Identificação de predadores sexuais brasileiros por meio de análise de conversas realizadas na Internet
Resumo
Nos dias de hoje, aplicações sociais representam uma dos principais ameaças a crianças e adolescentes na Internet. Dentre os diversos riscos existentes destaca-se a presença de predadores sexuais que buscam, entre os mais diversos fins, obter conteúdo pornográfico infantil, extorquir com finalidades financeiras e, em cenários mais graves, o abuso sexual. O presente trabalho tem como objetivo identificar predadores sexuais brasileiros por meio de Redes Neurais Convolucionais. Para atingir esse objetivo, são consideradas conversas provenientes de provas criminais disponibilizadas recentemente ao público. Resultados preliminares consolidam a metodologia apresentada como uma alternativa para tarefas de classificação binária de textos para a lı́ngua portuguesa.
Palavras-chave:
Data Mining, Text Classification, PAN-2012, Identificação de predador sexual, Machine Learning, Redes Neurais Convolucionais, Redes sociais, Chat Logs
Referências
Andrijauskas, A., Shimabukuro, A., and Maia, R. F. (2017). Desenvolvimento de base de dados em lı́ngua portuguesa sobre crimes sexuais. VII Simpósio de Iniciação Cientı́fica, Didática e de Ações Sociais da FEI.
BARBOSA, A. F. (2018). Pesquisa sobre o uso da internet por crianças e adolescentes no brasil: Tic kids online brasil 2017. São Paulo: Comitê Gestor da Internet no Brasil.
Bengel, J., Gauch, S., Mittur, E., and Vijayaraghavan, R. (2004). Chattrack: Chat room topic detection using classification. In ISI.
Bogdanova, D., Rosso, P., and Solorio, T. (2014). Exploring high-level features for de- tecting cyberpedophilia. Computer speech & language, 28(1):108–120.
Cardei, C. and Rebedea, T. (2017). Detecting sexual predators in chats using behavioral features and imbalanced learning. Natural Language Engineering, 23(4):589–616.
Dorasamy, M., Jambulingam, M., and Vigian, T. (2018). Building a bright society with au courant parents: Combating online grooming.
dos Santos, L. F. and Guedes, G. P. (2018). Detecção de traços de narcisismo em conversas com predadores sexuais. In 7 o Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2018), volume 7. SBC.
Ebrahimi, M., Suen, C. Y., and Ormandjieva, O. (2016). Detecting predatory conver- sations in social media by deep convolutional neural networks. Digital Investigation, 18:33–49.
Forsythand, E. N. and Martell, C. H. (2007). Lexical and discourse analysis of online chat dialog. In International Conference on Semantic Computing (ICSC 2007), pages 19–26. IEEE.
Inches, G. and Crestani, F. (2012). Overview of the international sexual predator iden- tification competition at pan-2012. In CLEF (Online working notes/labs/workshop), volume 30.
Johnson, R. and Zhang, T. (2015). Effective use of word order for text categorization with convolutional neural networks. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103–112.
Kloess, J. A., Hamilton-Giachritsis, C. E., and Beech, A. R. (2019). Offense processes of online sexual grooming and abuse of children via internet communication platforms. Sexual Abuse, 31(1):73–96.
Kontostathis, A. (2009). Chatcoder: Toward the tracking and categorization of internet predators. In Proceedings of Text Mining Workshop 2009 held in conjunction with the Ninth SIAM International Conference on Data Mining (SDM 2009). SPARKS, NV. May 2009. Citeseer.
Kontostathis, A., Garron, A., Reynolds, K., West, W., and Edwards, L. (2012). Identifying predators using chatcoder 2.0. In CLEF (Online Working Notes/Labs/Workshop).
NCMEC (2017). The online enticement of children: An in- depth analysis of cybertipline reports. National Center for Missing & Exploited Children Web site., https://missingkids- stage.adobecqms.net/ourwork/publications/exploitation/onlineenticement. Acessado em 16 de março de 2019.
Ngejane, C., Mabuza-Hocquet, G., Eloff, J., and Lefophane, S. (2018). Mitigating online sexual grooming cybercrime on social media using machine learning: A desktop sur- vey. In 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), pages 1–6. IEEE.
Olowu, D. (2014). Cyber-based obscenity and the sexual exploitation of children via the internet: Implications for africa. In African Cyber Citizenship Conference 2014 (ACCC2014), page 115.
Olson, L. N., Daggs, J. L., Ellevold, B. L., and Rogers, T. K. (2007). Entrapping the innocent: Toward a theory of child sexual predators’ luring communication. Commu- nication Theory, 17(3):231–251.
