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.
- Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, and Matei Ripeanu. 2013. Design and analysis of a social botnet. Computer Networks, Vol. 57, 2 (2013), 556--578. Google ScholarDigital Library
- Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro. 2012. Aiding the detection of fake accounts in large scale social online services Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). 197--210. Google ScholarDigital Library
- Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. 2010. Who is tweeting on Twitter: human, bot, or cyborg? Proceedings of the 26th annual computer security applications conference. ACM, 21--30. Google ScholarDigital Library
- John P Dickerson, Vadim Kagan, and VS Subrahmanian. 2014. Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on. IEEE, 620--627.Google ScholarCross Ref
- Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2014. The rise of social bots. arXiv preprint arXiv:1407.5225 (2014). Google ScholarDigital Library
- Alceu Ferraz Costa, Yuto Yamaguchi, Agma Juci Machado Traina, Caetano Traina Jr, and Christos Faloutsos. 2015. Rsc: Mining and modeling temporal activity in social media Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 269--278. Google ScholarDigital Library
- Zafar Gilani, Liang Wang, Jon Crowcroft, Mario Almeida, and Reza Farahbakhsh. 2016. Stweeler: A Framework for Twitter Bot Analysis. In Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, 37--38. Google ScholarDigital Library
- Yuede Ji, Qiang Li, Yukun He, and Dong Guo. 2015. BotCatch: leveraging signature and behavior for bot detection. Security and Communication Networks Vol. 8, 6 (2015), 952--969. Google ScholarDigital Library
- Kyumin Lee, James Caverlee, and Steve Webb. 2010. Uncovering social spammers: social honeypotsGoogle Scholar
- machine learning Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, 435--442.Google Scholar
- Kyumin Lee, Brian David Eoff, and James Caverlee. 2011. Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter. ICWSM.Google Scholar
- Juan Martinez-Romo and Lourdes Araujo. 2013. Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Systems with Applications Vol. 40, 8 (2013), 2992--3000. Google ScholarDigital Library
- Fred Morstatter, Liang Wu, Tahora H Nazer, Kathleen M Carley, and Huan Liu. 2016. A new approach to bot detection: striking the balance between precision and recall Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE, 533--540.Google Scholar
- Iren Tankova, Ana Adan, and Gualberto Buela-Casal. 1994. Circadian typology and individual differences. A review. Personality and individual differences Vol. 16, 5 (1994), 671--684.Google Scholar
- Bimal Viswanath, Ansley Post, Krishna P Gummadi, and Alan Mislove. 2010. An analysis of social network-based sybil defenses. ACM SIGCOMM Computer Communication Review Vol. 40, 4 (2010), 363--374. Google ScholarDigital Library
- Matthew D Zeiler. 2012. ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012).Google Scholar
Index Terms
- Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information
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