXiv preprint arXiv:1604.08504},
year = {2016}
}
Comments
NAACL-HLT 2016, Student Research Workshop