Exploring Interactions in YouTube to Support the Identification of Crime Suspects
Resumo
The identification of crime suspects on social networks (e.g. pedophilia, terrorism, etc.) has been one of the most relevant topics in social network analysis. The vast majority of methods are based on the extraction of textual content, senders and receivers from the network, but do not take into account the interactions that occur inside the textual content. The present work raises the hypothesis that these interactions, if taken into consideration, can lead to better results when it comes to identifying crime suspects. To validate this hypothesis, an algorithm named TROY was developed to define and represent the interactions that occur in Youtube. Then, it is proposed as the first stage of the INSPECTION method, which analyzes the textual content exchanged on a social network, in order to identify crime suspects. This method is based on the use of a controlled vocabulary with terms categorized according to a certain domain (for example, pedophilia, cyberbullying, terrorism, etc.). Experiments on the pedophilia domain were carried out applying the INSPECTION method to Youtube. The first stage of the method may use any algorithm that is able to extract social interactions. In this case, we used TROY and another algorithm called CRAWLER, which does not take into account the previously mentioned interactions. The results showed that the TROY algorithm obtained better results than the CRAWLER algorithm, validating the hypothesis raised.
Palavras-chave:
online social networks, identifying suspects, social network theory (graphs), social network interaction
Referências
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Leonardo Santos and Gustavo Paiva Guedes. 2019. Identificação de predadores sexuais brasileiros por meio de análise de conversas realizadas na Internet (in Portuguese). XXXIX Congresso da Sociedade Brasileira de Computação (2019).
Simon Scheider and Peter Kiefer. 2018. (Re-) Localization of Location-Based Games. In Geogames and Geoplay. Springer, 131–159.
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Fabrício Benevenuto, Tiago Rodrigues, Virgílio Almeida, Jussara Almeida, and Marcos Gonçalves. 2009. Detecting spammers and content promoters in online video social networks. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 620–627.
Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V Chawla, Jinghai Rao, and Huanhuan Cao. 2012. Link prediction and recommendation across heterogeneous social networks. In 2012 IEEE 12th International conference on data mining. IEEE, 181–190.
Marta Dynel. 2014. Participation framework underlying YouTube interaction. Journal of Pragmatics 73(2014), 37–52.
Paul Elzinga, Karl Erich Wolff, and Jonas Poelmans. 2012. Analyzing chat conversations of pedophiles with temporal relational semantic systems. In Europ. Intel. and Security Informatics Conf., 2012 (s.n ed.). IEEE, 242–249.
Érick S Florentino, Argus AB Cavalcante, and Ronaldo R Goldschmidt. 2019. A Topological Data Evolution Based Method to Predict Links in Social Networks. In Proceedings of the XV Brazilian Symposium on Information Systems. 1–8.
Érick S Florentino, Ronaldo R Goldschmidt, and Maria Claudia Cavalcanti. 2020. Identifying Criminal Suspects on Social Networks: A Vocabulary-Based Method. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 273–276.
Jerry R Hobbs and Feng Pan. 2006. Time ontology in OWL. W3C working draft 27(2006), 133.
Jytte Klausen, Eliane Tschaen Barbieri, Aaron Reichlin-Melnick, and Aaron Y Zelin. 2012. The YouTube Jihadists: A social network analysis of Al-Muhajiroun’s propaganda campaign. Perspectives on Terrorism 6, 1 (2012), 36–53.
Clair Kronk, Giao Q Tran, and Danny TY Wu. 2019. Creating a Queer Ontology: The Gender, Sex, and Sexual Orientation (GSSO) Ontology.Studies in health technology and informatics 264 (2019), 208–212.
Zhenzhong Kuang, Jun Yu, Zongmin Li, Baopeng Zhang, and Jianping Fan. 2018. Integrating multi-level deep learning and concept ontology for large-scale visual recognition. Pattern Recognition 78(2018), 198–214.
K Hazel Kwon and Anatoliy Gruzd. 2017. Is aggression contagious online? A case of swearing on Donald Trump’s campaign videos on YouTube. In Proceedings of the 50th Hawaii International Conference on System Sciences.
Pierre Lévy and Donata Feroldi. 1999. Cybercultura: gli usi sociali delle nuove tecnologie. Feltrinelli.
Shibao Li, Junwei Huang, Zhigang Zhang, Jianhang Liu, Tingpei Huang, and Haihua Chen. 2018. Similarity-based future common neighbors model for link prediction in complex networks. Scientific reports 8, 1 (2018), 1–11.
Enrico Mariconti, Guillermo Suarez-Tangil, Jeremy Blackburn, Emiliano De Cristofaro, Nicolas Kourtellis, Ilias Leontiadis, Jordi Luque Serrano, and Gianluca Stringhini. 2019. ” You Know What to Do” Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks. Proceedings of the ACM on Human-Computer Interaction 3, CSCW(2019), 1–21.
Subhabrata Mukherjee and Sachindra Joshi. 2013. Sentiment aggregation using ConceptNet ontology. In Proceedings of the Sixth International Joint Conference on Natural Language Processing. 570–578.
Carlos PMT Muniz, Ricardo Choren, and Ronaldo R Goldschmidt. 2017. Using a Time based Relationship Weighting Criterion to Improve Link Prediction in Social Networks.. In ICEIS (1). 73–79.
Flávia Neves. n.d.. Elogios de A a Z (in Portuguese). https://www.dicio.com.br/elogios-de-a-a-z/
Nick Pendar. 2007. Toward spotting the pedophile telling victim from predator in text chats. ICSC (2007), 235–241.
Cornelius Rosse and Jose LV Mejino. 2008. The foundational model of anatomy ontology. In Anatomy Ontologies for Bioinformatics. Springer, 59–117.
Leonardo Santos and Gustavo Paiva Guedes. 2019. Identificação de predadores sexuais brasileiros por meio de análise de conversas realizadas na Internet (in Portuguese). XXXIX Congresso da Sociedade Brasileira de Computação (2019).
Simon Scheider and Peter Kiefer. 2018. (Re-) Localization of Location-Based Games. In Geogames and Geoplay. Springer, 131–159.
Esaú Villatoro-Tello, Antonio Juárez-González, Hugo Jair Escalante, Manuel Montes-y Gómez, and Luis Villasenor Pineda. 2012. A Two-step Approach for Effective Detection of Misbehaving Users in Chats.CLEF.
Érick Florentino., Ronaldo Goldschmidt., and Maria Cavalcanti.2021. Identifying Suspects on Social Networks: An Approach based on Non-structured and Non-labeled Data. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS,. INSTICC, SciTePress, 51–62. https://doi.org/10.5220/0010440300510062
Publicado
07/06/2021
Como Citar
FLORENTINO, Érick da Silva; GOLDSCHMIDT, Ronaldo Ribeiro; CAVALCANTI, Maria Cláudia Reis.
Exploring Interactions in YouTube to Support the Identification of Crime Suspects. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 17. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
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