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

arXiv: https://arxiv.org/abs/1604.08504 · HTML: https://mlsanity.com/paper/1604.08504/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media](https://mlsanity.com/paper/2305.16797/) (2023)
- [Sentiment Analysis for Troll Detection on Weibo](https://mlsanity.com/paper/2103.09054/) (2021)

## Abstract (arXiv)

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.