Tingmin Wu
ABSTRACT
Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.
- R. Aires, A. Manfrin, S. M. Aluísio, and D. Santos. Which Classification Algorithm Works Best with Stylistic Features of Portuguese in Order to Classify Web Texts According to Users' needs?. ICMC-USP, 2004.Google Scholar
- N. B. Amor, S. Benferhat, and Z. Elouedi. Naive bayes vs decision trees in intrusion detection systems. In Proceedings of the 2004 ACM symposium on Applied computing, pages 420--424. ACM, 2004. Google ScholarDigital Library
- F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), volume 6, page 12, 2010.Google Scholar
- M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Kötter, T. Meinl, P. Ohl, K. Thiel, and B. Wiswedel. Knime-the konstanz information miner: version 2.0 and beyond. AcM SIGKDD explorations Newsletter, 11(1):26--31, 2009. Google ScholarDigital Library
- L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001. Google ScholarDigital Library
- C. Chen, J. Zhang, Y. Xiang, and W. Zhou. Asymmetric self-learning for tackling twitter spam drift. In 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 208--213. IEEE, 2015.Google ScholarCross Ref
- C. Chen, J. Zhang, Y. Xie, Y. Xiang, W. Zhou, M. M. Hassan, A. AlElaiwi, and M. Alrubaian. A performance evaluation of machine learning-based streaming spam tweets detection. IEEE Transactions on Computational Social Systems, 2(3):65--76, 2015.Google ScholarCross Ref
- R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12(Aug):2493--2537, 2011. Google ScholarDigital Library
- T. G. Dietterich. Ensemble methods in machine learning. In International workshop on multiple classifier systems, pages 1--15. Springer, 2000. Google ScholarDigital Library
- V. N. Ghate and S. V. Dudul. Optimal mlp neural network classifier for fault detection of three phase induction motor. Expert Systems with Applications, 37(4):3468--3481, 2010. Google ScholarDigital Library
- C. Grier, K. Thomas, V. Paxson, and M. Zhang. @ spam: the underground on 140 characters or less. In Proceedings of the 17th ACM conference on Computer and communications security, pages 27--37. ACM, 2010. Google ScholarDigital Library
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56--65. ACM, 2007. Google ScholarDigital Library
- X. Jin, C. Lin, J. Luo, and J. Han. A data mining-based spam detection system for social media networks. Proceedings of the VLDB Endowment, 4(12):1458--1461, 2011.Google ScholarDigital Library
- Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. In ICML, volume 14, pages 1188--1196, 2014.Google ScholarDigital Library
- Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436--444, 2015.Google ScholarCross Ref
- K. Lee, J. Caverlee, and S. Webb. Uncovering social spammers: social honeypots+ machine learning. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 435--442. ACM, 2010. Google ScholarDigital Library
- S. Lee and J. Kim. Warningbird: Detecting suspicious urls in twitter stream. In NDSS, volume 12, pages 1--13, 2012.Google Scholar
- A. Liaw and M. Wiener. Classification and regression by randomforest. R news, 2(3):18--22, 2002.Google Scholar
- S. Liu, J. Zhang, Y. Wang, and Y. Xiang. Fuzzy-based feature and instance recovery. In Asian Conference on Intelligent Information and Database Systems, pages 605--615. Springer, 2016.Google ScholarCross Ref
- S. Liu, J. Zhang, and Y. Xiang. Statistical detection of online drifting twitter spam: Invited paper. In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, pages 1--10. ACM, 2016. Google ScholarDigital Library
- J. Ma, L. K. Saul, S. Savage, and G. M. Voelker. Learning to detect malicious urls. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):30, 2011. Google ScholarDigital Library
- T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.Google Scholar
- J. Oliver, P. Pajares, C. Ke, C. Chen, and Y. Xiang. An in-depth analysis of abuse on twitter. Trend Micro, 225, 2014.Google Scholar
- J. D. Rennie, L. Shih, J. Teevan, D. R. Karger, et al. Tackling the poor assumptions of naive bayes text classifiers. In ICML, volume 3, pages 616--623. Washington DC), 2003.Google ScholarDigital Library
- K. Rybina. Sentiment analysis of contexts around query terms in documents. PhD thesis, MasterâĂŹs thesis, 2012.Google Scholar
- J. Song, S. Lee, and J. Kim. Spam filtering in twitter using sender-receiver relationship. In International Workshop on Recent Advances in Intrusion Detection, pages 301--317. Springer, 2011. Google ScholarDigital Library
- G. Stringhini, C. Kruegel, and G. Vigna. Detecting spammers on social networks. In Proceedings of the 26th Annual Computer Security Applications Conference, pages 1--9. ACM, 2010. Google ScholarDigital Library
- I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104--3112, 2014. Google ScholarDigital Library
- D. Tang, F. Wei, B. Qin, T. Liu, and M. Zhou. Coooolll: A deep learning system for twitter sentiment classification. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 208--212, 2014.Google ScholarCross Ref
- D. Urbansky, K. Muthmann, P. Katz, and S. Reichert. Tud palladian overview. TU Dresden, Department of Systems Engineering, Chair Computer Networks, IIR Group, 5, 2011.Google Scholar
- A. H. Wang. Don't follow me: Spam detection in twitter. In Security and Cryptography (SECRYPT), Proceedings of the 2010 International Conference on, pages 1--10. IEEE, 2010.Google Scholar
- D. Wang, S. B. Navathe, L. Liu, D. Irani, A. Tamersoy, and C. Pu. Click traffic analysis of short url spam on twitter. In Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom), 2013 9th International Conference Conference on, pages 250--259. IEEE, 2013.Google Scholar
- C. Yang, R. Harkreader, and G. Gu. Empirical evaluation and new design for fighting evolving twitter spammers. IEEE Transactions on Information Forensics and Security, 8(8):1280--1293, 2013. Google ScholarDigital Library
Index Terms
- Twitter spam detection based on deep learning
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