Combatendo Fake News nas Redes Sociais via Crowd Signals Implícitos
Resumo
A disseminação de Fake News é um problema conhecido nas redes sociais. Uma das principais abordagens para detectar, automaticamente, este tipo de notícia é baseada na reputação, em especial a que utiliza Crowd Signals. Embora promissora, esta abordagem depende de informações nem sempre disponíveis: a opinião explícita dos usuários sobre as notícias serem fake ou não. Para superar esta desvantagem, este artigo propõe um método, baseado em Crowd Signals Implícitos, que não exige a opinião explícita dos usuários. Experimentos forneceram evidências de que o método proposto pode detectar Fake News sem exigir a opinião explícita dos usuários e sem comprometer os resultados obtidos pelo estado da arte dos métodos baseados em Crowd Signals.
Palavras-chave:
Fake news, social media, crowd signals
Referências
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Farajtabar, M., Yang, J., Ye, X., Xu, H., Trivedi, R., Khalil, E., Li, S., Song, L., and Zha, H. (2017). Fake news mitigation via point process based intervention. In Int Con on Machine Learning.
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Moore, T. and Clayton, R. (2008). Financial cryptography and data security. chapter Evaluating the Wisdom of Crowds in Assessing Phishing Websites.
Nasim, M., Nguyen, A., Lothian, N., Cope, R., and Mitchell, L. (2018). Real-time detection of content polluters in partially observable twitter networks. In Comp. Proc. of the The Web Con.
Pérez-Rosas, V., B. Kleinberg, A. L., and Mihalcea, R. (2018). Automatic detection of fake news. In Int Con on Comput. Linguistics.
Qian, F., Gong, C., Sharma, K., and Liu, Y. (2018). Neural user response generator: Fake news detection with collective user intelligence. In Int Joint Con on Artificial Intelligence.
Rubin, V. L., Conroy, N. J., and Chen, Y. (2015). Towards news verification: Deception detection methods for news discourse.
Ruchansky, N., Seo, S., and Liu, Y. (2017). Csi: A hybrid deep model for fake news detection. In ACM on Con on Inform. and Knowledge Management.
Sethi, R. J. (2017). Crowdsourcing the verification of fake news and alternative facts. In ACM Con on Hypertext and Social Media.
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., and Liu, Y. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol.
Shu, K., Mahudeswaran, D., and Liu, H. (2019). Fakenewstracker:a tool for fake news collection,detection, and visualization. Com. Mat. Or. The.
Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017a). Fake news detection on social media: A data mining perspective. SIGKDD Ex. New.
Shu, K., Wang, S., and Liu, H. (2017b). Exploiting tri-relationship for fake news detection. In arXiv.
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., and Krause, A. (2018). Fake news detection in social networks via crowd signals. In Comp. Proc. of the The Web Con.
Vosoughi, S., Mohsenvand, M. N., and Roy, D. (2017). Rumor gauge: Predicting the veracity of rumors on twitter. ACM Trans. Know. Disc. Data.
Wang, W. Y. (2017). “liar, liar pants on fire”: A new benchmark dataset for fake news detection. In The Assoc. for Comput. Linguistics.
Woloszyn, V. and Nejdl, W. (2018). Distrustrank: Spotting false news domains. In ACM Con on Web Science.
Wu, L. and Liu, H. (2018). Tracing fake-news footprints: Characterizing social media messages by how they propagate. In ACM Int Con on Web Search and Data Mining.
Zhang, Q., Yilmaz, E., and Liang, S. (2018). Ranking-based method for news stance detection. In Comp. Proc. of the The Web Con.
Buntain, C. and Golbeck, J. (2017). Automatically identifying fake news in popular twitter threads. In IEEE Int Con on Smart Cloud.
Chia, P. H. and Knapskog, S. J. (2012). Re-evaluating the wisdom of crowds in assessing web security. In The 15th Int Con on Financial Cryptography and Data Security, Berlin, Heidelberg. Springer-Verlag.
Farajtabar, M., Yang, J., Ye, X., Xu, H., Trivedi, R., Khalil, E., Li, S., Song, L., and Zha, H. (2017). Fake news mitigation via point process based intervention. In Int Con on Machine Learning.
Freeman, D. M. (2017). Can you spot the fakes?: On the limitations of user feedback in online social networks. In Int Con on WWW.
Gilda, S. (2017). Evaluating machine learning algorithms for fake news detection. In IEEE Student Con on Research and Development.
Janze, C. and Risius, M. (2017). Automatic detection of fake news on social media platforms. In PACIS.
Kim, J., Tabibian, B., Oh, A., Schölkopf, B., and Gomez-Rodriguez, M. (2018). Leverging the crowd to detect and reduce the spread of fake news and misinformation. In ACM Int Con on Web Search and Data Mining.
Liu, Y. and BrookWu, Y. (2018). Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In AAAI Con on Artificial Intelligence.
Moore, T. and Clayton, R. (2008). Financial cryptography and data security. chapter Evaluating the Wisdom of Crowds in Assessing Phishing Websites.
Nasim, M., Nguyen, A., Lothian, N., Cope, R., and Mitchell, L. (2018). Real-time detection of content polluters in partially observable twitter networks. In Comp. Proc. of the The Web Con.
Pérez-Rosas, V., B. Kleinberg, A. L., and Mihalcea, R. (2018). Automatic detection of fake news. In Int Con on Comput. Linguistics.
Qian, F., Gong, C., Sharma, K., and Liu, Y. (2018). Neural user response generator: Fake news detection with collective user intelligence. In Int Joint Con on Artificial Intelligence.
Rubin, V. L., Conroy, N. J., and Chen, Y. (2015). Towards news verification: Deception detection methods for news discourse.
Ruchansky, N., Seo, S., and Liu, Y. (2017). Csi: A hybrid deep model for fake news detection. In ACM on Con on Inform. and Knowledge Management.
Sethi, R. J. (2017). Crowdsourcing the verification of fake news and alternative facts. In ACM Con on Hypertext and Social Media.
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., and Liu, Y. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol.
Shu, K., Mahudeswaran, D., and Liu, H. (2019). Fakenewstracker:a tool for fake news collection,detection, and visualization. Com. Mat. Or. The.
Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017a). Fake news detection on social media: A data mining perspective. SIGKDD Ex. New.
Shu, K., Wang, S., and Liu, H. (2017b). Exploiting tri-relationship for fake news detection. In arXiv.
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., and Krause, A. (2018). Fake news detection in social networks via crowd signals. In Comp. Proc. of the The Web Con.
Vosoughi, S., Mohsenvand, M. N., and Roy, D. (2017). Rumor gauge: Predicting the veracity of rumors on twitter. ACM Trans. Know. Disc. Data.
Wang, W. Y. (2017). “liar, liar pants on fire”: A new benchmark dataset for fake news detection. In The Assoc. for Comput. Linguistics.
Woloszyn, V. and Nejdl, W. (2018). Distrustrank: Spotting false news domains. In ACM Con on Web Science.
Wu, L. and Liu, H. (2018). Tracing fake-news footprints: Characterizing social media messages by how they propagate. In ACM Int Con on Web Search and Data Mining.
Zhang, Q., Yilmaz, E., and Liang, S. (2018). Ranking-based method for news stance detection. In Comp. Proc. of the The Web Con.
Publicado
15/10/2019
Como Citar
FREIRE, Paulo; GOLDSCHMIDT, Ronaldo.
Combatendo Fake News nas Redes Sociais via Crowd Signals Implícitos. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 424-435.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9303.
