Detecting "Smart" Spammers On Social Network: A Topic Model Approach

TL;DR

This paper introduces a novel LDA-based method for detecting 'smart' spammers on social networks by analyzing topic distribution patterns, outperforming existing methods on benchmark datasets.

Contribution

The paper proposes a new spammer detection approach using topic modeling that captures local and global topic patterns, effectively identifying sophisticated spammers.

Findings

Outperforms state-of-the-art methods in F1-score

Effective on both benchmark and self-collected datasets

Captures nuanced spamming behaviors through topic patterns

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.