Avaliando um Mecanismo de Consenso no Processo de Perícia de Desinformação através de Simulação
Resumo
A desinformação é uma preocupação contemporânea latente, impulsionada principalmente pela sua disseminação nas redes sociais. Embora as técnicas de inteligência artificial (IA) acelerem o processo de detecção, tais técnicas ainda exigem a participação humana no processo de verificação. Checadores de fatos são peritos que desempenham um papel crucial, mas o processo pode ser lento e ineficiente. Neste sentido, um projeto de P&D está sendo desenvolvido numa parceria entre a ANATEL e a UFG, criando uma prova de conceito (PoC) que agilize o processo de verificação, permitindo que múltiplos checadores trabalhem em paralelo. A contribuição principal deste artigo é demonstrar, através de simulações, a eficácia do mecanismo de consenso que apoia o trabalho dos checadores antes de sua implantação. Resultados preliminares sugerem que o mecanismo de consenso baseado em maioria por ponderação possibilita que fact-checkers com mais relevância influenciem mais significativamente o resultado da avaliação, ainda que em alguns casos avaliadores com importância baixa possam anular a importância de um ou poucos fact-checkers com mais relevância.Referências
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Bodaghi, A., Schmitt, K. A., Watine, P., and Fung, B. C. (2023). A literature review on detecting, verifying, and mitigating online misinformation. IEEE Transactions on Computational Social Systems.
Boovitha, D., Abirami, M., Gunavathi, S., Revathi, N., and Rubavarshini, S. (2023). Fake media detection based on natural language processing and blockchain approaches. South Asian Journal of Engineering and Technology, 13(1):69–82.
Cavalcante, A. A. B., Freire, P. M. S., Goldschmidt, R. R., and Justel, C. M. (2024). Improving implicit crowd signals based fake news detection on social media: A time-aware method for early detection. In 20th SBSI 2024, pages 7:1–7:9, Juiz de Fora, Brazil. ACM.
Choi, N. and Kim, H. (2023). Dds: Deepfake detection system through collective intelligence and deep-learning model in blockchain environment. Applied Sciences, 13(4):2122.
de França, B. B. N. and Travassos, G. H. (2016). Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines. Empirical Software Engineering, 21(3):1302–1345.
Faridi, A. R., Singh, R., Masood, F., and Salmony, M. Y. (2023). Machine learning based novel framework for fake news detection and prevention using blockchain. In 10th INDIACom, pages 751–755. IEEE.
Graciano-Neto, V. V., Barbosa, J., Lima, E., Carvalho, S., and Venzi, S. (2024a). A Blockchain-based and AI-Endorsed Mechanism to Support Social Networks on Fake News Containment. In XIII BraSNAM, pages 207–213, Brasília/DF. SBC.
Graciano-Neto, V. V., Barbosa, J. R., de Lima, E. A., de Freitas Cintra, L. M., Venzi, S., and Kassab, M. (2024b). Establishing a blockchain-based architecture for fake news detection. In SBCARS, pages 1–10, Curitiba/PR. SBC.
Iavernaro, F., La Scala, M., and Mazzia, F. (1998). Boundary values methods for time-domain simulation of power system dynamic behavior. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 45(1):50–63.
Jaroucheh, Z., Alissa, M., Buchanan, W. J., and Liu, X. (2020). Trustd: Combat fake content using blockchain and collective signature technologies. In 44th COMPSAC, pages 1235–1240. IEEE.
Jiang, Y. and Porter, M. D. (2022). Simulating fake news dissemination on twitter with multivariate hawkes processes. In IEEE BigData, pages 3597–3606.
Morais, J. I. d., Abonizio, H. Q., Tavares, G. M., da Fonseca, A. A., and Jr, S. B. (2020). A multi-label classification system to distinguish among fake, satirical, objective and legitimate news in brazilian portuguese. iSys - Brazilian Journal of Information Systems, 13(4):126–149.
Naik, K. and Tripathy, P. (2011). Software testing and quality assurance: theory and practice. John Wiley & Sons.
Nolasco, D. and Oliveira, J. (2021). Topical rumor detection based on social network topic models relationship. iSys - Brazilian Journal of Information Systems, 14(2):05–27.
Oyinloye, D. P., Teh, J. S., Jamil, N., and Teh, J. (2023). Sim-p—a simplified consensus protocol simulator: Applications to proof of reputation-x and proof of contribution. IEEE Internet of Things Journal, 10(6):5083–5094.
Rani, P., Jain, V., Shokeen, J., and Balyan, A. (2022). Blockchain-based rumor detection approach for covid-19. Journal of Ambient Intelligence and Humanized Computing, pages 1–15.
Sengupta, E., Nagpal, R., Mehrotra, D., and Srivastava, G. (2021). Problock: a novel approach for fake news detection. Cluster Computing, 24:3779–3795.
Shao, X., Ma, X., Chen, F., Song, S., Pan, X., and You, K. (2020). A random parameters ordered probit analysis of injury severity in truck involved rear-end collisions. International Journal of Environmental Research and Public Health, 17:395.
Tajrian, M., Rahman, A., Kabir, M. A., and Islam, M. R. (2023). A review of methodologies for fake news analysis. IEEE Access.
Torky, M., Nabil, E., and Said, W. (2019). Proof of credibility: A blockchain approach for detecting and blocking fake news in social networks. IJACSA, 10(12).
Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology innovation management review, 9(11).
World Economic Forum (2024). Global risks 2024: Disinformation tops global risks 2024 as environmental threats intensify. [link]. Accessed: [June, 2024].
Yu, P., Xia, Z., Fei, J., and Lu, Y. (2021). A survey on deepfake video detection. Iet Biometrics, 10(6):607–624.
Publicado
30/09/2024
Como Citar
GRACIANO NETO, Valdemar Vicente; CINTRA, Luiza Martins de Freitas; DAMACENA, Pedro Henrique Campos; ROCHA, Acquila Santos; BORGES, Vinícius Cunha M.; BARBOSA, Jacson Rodrigues; LIMA, Eliomar Araújo de.
Avaliando um Mecanismo de Consenso no Processo de Perícia de Desinformação através de Simulação. In: WORKSHOP EM MODELAGEM E SIMULAÇÃO DE SISTEMAS INTENSIVOS EM SOFTWARE (MSSIS), 6. , 2024, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
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p. 1-10.
DOI: https://doi.org/10.5753/mssis.2024.3668.
