Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information
Chiyu Cai1,2; Linjing Li1; Daniel Zeng1,3
2017-11
Conference NameInternational Conference on Information and Knowledge Management (CIKM 2017)
Conference DateNovember 6-10
Conference PlaceSingapore
Abstract

Bots are regarded as the most common kind of malwares in the era of Web 2.0. In recent years, Internet has been populated by hundreds of millions of bots, especially on social media. Thus, the demand on effective and efficient bot detection algorithms is more urgent than ever. Existing works have partly satisfied this requirement by way of laborious feature engineering. In this paper, we propose a deep bot detection model aiming to learn an effective representation of social user and then detect social bots by jointly modeling social behavior and content information. The proposed model learns the representation of social behavior by encoding both endogenous and exogenous factors which affect user behavior. As to the representation of content, we regard the user content as temporal text data instead of just plain text as be treated in other existing works to extract semantic information and latent temporal patterns. To the best of our knowledge, this is the first trial that applies deep learning in modeling social users and accomplishing social bot detection. Experiments on real world dataset collected from Twitter demonstrate the effectiveness of the proposed model.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19858
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.University of Arizona
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chiyu Cai,Linjing Li,Daniel Zeng. Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information[C],2017.
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