Skip to main content

Detecting sexual predators in chats using behavioral features and imbalanced learning*


This paper presents a system developed for detecting sexual predators in online chat conversations using a two-stage classification and behavioral features. A sexual predator is defined as a person who tries to obtain sexual favors in a predatory manner, usually with underage people. The proposed approach uses several text categorization methods and empirical behavioral features developed especially for the task at hand. After investigating various approaches for solving the sexual predator identification problem, we have found that a two-stage classifier achieves the best results. In the first stage, we employ a Support Vector Machine classifier to distinguish conversations having suspicious content from safe online discussions. This is useful as most chat conversations in real life do not contain a sexual predator, therefore it can be viewed as a filtering phase that enables the actual detection of predators to be done only for suspicious chats that contain a sexual predator with a very high degree. In the second stage, we detect which of the users in a suspicious discussion is an actual predator using a Random Forest classifier. The system was tested on the corpus provided by the PAN 2012 workshop organizers and the results are encouraging because, as far as we know, our solution outperforms all previous approaches developed for solving this task.

Hide All

This work has been partially funded by the Sectorial Operational Programme Human Resources Development 2007–2013 of the Romanian Ministry of European Funds through the Financial Agreement POSDRU/159/1.5/S/132397. Moreover, Claudia Cardei would like to thank Google for the Anita Borg scholarship granted in 2014 which partly funded this work on sexual predator identification in online conversations.

Hide All
Blei D. M., Ng A. Y., and Jordan M. I. 2003. Latent dirichlet allocation. The Journal of Machine Learning Research 3 (Jan): 9931022.
Bogdanova D., Rosso P., and Solorio T. 2012a. Modelling fixated discourse in chats with cyberpedophiles. In Proceedings of the Workshop on Computational Approaches to Deception Detection, Association for Computational Linguistics, Avignon, France, pp. 8690.
Bogdanova D., Rosso P., and Solorio T. 2012b. On the impact of sentiment and emotion based features in detecting online sexual predators. In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, Association for Computational Linguistics, Jeju, Republic of Korea, pp. 110–8.
Breiman L. 2001. Random forests. Machine Learning 45 (1): 532.
Cambria E., and Hussain A. 2012. Sentic Computing: Techniques, Tools, and Applications. Dordrecht: Springer Netherlands.
Core M. G., and Allen J. 1997. Coding dialogs with the damsl annotation scheme. In AAAI Fall Symposium on Communicative Action in Humans and Machines, Boston, MA, pp. 2835.
Cover T. M., and Thomas J. A. 2012. Elements of Information Theory. New York, NY: John Wiley & Sons.
Domingos P. 1999. Metacost: a general method for making classifiers cost-sensitive. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 155–64.
Eriksson G., and Karlgren J. 2012. Features for modelling characteristics of conversations. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Escalante H. J., Erro L., Villesanor E. Villatoro-Tello A. Juá rez, and Montes-y Gómez M. 2013. Sexual predator detection in chats with chained classifiers. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, pp. 46–54, Atlanta, Georgia.
Finkelhor D., Ormrod R., Turner H., and Hamby S. L. 2005. The victimization of children and youth: a comprehensive, national survey. Child Maltreatment 10 (1): 525.
Flesch R. 1948. A new readability yardstick. Journal of Applied Psychology 32 (3): 221.
Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., and Witten I. H. 2009. The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11 (1): 10–8.
He H., and Garcia E. A. 2009. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21 (9): 1263–84.
Inches G., and Crestani F. 2012. Overview of the international sexual predator identification competition at PAN-2012. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Jo T., and Japkowicz N. 2004. Class imbalances versus small disjuncts. ACM SIGKDD Explorations Newsletter 6 (1): 40–9.
Kontostathis A., Garron A., Reynolds K., West W., and Edwards L. 2012. Identifying predators using ChatCoder 2.0. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Kukar M., and Kononenko I. 1998. Cost-sensitive learning with neural networks. In Proceedings of 13th European Conference on Artificial Intelligence, Brighton, UK, pp. 445–9.
Liu X.-Y., Wu J., and Zhou Z.-H. 2009. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 (2): 539–50.
Malesky L. A. 2007. Predatory online behavior: modus operandi of convicted sex offenders in identifying potential victims and contacting minors over the internet. Journal of Child Sexual Abuse 16 (2): 2332. PMID: 17895230.
Maloof M. A. 2003. Learning when data sets are imbalanced and when costs are unequal and unknown. In Proceedings of ICML-2003 Workshop on Learning from Imbalanced Data Sets II, vol. 2, Washington, DC.
Manning C. D., Raghavan P., and Schütze H. 2008. Introduction to Information Retrieval. Cambridge: Cambridge University Press.
Mitchell K. J., Finkelhor D., and Wolak J. 2007. Youth internet users at risk for the most serious online sexual solicitations. American Journal of Preventive Medicine 32 (6): 532–7.
Morris C., and Hirst G. 2012. Identifying sexual predators by svm classification with lexical and behavioral features. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Parapar J., Losada D. E., and Barreiro A. 2012. A learning-based approach for the identification of sexual predators in chat logs. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Peersman C., Vaassen F., Van Asch V., and Daelemans W. 2012. Conversation level constraints on pedophile detection in chat rooms. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Pennebaker J. W., Francis M. E., and Booth R. J. 2001. Linguistic inquiry and word count: LIWC 2001. Mahway, NJ: Lawrence Erlbaum Associates.
Platt J. 1998. Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report msr-tr-98-14, Microsoft Research.
Popescu M., and Grozea C. 2012. Kernel methods and string kernels for authorship analysis. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Sun Y., Kamel M. S., Wong A. K., and Wang Y. 2007. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40 (12): 3358–78.
Vartapetiance A., and Gillam L. 2012. Quite simple approaches for authorship attribution, intrinsic plagiarism detection and sexual predator identification. In Proceedings of the 6th PAN workshop at CLEF2012 on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN2012), Rome.
Vartapetiance A., and Gillam L. 2014. Our little secret: pinpointing potential predators. Security Informatics 3 (1): 119.
Villatoro-Tello E., Juá rez-Gonzá lez A., Escalante H. J., Montes-y Gómez M., and Pineda L. V. 2012. A two-step approach for effective detection of misbehaving users in chats. In Proceedings of CLEF 2012 (Online Working Notes/Labs/Workshop), CEUR-WS, Rome, Italy.
Whittle H., Hamilton-Giachritsis C., Beech A., and Collings G. 2013. A review of online grooming: characteristics and concerns. Aggression and Violent Behavior 18 (1): 6270.
Whitty M. T. 2002. Liar, liar! an examination of how open, supportive and honest people are in chat rooms. Computers in Human Behavior 18 (4): 343352.
Wolak J., Finkelhor D., and Mitchell K. 2004. Internet-initiated sex crimes against minors: implications for prevention based on findings from a national study. Journal of Adolescent Health 35 (5): 1120.
Wolak J., Finkelhor D., Mitchell K. J., and Ybarra M. L. 2008. Online “predators” and their victims: myths, realities, and implications for prevention and treatment. American Psychologist 63 (2), 111128.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
  • URL: /core/journals/natural-language-engineering
Please enter your name
Please enter a valid email address
Who would you like to send this to? *


Full text views

Total number of HTML views: 12
Total number of PDF views: 79 *
Loading metrics...

Abstract views

Total abstract views: 568 *
Loading metrics...

* Views captured on Cambridge Core between 31st January 2017 - 11th December 2017. This data will be updated every 24 hours.