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PUD: Social Spammer Detection Based on PU Learning

  • Yuqi Song
  • Min GaoEmail author
  • Junliang Yu
  • Wentao Li
  • Junhao Wen
  • Qingyu Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Social networks act as the communication channels for people to share various information online. However, spammers who generate spam information reduce the satisfaction of common users. Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, supervised and semi-supervised methods. While the performance of supervised and semi-supervised methods is superior in terms of detection accuracy, these methods usually suffer from the dilemma of imbalanced data since the labeled normal users are far more than spammers in real situations. To address the problem, we propose a novel method only relying on normal users to detect spammers. Firstly, a classifier is built from a part of normal and unlabeled samples to pick out reliable spammers from unlabeled samples. Secondly, our well-trained detector, which is based on the given normal users and predicted spammers, can distinguish between normal users and spammers. Experiments conducted on real-world datasets show that the proposed method is competitive with supervised methods.

Keywords

Spammer detection Social network PU Learning 

Notes

Acknowledgments

The work is supported by the Basic and Advanced Research Projects in Chongqing under Grant No. cstc2015jcyjA40049, the National Key Basic Research Program of China (973) under Grant No. 2013CB328903, the Guangxi Science and Technology Major Project under Grant No. GKAA17129002, and the Graduate Scientific Research and Innovation Foundation of Chongqing, China under Grant No. CYS17035.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuqi Song
    • 1
    • 2
  • Min Gao
    • 1
    • 2
    Email author
  • Junliang Yu
    • 1
    • 2
  • Wentao Li
    • 3
  • Junhao Wen
    • 1
    • 2
  • Qingyu Xiong
    • 1
    • 2
  1. 1.Key Laboratory of Dependable Service Computing in Cyber Physical SocietyChongqing University, Ministry of EducationChongqingChina
  2. 2.School of Software EngineeringChongqing UniversityChongqingChina
  3. 3.Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, School of SoftwareUniversity of Technology SydneyUltimoAustralia

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