The 3rd International Workshop on Data, Text, Web, and Social Network Mining

Research Article

SpamDia: Spammer Diagnosis in Sina Weibo Microblog

  • @INPROCEEDINGS{10.4108/eai.18-6-2016.2264143,
        author={Hao Chen and Jun Liu and Jianhong Mi},
        title={SpamDia: Spammer Diagnosis in Sina Weibo Microblog},
        proceedings={The 3rd International Workshop on Data, Text, Web, and Social Network Mining},
        keywords={microblog spammer detection},
  • Hao Chen
    Jun Liu
    Jianhong Mi
    Year: 2016
    SpamDia: Spammer Diagnosis in Sina Weibo Microblog
    DOI: 10.4108/eai.18-6-2016.2264143
Hao Chen1,*, Jun Liu2, Jianhong Mi2
  • 1: Xi'an Jiaotong university
  • 2: Xi'an Jiaotong University
*Contact email:


Microblogs open opportunities for social spammer accounts. These widespread spammers, are threatening for microblog services and normal users. Therefore, detecting spammers should be conducted to fight and stop them. In this paper, we propose an approach to diagnose user accounts in China’s most popular microblog site Sina Weibo. Unlike existing approaches, which can hardly discover sophisticated spammers and only give a simple conclusion as spammer or not lacking of detail information, but our work provides a more responsible way that reveals the clues to verify a spammer account by using classifier-level fusion and feature-level comparison. Distinct discriminative features are used to train basic classifiers. Then a fusion model is learned to combine the outputs of the basic classifiers and make the final prediction. Comparing basic classifiers outputs with the final prediction offers the insights of spammer identification. Experiments show that our approach significantly improves the classification performance and this approach can point out spammers’ specific spam action in a detail way that helps us strike spammers accurately.