Twitter content-based spam filtering

Advances in Intelligent Systems and Computing

Volumn 239, Issue , 2014, Pages 449-458

  Santos, Igor ^a   Miñambres Marcos, Igor ^a   Laorden, Carlos ^a   Galán García, Patxi ^a   Santamaría Ibirika, Aitor ^a   García Bringas, Pablo ^a  

^a UNIVERSITY OF DEUSTO   (Spain)

Author keywords

Machine learning; Social networks; Spam filtering; Text classification; Twitter

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); INTERNET; LEARNING SYSTEMS; SOCIAL NETWORKING (ONLINE); SOFT COMPUTING; TEXT PROCESSING;

COMPRESSION ALGORITHMS; CONTENT-BASED; CONTENT-BASED APPROACH; SPAM FILTERING; SPAM MESSAGES; TEXT CLASSIFICATION; TWITTER;

ELECTRONIC MAIL FILTERS;

EID: 84927618942     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-01854-6_46     Document Type: Conference Paper

References (43)

1. Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of twitter spam. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 243–258. ACM (2011)
2. Bratko, A., Filipič, B., Cormack, G., Lynam, T., Zupan, B.: Spam filtering using statistical data compression models. The Journal of Machine Learning Research 7, 2673–2698 (2006)
3. Jagatic, T., Johnson, N., Jakobsson, M., Menczer, F.: Social phishing. Communications of the ACM 50(10), 94–100 (2007)
4. Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference, CEAS (2010)
5. Grier, C., Thomas, K., Paxson, V., Zhang, M.: @spam: The underground on 140 characters or less. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 27–37. ACM (2010)
6. Wang, A.H.: Don’t follow me: Spam detection in twitter. In: Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), pp. 1–10. IEEE (2010)
7. Gao, H., Chen, Y., Lee, K., Palsetia, D., Choudhary, A.: Towards online spam filtering in social networks. In: Symposium on Network and Distributed System Security, NDSS (2012)
8. Ahmed, F., Abulaish, M.: A generic statistical approach for spam detection in online social networks. Computer Communications (in press, 2013)
9. Martinez-Romo, J., Araujo, L.: Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Systems with Applications (2012)
10. Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34(1), 1–47 (2002)
11. Lewis, D.: Naive (Bayes) at forty: The independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–18. Springer, Heidelberg (1998)
12. Schneider, K.: A comparison of event models for Naive Bayes anti-spam e-mail filtering. In: Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, pp. 307–314 (2003)
13. Androutsopoulos, I., Koutsias, J., Chandrinos, K., Spyropoulos, C.: An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–167 (2000)
14. Seewald, A.: An evaluation of naive Bayes variants in content-based learning for spam filtering. Intelligent Data Analysis 11(5), 497–524 (2007)
15. Vapnik, V.: The nature of statistical learning theory. Springer (2000)
16. Drucker, H., Wu, D., Vapnik, V.: Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10(5), 1048–1054 (1999)
17. Blanzieri, E., Bryl, A.: Instance-based spam filtering using SVM nearest neighbor classifier. Proceedings of FLAIRS 20, 441–442 (2007)
18. Sculley, D., Wachman, G.: Relaxed online SVMs for spam filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 415–422 (2007)
19. Quinlan, J.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
20. Carreras, X., Márquez, L.: Boosting trees for anti-spam email filtering. In: Proceedings of RANLP 2001, 4th International Conference on Recent Advances in Natural Language Processing, pp. 58–64. Citeseer (2001)
21. Zhang, L., Zhu, J., Yao, T.: An evaluation of statistical spam filtering techniques. ACM Transactions on Asian Language Information Processing (TALIP) 3(4), 243–269 (2004)
22. Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)
23. Wittel, G., Wu, S.: On attacking statistical spam filters. In: Proceedings of the 1st Conference on Email and Anti-Spam, CEAS (2004)
24. Pearl, J.: Reverend bayes on inference engines: a distributed hierarchical approach. In: Proceedings of the National Conference on Artificial Intelligence, pp. 133–136 (1982)
25. Bayes, T.: An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society 53, 370–418 (1763)
26. Castillo, E., Gutiérrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models, Erste edn., New York, NY, USA (1996)
27. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
28. Garner, S.: Weka: The Waikato environment for knowledge analysis. In: Proceedings of the 1995 New Zealand Computer Science Research Students Conference, pp. 57–64 (1995)
29. Quinlan, J.: C4. 5 programs for machine learning. Morgan Kaufmann Publishers (1993)
30. Fix, E., Hodges, J.L.: Discriminatory analysis: Nonparametric discrimination: Small sample performance. technical report project 21-49-004, report number 11. Technical report, USAF School of Aviation Medicine, Randolf Field, Texas (1952)
31. Amari, S., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12(6), 783–789 (1999)
32. Begleiter, R., El-Yaniv, R., Yona, G.: On prediction using variable order markov models. J. Artif. Intell. Res. (JAIR) 22, 385–421 (2004)
33. Cleary, J., Witten, I.: Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications 32(4), 396–402 (1984)
34. Ziv, J., Lempel, A.: Compression of individual sequences via variable-rate coding. IEEE Transactions on Information Theory 24(5), 530–536 (1978)
35. Nisenson, M., Yariv, I., El-Yaniv, R., Meir, R.: Towards behaviometric security systems: Learning to identify a typist. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 363–374. Springer, Heidelberg (2003)
36. Willems, F.: The context-tree weighting method: Extensions. IEEE Transactions on Information Theory 44(2), 792–798 (1998)
37. Volf, P.A.J.: Weighting techniques in data compression: Theory and algorithms. Citeseer (2002)
38. Ron, D., Singer, Y., Tishby, N.: The power of amnesia: Learning probabilistic automata with variable memory length. Machine Learning 25(2), 117–149 (1996)
39. Cormack, G., Horspool, R.: Data compression using dynamic markov modelling. The Computer Journal 30(6), 541–550 (1987)
40. Cormack, G., Gómez Hidalgo, J., Sánz, E.: Spam filtering for short messages. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management, pp. 313–320. ACM (2007)
41. Cormack, G., Hidalgo, J., Sánz, E.: Feature engineering for mobile(sms) spam filtering. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, vol. 23, pp. 871–872 (2007)
42. Santos, I., Laorden, C., Sanz, B., Bringas, P.G.: Enhanced topic-based vector space model for semantics-aware spam filtering. Expert SystemsWith Applications 39(1), 437–444, doi: 10.1016/j.eswa.2011.07.034
43. Laorden, C., Santos, I., Sanz, B., Alvarez, G., Bringas, P.G.: Word sense disambiguation for spam filtering. Electron. Commer. Rec. Appl. 11(3), 290–298 (2012)