Detecting "Smart" Spammers On Social Network: A Topic Model Approach
This addresses the challenge of identifying sophisticated spammers that evade traditional rules, which is a domain-specific problem for social network security.
The paper tackled the problem of detecting 'smart' spammers on social networks by proposing a novel classification approach based on Latent Dirichlet Allocation (LDA) to extract topic distribution patterns, achieving improved performance over state-of-the-art methods in terms of averaged F1-score.
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.