Pendar, N. (2007). Toward spotting the pedophile telling victim from predator in text chats. In International Conference on Semantic Computing (ICSC 2007), pages 235– 241. IEEE.
Villatoro-Tello, E., Juárez-González, A., Escalante, H. J., Montes-y Gómez, M., and Pi- neda, L. V. (2012). A two-step approach for effective detection of misbehaving users in chats. In CLEF (Online Working Notes/Labs/Workshop), volume 1178.
BARBOSA, A. F. (2018). Pesquisa sobre o uso da internet por crianças e adolescentes no brasil: Tic kids online brasil 2017. São Paulo: Comitê Gestor da Internet no Brasil.
Bengel, J., Gauch, S., Mittur, E., and Vijayaraghavan, R. (2004). Chattrack: Chat room topic detection using classification. In ISI.
Bogdanova, D., Rosso, P., and Solorio, T. (2014). Exploring high-level features for de- tecting cyberpedophilia. Computer speech & language, 28(1):108–120.
Cardei, C. and Rebedea, T. (2017). Detecting sexual predators in chats using behavioral features and imbalanced learning. Natural Language Engineering, 23(4):589–616.
Dorasamy, M., Jambulingam, M., and Vigian, T. (2018). Building a bright society with au courant parents: Combating online grooming.
dos Santos, L. F. and Guedes, G. P. (2018). Detecção de traços de narcisismo em conversas com predadores sexuais. In 7 o Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2018), volume 7. SBC.
Ebrahimi, M., Suen, C. Y., and Ormandjieva, O. (2016). Detecting predatory conver- sations in social media by deep convolutional neural networks. Digital Investigation, 18:33–49.
Forsythand, E. N. and Martell, C. H. (2007). Lexical and discourse analysis of online chat dialog. In International Conference on Semantic Computing (ICSC 2007), pages 19–26. IEEE.
Inches, G. and Crestani, F. (2012). Overview of the international sexual predator iden- tification competition at pan-2012. In CLEF (Online working notes/labs/workshop), volume 30.
Johnson, R. and Zhang, T. (2015). Effective use of word order for text categorization with convolutional neural networks. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 103–112.
Kloess, J. A., Hamilton-Giachritsis, C. E., and Beech, A. R. (2019). Offense processes of online sexual grooming and abuse of children via internet communication platforms. Sexual Abuse, 31(1):73–96.
Kontostathis, A. (2009). Chatcoder: Toward the tracking and categorization of internet predators. In Proceedings of Text Mining Workshop 2009 held in conjunction with the Ninth SIAM International Conference on Data Mining (SDM 2009). SPARKS, NV. May 2009. Citeseer.
Kontostathis, A., Garron, A., Reynolds, K., West, W., and Edwards, L. (2012). Identifying predators using chatcoder 2.0. In CLEF (Online Working Notes/Labs/Workshop).
NCMEC (2017). The online enticement of children: An in- depth analysis of cybertipline reports. National Center for Missing & Exploited Children Web site., https://missingkids- stage.adobecqms.net/ourwork/publications/exploitation/onlineenticement. Acessado em 16 de março de 2019.
Ngejane, C., Mabuza-Hocquet, G., Eloff, J., and Lefophane, S. (2018). Mitigating online sexual grooming cybercrime on social media using machine learning: A desktop sur- vey. In 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), pages 1–6. IEEE.
Olowu, D. (2014). Cyber-based obscenity and the sexual exploitation of children via the internet: Implications for africa. In African Cyber Citizenship Conference 2014 (ACCC2014), page 115.
Olson, L. N., Daggs, J. L., Ellevold, B. L., and Rogers, T. K. (2007). Entrapping the innocent: Toward a theory of child sexual predators’ luring communication. Commu- nication Theory, 17(3):231–251.
Pendar, N. (2007). Toward spotting the pedophile telling victim from predator in text chats. In International Conference on Semantic Computing (ICSC 2007), pages 235– 241. IEEE.
Villatoro-Tello, E., Juárez-González, A., Escalante, H. J., Montes-y Gómez, M., and Pi- neda, L. V. (2012). A two-step approach for effective detection of misbehaving users in chats. In CLEF (Online Working Notes/Labs/Workshop), volume 1178.
Publicado
09/07/2019
Como Citar
DOS SANTOS, Leonardo Ferreira; GUEDES, Gustavo Paiva .
Identificação de predadores sexuais brasileiros por meio de análise de conversas realizadas na Internet. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 8. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 143-154.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2019.6556.
