Detecting predatory conversations in social media by deep Convolutional Neural Networks

Digital Investigation

Volumn 18, Issue , 2016, Pages 33-49

  Ebrahimi, Mohammadreza ^a   Suen, Ching Y ^a   Ormandjieva, Olga ^a  

^a Civil and Environmental Engineering   (Canada)

Author keywords

Convolutional neural network; Deep learning; Language model; Online predator identification; Predatory conversation; Support vector machine; Word embedding

Indexed keywords

AUTOMATION; CONVOLUTION; LEARNING ALGORITHMS; NEURAL NETWORKS; PROGRAM PROCESSORS; SOCIAL NETWORKING (ONLINE);

CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; LANGUAGE MODEL; PREDATORY CONVERSATION; WORD EMBEDDING;

SUPPORT VECTOR MACHINES;

EID: 84978315688     PISSN: 17422876     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.diin.2016.07.001     Document Type: Article

References (40)

1. Bengio, Y., Learning deep architectures for AI. Found Trends Mach Learn 2:1 (2009), 1–127.
2. Bogdanova, D., Rosso, P., Solorio, T., Exploring high-level features for detecting cyberpedophilia. Comput Speech Lang 28:1 (2014), 108–120.
3. Cano, A., Fernandez, M., Alani, H., Detecting child grooming behaviour patterns on social media. Aiello, L., McFarland, D., (eds.) Social informatics, 8851, 2014, Springer International Publishing, 412–427.
4. Chang, C.-C., Lin, C.-J., LIBSVM: a library for support vector machines. ACM Trans Intelligent Syst Technol, 2(3), 2011 27:1–27:27.
5. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.P., Natural language processing (almost) from scratch. JMLR 12 (2011), 2493–2537.
6. Ebrahimi, M., Suen, C.Y., Ormandjieva, O., Krzyzak, A., Recognizing predatory chat documents using semi-supervised anomaly detection. Presented at the document recognition and Retrieval XXIII, San Francisco, CA, USA: electronics and imaging 2016, 2016, 1–9.
7. Eriksson, G., Karlgren, J., Features for modelling characteristics of conversations. Presented at the notebook for PAN at CLEF 2012, Rome, Italy: CLEF 2012, 2012.
8. Escalante, H.J., Villatoro-Tello, E., Juárez, A., Montes-y-Gómez, M., Villaseñor, L., Sexual predator detection in chats with chained classifiers. Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis, 2013, Association for Computational Linguistics, Atlanta, Georgia, 46–54.
9. Goodfellow, I., Yoshua, B., Courville, A., Deep learning. 2016 Book in preparation for MIT Press.
10. Hochreiter, S., Schmidhuber, J., Long short-term memory. Neural Comput 9:8 (1997), 1735–1780.
11. Inches, G., Crestani, F., Overview of the international sexual predator identification competition at PAN-2012. 2012 [CLEF (working notes)].
12. İrsoy, O., Cardie, C., Opinion mining with deep recurrent neural networks. Proceedings of the conference on empirical methods in natural language processing, 2014, 720–728 [Doha, Qatar].
13. Johnson, R., ConText v2. Retrieved May 22, 2016, from, 2016, May 15 http://riejohnson.com/cnn_download.html.
14. Johnson, R., Zhang, T., Effective use of word order for text categorization with convolutional neural networks. 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015.
15. Johnson, R., Zhang, T., Semi-supervised convolutional neural networks for text categorization via region embedding. Presented at the NIPS, Montreal, 2015.
16. Kalchbrenner, N., Grefenstette, E., Blunsom, P., A convolutional neural network for modelling sentences. ACL, 2014, 2014.
17. Kang, I.-S., Kim, C.-K., Kang, S.-J., Na, S.-H., IR-based k-nearest neighbor approach for identifying abnormal chat users. Presented at the notebook for PAN at CLEF 2012, Rome, Italy: CLEF 2012, 2012.
18. Kim, Y., Convolutional neural networks for sentence classification. Proceedings of the conference on empirical methods in natural language processing, 2014 [Doha, Qatar].
