Abstract
This paper is devoted to the research and development of machine learning methods aimed at discovering potentially dangerous extremist information in social networks using pattern based approach. In this approach, a text document containing extremist information is used for automatic extracting keywords to query a social networks search engine, and then found messages are filtered according to the topic based measure of relevance with the pattern. N-gram based algorithms are proposed for constructing hidden topics and keywords that allow applying the proposed approach in the case of multilingual and illiterate texts. The performance of the proposed methods is experimentally studied on benchmark Ansar1 dataset.
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References
Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104(2), 211–240 (1997)
Cavnar, W.B.: Using an N-gram-based document representation with a vector processing retrieval model. In: NIST Special Publication 500–225: Overview of the Third Text REtrieval Conference (TREC-3), pp. 269–278. DIANE Publishing Company (1995)
Kuang, D., Choo, J., Park, H.: Nonnegative matrix factorization for interactive topic modeling and document clustering. In: Partitional Clustering Algorithms, pp. 215–243 (2015)
Mirzal, A.: Converged algorithms for orthogonal nonnegative matrix factorizations. arXiv Computing Research Repository arXiv:1010.5290v2, pp. 1–55 (2011)
Manning, C.D., et al.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Chisholm, E., Kolda, T.G.: New Term Weighting Formulas for the Vector Space Method in Information Retrieval. Computer Science and Mathematics Division, Oak Ridge National Laboratory (1999)
Xu, W., Liu, X.C.: Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 267–273. ACM (2003)
Zhang, Y., Zeng, S., Fan, L., Dang, Y., Larson, C.A., Chen, H.: Dark web forums portal: searching and analyzing jihadist forums. In: IEEE International Conference on Intelligence and Security Informatics, pp. 71–76 (2009)
The 20 Newsgroups data set. http://people.csail.mit.edu/jrennie/20Newsgroups/. Accessed 18 Aug 2017
Information retrieval. https://en.wikipedia.org/wiki/Information_retrieval. Accessed 18 Aug 2017
Acknowledgment
This research is supported by RFFI Grant # 16-29-09555.
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Petrovskiy, M., Tsarev, D., Pospelova, I. (2017). Pattern Based Information Retrieval Approach to Discover Extremist Information on the Internet. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_24
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DOI: https://doi.org/10.1007/978-3-319-71928-3_24
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