Abstract
Online paedophile activity in social media has become a major concern in society as Internet access is easily available to a broader younger population. One common form of online child exploitation is child grooming, where adults and minors exchange sexual text and media via social media platforms. Such behaviour involves a number of stages performed by a predator (adult) with the final goal of approaching a victim (minor) in person. This paper presents a study of such online grooming stages from a machine learning perspective. We propose to characterise such stages by a series of features covering sentiment polarity, content, and psycho-linguistic and discourse patterns. Our experiments with online chatroom conversations show good results in automatically classifying chatlines into various grooming stages. Such a deeper understanding and tracking of predatory behaviour is vital for building robust systems for detecting grooming conversations and potential predators on social media.
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References
Baker, C.F., Fillmore, C.J., Lowe, J.B.: The berkeley framenet project. In: In Proc. of COLING/ACL (1998)
Bogdanova, D., Rosso, P., Solorio, T.: Exploring high-level features for detecting cyberpedophilia. Comput. Speech Lang. 28(1), 108–120 (2014)
Chang, C.-C., Lin, C.-J.: Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)
Das, D., Schneider, N., Chen, D., Smith, N.A.: Semafor 1.0: A probabilistic frame-semantic parser. Technical report, Carnegie Mellon University Technical Report CMU-LTI-10-001 (2010)
Egan, V., Hoskinson, J., Shewan, D.: Perverted justice: a content analysis of the language used by offenders detected attempting to solicit children for sex. Antisocial Behavior: Causes, Correlations and Treatments 20(3), 273 (2011)
Escalante, H.J., Inaoe, L., Enrique, L., No, E., Villatoro-tello, E., Cuajimalpa, U., Juárez, A., Enrique, L., No, E., Villasen̈or, L.: Sexual predator detection in chats with chained classifiers. In: Proc. of ACL (2013)
Webster, S., et al.: European online grooming project - final report. Technical report, European Comission Safer Internet Plus Programme (2012)
Gunning, R.: The technique of clear writing. McGraw-Hill, International Book (1952)
Gupta, A., Kumaraguru, P., Sureka, A.: Characterizing pedophile conversations on the internet using online grooming. CoRR (2012)
Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, University of Waikato, Hamilton, New Zealand (1998)
Inches, G., Crestani, F.: Overview of the international sexual predator identification competition at pan-2012. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF (2012)
Chambers, J.K., Trudgill, P., Schilling-Estes, N.: The Handbook Of Language Variation And Change. Blackwell, London (2004)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. 14th IJCAI, vol. 2 (1995)
Kontostathis, A.: Chatcoder: Toward the tracking and categorization of internet predators. In: Proc. Text Mining Workshop 2009 held in conjunction with the Ninth SIAM International Conference on Data Mining. SPARKS, NV (2009)
Kontostathis, A., Edwards, L., Bayzick, J., Mcghee, I., Leatherman, A., Moore, K.: Comparison of rule-based to human analysis of chat logs. In: 1st International Workshop on Mining Social Media Programme, Conferencia de la Asociación Española Para La Inteligencia Artificial, 2009 (2010)
Lilley, C., Ball, R., Vernon, H.: The experiences of 11-16 year olds on social networking sites. Technical report, NSPCC (2014)
Michalopoulos, D., Mavridis, I.: Utilizing document classification for grooming attack recognition. In: Proceedings of the IEEE Symposium on Computers and Communications (2011)
Nijman, H., Merckelbach, H., Cima, M.: Performance intelligence, sexual offending and psychopathy. Journal of Sexual Aggression 15, 319–330 (2009)
O’Connell, R.: A typology of child cybersexploitation and online grooming practices. Technical report, Cyberspace Research Unit, University of Central Lancashier (2003)
Australian Institute of Criminology (AIC). Online child grooming laws. Technical report, High tech crime brief no. 17. Canberra: AIC (2008)
Australian Institute of Criminology (AIC). Children’s use of mobile phones. Technical report, GSMA, NTT DOCOMO (2013)
Olson, L.N., Daggs, J.L., Ellevold, B.L., Rogers, T.K.K.: Entrapping the innocent: Toward a theory of child sexual predators luring communication. Communication Theory 17(3), 231–251 (2007)
Palmer, T., Stacey, L.: Just one click - sexual abuse of children and young people through the internet and mobile phone technology. Technical report. Barnardo’s UK, Essex (2004)
Pendar, N.: Toward spotting the pedophile telling victim from predator in text chats. In: International Conference on Semantic Computing, ICSC 2007, pp. 235–241 (September 2007)
Pennebaker, J.W., Chung, C.K., Ireland, M., Gonzales, A., Booth, R.J.: The development and psychometric properties of liwc 2007. Technical report, Technical report, Austin, TX, LIWC.Net (2007)
Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count (liwc): Liwc2001 manual. Technical report, Erlbaum Publishers (2001)
Porter, M.: An algorithm for suffix stripping. Program 14(3) (1980)
SCAMP Project. Study of cognition, adolescents and mobile phones (scamp). Technical report, Imperial College London (2014)
Rowe, M., Alani, H.: Mining and comparing engagement dynamics across multiple social media platforms. In: Proc. of ACM 2014 Web Science Conference, Bloomington, Indiana, USA, pp. 229–238 (2014)
Argamon, S., Koppel, M., Pennebaker, J., Schler, J.: Automatically profiling the author of an anonymous text. Communications of the ACM 52(2), 119–123 (2009)
Salzberg, S.L., Fayyad, U.: On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 317–328 (1997)
Griffith, G., Roth, L.: Protecting children from online sexual predators. Technical report, NSW parliamentary library briefing paper no. 10/07 Sydney: NSW Parliamentary Library (2007)
Toutanova, K., Manning, C.D.: Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), pp. 63–70 (2000)
Wagner, C., Rowe, M., Strohmaier, M., Alani, H.: Ignorance isn’t bliss: an empirical analysis of attention patterns in online communities. In: Proc. of 4th IEEE International Conference on Social Computing, Amsterdam, The Netherlands (2012)
Whittle, H., Hamilton-Giachritsis, C., Beech, A.: Victim’s voices: The impact of online grooming and sexual abuse. Universal Journal of Psychology 1(2), 59–71 (2013)
Wolak, J., Mitchell, K., Finkelhor, D.: Online victimization of youth: Five years later. Technical report, Bulleting 07-06-025, National Center for Missing and Exploted Children, Alexadia, Alexandria, VA (2006)
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Cano, A.E., Fernandez, M., Alani, H. (2014). Detecting Child Grooming Behaviour Patterns on Social Media. In: Aiello, L.M., McFarland, D. (eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science, vol 8851. Springer, Cham. https://doi.org/10.1007/978-3-319-13734-6_30
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DOI: https://doi.org/10.1007/978-3-319-13734-6_30
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