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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Detecting Bots in Follower Markets
verfasst von : Wu Yang, Guozhong Dong, Wei Wang, Guowei Shen, Liangyi Gong, Miao Yu, Jiguang Lv, Yaxue Hu
Erschienen in: Bio-Inspired Computing - Theories and Applications
Verlag: Springer Berlin Heidelberg
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Along with the widely using of microblog, third party services such as follower markets sell bots to customers to build fake influence and reputation. However, the bots and the customers that have large numbers of followers usually post spam messages such as promoted messages, messages containing malicious links. In this paper, we propose an effective approach for bots detection based on interaction graph model and BP neural network. We build an interaction graph model based on user interaction and design robust interaction-based features. We conduct a comprehensive set of experiments to evaluate the proposed features using different machine learning classifiers. The results of our evaluation experiments show that BP neural network classifier using our proposed features can be effectively used to detect bots compared to other existing state-of-the-art approaches.