Social Spammer Detection with Sentiment Information

Xia Hu, Jiliang Tang, Huiji Gao, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

77 Scopus citations


Social media is a popular platform for spammers to unfairly overwhelm normal users with unwanted or fake content via social networking. The spammers significantly hinder the use of social media systems for effective information dissemination and sharing. Different from the spammers in traditional platforms such as email and the Web, spammers in social media can easily connect with each other, sometimes without mutual consent. They collude with each other to imitate normal users by quickly accumulating a large number of 'human' friends. In addition, content information in social media is noisy and unstructured. It is infeasible to directly apply traditional spammer detection methods in social media. Understanding and detecting deception has been extensively studied in traditional sociology and social sciences. Motivated by psychological findings in physical world, we investigate whether sentiment analysis can help spammer detection in online social media. In particular, we first conduct an exploratory study to analyze the sentiment differences between spammers and normal users, and then present an optimization formulation that incorporates sentiment information into a novel social spammer detection framework. Experimental results on real-world social media datasets show the superior performance of the proposed framework by harnessing sentiment analysis for social spammer detection.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
EditorsRavi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781479943029
StatePublished - Jan 1 2014
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other14th IEEE International Conference on Data Mining, ICDM 2014

ASJC Scopus subject areas

  • Engineering(all)


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