Abstract
Researchers have designed features of Twitter accounts that help machine learning algorithms to detect spammers. Spammers try to evade detection by manipulating such features. This has led to the design of robust features, i.e., features that are hard to manipulate. In this paper, we propose and evaluate five new robust features.
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References
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter. In: CEAS (2010)
Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Soc. 2, 113–120 (1972)
Lazic, M.: On the Laplacian energy of a graph. Czech. Math. J. 56, 1207–1213
Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots + machine learning. In: ACM SIGIR, pp. 435–442 (2010)
Lin, D.: An information-theoretic definition of similarity. In: Proc. 15th International Conference on Machine Learning, pp. 296–304 (1998)
Lintean, M.C., Moldovan, C., Rus, V., McNamara, D.S.: The role of local and global weighting in assessing the semantic similarity of texts using latent semantic analysis. In: FLAIRS (2010)
McCord, M., Chuah, M.: Spam detection on Twitter using traditional classifiers. In: Calero, J.M.A., Yang, L.T., Mármol, F.G., GarcÃa Villalba, L.J., Li, A.X., Wang, Y. (eds.) ATC 2011. LNCS, vol. 6906, pp. 175–186. Springer, Heidelberg (2011)
Qi, X., Duval, D., Christensen, K., Fuller, E., Spahiu, A., Wu, Q., Wu, Y., Tang, W., Zhang, C.: Terrorist networks, network energy and node removal: A new measure of centrality based on Laplacian energy. Social Networking 2, 19–31 (2013)
Rus, V., Lintean, M., Moldovan, C., Baggett, W., Niraula, N., Morgan, B.: SIMILAR Corpus: A resource to foster the qualitative understanding of semantic similarity of texts. In: LREC, pp. 50–59 (2012)
Song, J., Lee, S., Kim, J.: Spam filtering in Twitter using sender-receiver relationship. In: Sommer, R., Balzarotti, D., Maier, G. (eds.) RAID 2011. LNCS, vol. 6961, pp. 301–317. Springer, Heidelberg (2011)
Wang, A.: Don’t follow me: Spam detection in Twitter. In: SECRYPT, pp. 1–10 (2010)
Yang, C., Harkreader, R., Gu, G.: Empirical evaluation and new design for fighting evolving Twitter spammers. IEEE TIFS 8, 1280–1293 (2013)
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Karim, M.R., Zilles, S. (2014). Robust Features for Detecting Evasive Spammers in Twitter. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_28
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DOI: https://doi.org/10.1007/978-3-319-06483-3_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06482-6
Online ISBN: 978-3-319-06483-3
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