Year: 2016
Type: conference-paper
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 stateof-the-art methods in terms of averaged F1score.
DOI: 10.18653/v1/n16-2007
Authors: Linqing Liu, Yao Lu, Ye Luo, Renxian Zhang, Laurent Itti, Jianwei Lu
Institutions: Tongji University, University of Southern California
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2344246423