19. Kontostathis, A., Toward the tracking and categorization of internet predators. Proceeding of text mining workshop 2009 held in conjunction with Ninth Siam International Conference Data Mining, 2009.
20. Kontostathis, A., Edwards, L., Leatherman, A., Text mining and cybercrime. Text mining, 2010, John Wiley & Sons, Ltd, 149–164.
21. Le, Q.V., Mikolov, T., Distributed representations of sentences and documents. Proceedings of the 31th international conference on machine learning, ICML 2014, Beijing, China, 21–26 June 2014, 2014, 1188–1196.
22. LeCun, Y., Kavukcuoglu, K., Farabet, C., Convolutional networks and applications in vision. Circuits and systems (ISCAS), proceedings of 2010 IEEE international symposium on, 2010, 253–256.
23. LeCun, Y., Bottou, L., Orr, G., Müller, K.-R., Efficient backprop. Neural networks: tricks of the trade, 2012, Springer Berlin Heidelberg, 9–48.
24. Mcghee, I., Bayzick, J., Kontostathis, A., Edwards, L., Mcbride, A., Jakubowski, E., Learning to identify internet sexual predation. Int J Electron Commer 15:3 (2011), 103–122.
25. Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., Khudanpur, S., Recurrent neural network based language model. INTERSPEECH 2010, 11th annual conference of the international speech communication association, Makuhari, Chiba, Japan, September 26–30, 2010, 2010, 1045–1048.
26. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., Distributed representations of words and phrases and their compositionality. Presented at the advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013, 2013, 3111–3119.
27. Morris, C., Identifying online sexual predators by SVM classification with lexical and behavioral features. (Master of Science Thesis), 2013, January 30, University Of Toronto, Canada Retrieved from ftp.cs.toronto.edu/pub/gh/Morris,Colin-MSc-thesis-2013.pdf.
28. Ng, A.Y., Feature selection, L1 vs. L2 regularization, and rotational invariance. Proceedings of the twenty-first international conference on machine learning, 2004, ACM, New York, NY, USA, 78.
29. Pendar, N., Toward spotting the pedophile telling victim from predator in text chats. 2007, IEEE, Washington, USA, 235–241.
30. Pennington, J., Socher, R., Manning, C.D., GloVe: global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP 2014), 2014, 1532–1543.
31. Pennington, J., Socher, R., Manning, C.D., GloVe. Retrieved May 20, 2016, from, 2016, May 10 http://nlp.stanford.edu/projects/glove/.
32. Perverted Justice, Retrieved from http://www.perverted-justice.com, 2016, May 21.
33. Calcul Quebec, Retrieved January 20, 2016, from http://www.calculquebec.ca/en/, 2016, May 24.
34. Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors. Nature 323:6088 (1986), 533–536.
35. Schmidhuber, J., Deep learning in neural networks: an overview. Neural Netw 61 (2015), 85–117.
36. Socher, R., Lin, C.C.-Y., Ng, A.Y., Manning, C.D., Parsing natural scenes and natural language with recursive neural networks. Getoor, L., Scheffer, T., (eds.) ICML, 2011, Omnipress, 129–136.
37. Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., et al. Recursive deep models for semantic compositionality over a sentiment treebank. Proceedings of the 2013 conference on empirical methods in natural language processing, 2013, Association for Computational Linguistics, Seattle, Washington, USA, 1631–1642.
38. Villatoro-Tello, E., Juárez-González, A., Escalante, H.J., Montes-y-Gómez, M., Villaseñor-Pineda, L., A two-step approach for effective detection of misbehaving users in chats. Presented at the notebook for PAN at CLEF’12, 2012, CLEF’12, Rome, Italy.
39. word2vec, Retrieved May 20, 2016, from https://code.google.com/archieve/p/word2vec, 2016, May 10.
40. Zhang, Y., Wallace, B., A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification. CoRR, abs/1510.03820. Retrieved from, 2015 http://arxiv.org/abs/1510.03820.