Plataforma Colaborativa de Denúncias
Resumo
Abordagens para denunciar crimes têm sido amplamente discutidas nos últimos anos já que muitas cidades enfrentam um aumento significativo de violência. Assim, muitos esforços para encorajar os cidadãos a tornarem-se denunciantes mais ativos baseiam-se na melhoria do modelo de denúncia de crimes. Nesse trabalho, propomos a construção de uma plataforma colaborativa de denúncias com o uso de um atendente virtual, que foi projetado para coletar informações confidenciais relacionadas à segurança pública, simulando um comportamento análogo ao humano, incluindo a identificação de intenções através da semântica encontrada nos relatos de crimes.Referências
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BRANDTZÆG, Petter; FØLSTAD, Asbjørn. Why People Use Chatbots. The 4th International Conference on Internet Science. 2017.
BRANTINGHAM, Jeffrey; MOHLER, George. Does Predictive Policing Lead to Biased Arrests? Results from a Randomized Controlled Trial. Statistics and Public Policy, n. 5, p. 11-17, 2018.
CHAINEY, Spencer et al. The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime. Security Journal, n. 21, p. 4-28, 2008.
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CIECHANOWSKI, Leon et al. In the shades of the Uncanny Valley: An Experimental Study of Human–Chatbot Interaction. Future Generation Computer Systems. v. 92, p. 539-548. 2018.
ELMESSIRY, Adel et al. Leveraging sentiment analysis for classifying patient complaints. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2017. p. 44-51.
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JIMOH, Rasheed et al. A Scalable Online Crime Reporting System, African Journal of Computing & ICT, v. 7, n. 1, 2014.
KANABLE, R. Talking to tipsters: Websites and text messages allow valuable, anonymous communication, Law Enforcement Technology, v. 35, n. 11, p. 10-12, 2008.
KUN, Chih Hao; LEROY, Gondy. A Decision Support System: Automated Crime Report Analysis and Classification for E-Government. Government Information Quarterly, v. 31, n. 4, p. 534-544, 2014.
LASLEY, James; PALOMBO, Bernadette Jones. When Crime Reporting Goes HighTech: An Experimental Test of Computerized Citizen Response to Crime. Journal of Criminal Justice, v. 23, n. 6, p. 519-529, 1995.
PEFFERS, Ken et al. A design science research methodology for information systems research. Journal of management information systems, v. 24, n. 3, p. 45-77, 2007.
PESTIAN, John et al. Using natural language processing to classify suicide notes. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing. 2008. p. 96-97.
RESHMI, S.; BALAKRISHNAN, Kannan. Implementation of an inquisitive chatbot for database supported knowledge bases. sādhanā, v. 41, n. 10, p. 1173-1178, 2016.
RODRÍGUEZ, Juan Manuel; MERLINO, Hernán; FERNÁNDEZ, Enrique. Comportamiento Adaptable de Chatbots Dependiente del Contexto. Revista Latinoamericana de Ingenieria de Software, v. 2, n. 2, p. 115-136, 2014.
SANO, Yuta; MINE, Tsunenori. Extraction of current actual status and demand expressions from complaint reports. In: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services. 2016. p. 149-153.
SONG, Wonsong et al. Using IM and SMS for Emergency Text Communications. 3rd International Conference on Principles, Systems and Applications of IP Telecommunications. n.4. p. 1-7.
SRIVASTAVA, Saurabh; PRABHAKAR, T. V. A Reference Architecture for Applications with Conversational Components. In: 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2019. p. 1-5.
SUNDAR, Shyam; LIMPEROS, Anthony. Uses and Grats 2.0: New Gratifications for New Media. Journal of Broadcasting & Electronic Media, v. 57, 2013.
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BRANDTZÆG, Petter; FØLSTAD, Asbjørn. Why People Use Chatbots. The 4th International Conference on Internet Science. 2017.
BRANTINGHAM, Jeffrey; MOHLER, George. Does Predictive Policing Lead to Biased Arrests? Results from a Randomized Controlled Trial. Statistics and Public Policy, n. 5, p. 11-17, 2018.
CHAINEY, Spencer et al. The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime. Security Journal, n. 21, p. 4-28, 2008.
CHAUDHURI et al. An Overview of Business Intelligence Technology. ACM, N. 8., Vol. 54, 2011, 88-98.
CIECHANOWSKI, Leon et al. In the shades of the Uncanny Valley: An Experimental Study of Human–Chatbot Interaction. Future Generation Computer Systems. v. 92, p. 539-548. 2018.
ELMESSIRY, Adel et al. Leveraging sentiment analysis for classifying patient complaints. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2017. p. 44-51.
GAGLIO, Salvatore et al. Smart assistance for students and people living in a campus. In: 2019 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2019. p. 132-137.
JIMOH, Rasheed et al. A Scalable Online Crime Reporting System, African Journal of Computing & ICT, v. 7, n. 1, 2014.
KANABLE, R. Talking to tipsters: Websites and text messages allow valuable, anonymous communication, Law Enforcement Technology, v. 35, n. 11, p. 10-12, 2008.
KUN, Chih Hao; LEROY, Gondy. A Decision Support System: Automated Crime Report Analysis and Classification for E-Government. Government Information Quarterly, v. 31, n. 4, p. 534-544, 2014.
LASLEY, James; PALOMBO, Bernadette Jones. When Crime Reporting Goes HighTech: An Experimental Test of Computerized Citizen Response to Crime. Journal of Criminal Justice, v. 23, n. 6, p. 519-529, 1995.
PEFFERS, Ken et al. A design science research methodology for information systems research. Journal of management information systems, v. 24, n. 3, p. 45-77, 2007.
PESTIAN, John et al. Using natural language processing to classify suicide notes. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing. 2008. p. 96-97.
RESHMI, S.; BALAKRISHNAN, Kannan. Implementation of an inquisitive chatbot for database supported knowledge bases. sādhanā, v. 41, n. 10, p. 1173-1178, 2016.
RODRÍGUEZ, Juan Manuel; MERLINO, Hernán; FERNÁNDEZ, Enrique. Comportamiento Adaptable de Chatbots Dependiente del Contexto. Revista Latinoamericana de Ingenieria de Software, v. 2, n. 2, p. 115-136, 2014.
SANO, Yuta; MINE, Tsunenori. Extraction of current actual status and demand expressions from complaint reports. In: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services. 2016. p. 149-153.
SONG, Wonsong et al. Using IM and SMS for Emergency Text Communications. 3rd International Conference on Principles, Systems and Applications of IP Telecommunications. n.4. p. 1-7.
SRIVASTAVA, Saurabh; PRABHAKAR, T. V. A Reference Architecture for Applications with Conversational Components. In: 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2019. p. 1-5.
SUNDAR, Shyam; LIMPEROS, Anthony. Uses and Grats 2.0: New Gratifications for New Media. Journal of Broadcasting & Electronic Media, v. 57, 2013.
WEIZENBAUM, Joseph. ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, v. 9, n. 1, p. 36-45, 1966.
Publicado
22/05/2023
Como Citar
DE FERRAN, Cristianna M.; COSTA, Alexandre F.; DE BRITO, Walkir A. T.; MOTTA, Claudia L. R..
Plataforma Colaborativa de Denúncias. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 18. , 2023, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 169-182.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2023.229105.
