Detecção de Fake News em Domínios Cruzados: Uma Revisão Sistemática
Resumo
Este artigo apresenta uma revisão sistemática sobre a detecção de fake news em domínios cruzados, em que o desafio é identificar desinformação em contextos variados, como diferentes temas, idiomas ou fontes. A revisão revela uma preferência pela Generalização de Domínio (DG), que busca desenvolver modelos capazes de identificar fake news em uma ampla gama de contextos sem ajustes específicos, em detrimento da Adaptação de Domínio (DA), que visa otimizar o desempenho de um modelo treinado em um domínio fonte para domínios-alvo específicos. A diversidade dos conjuntos de dados utilizados ressalta a necessidade de benchmarks padronizados para avaliações consistentes. O estudo sugere a exploração de novas técnicas de generalização e adaptação de domínio para aprimorar a detecção de fake news em diferentes contextos.Referências
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Gautam, A. and Jerripothula, K. R. (2020). Sgg: Spinbot, grammarly and glove based fake news detection. In 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), pages 174–182.
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Guo, Y., Ge, H., and Li, J. (2022). Fake news detection based on two-branch network and domain adversarial. In 2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET), pages 172–176.
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Joshi, G., Srivastava, A., Yagnik, B., Hasan, M., Saiyed, Z., Gabralla, L. A., Abraham, A., Walambe, R., and Kotecha, K. (2023). Explainable misinformation detection across multiple social media platforms. IEEE Access, 11:23634–23646.
Kato, S., Yang, L., and Ikeda, D. (2022). Domain bias in fake news datasets consisting of fake and real news pairs. In 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI), pages 101–106.
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Kong, J. T. H., Wong, W. K., Juwono, F. H., and Apriono, C. (2023). Generating fake news detection model using a two-stage evolutionary approach. IEEE Access, 11:85067–85085.
Lazer, D., Baum, M., Benkler, Y., Berinsky, A., Greenhill, K., Menczer, F., Metzger, M., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S., Sunstein, C., Thorson, E., Watts, D., and Zittrain, J. (2018). The science of fake news. Science, 359:1094–1096.
Li, Y., Lee, K., Kordzadeh, N., Faber, B., Fiddes, C., Chen, E., and Shu, K. (2021). Multisource domain adaptation with weak supervision for early fake news detection. In 2021 IEEE International Conference on Big Data (Big Data), pages 668–676.
Liu, H., Wang, W., Sun, H., Rocha, A., and Li, H. (2024). Robust domain misinformation detection via multi-modal feature alignment. IEEE Transactions on Information Forensics and Security, 19:793–806.
Lu, M., Huang, Z., Li, B., Zhao, Y., Qin, Z., and Li, D. (2022). Sifter: A framework for robust rumor detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30:429–442.
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Rastogi, S., Gill, S. S., and Bansal, D. (2021). An adaptive approach for fake news detection in social media: Single vs cross domain. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI), pages 1401–1405.
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Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19.
Sicilia, R., Francini, L., and Soda, P. (2021). Representation and knowledge transfer for health-related rumour detection. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pages 591–596.
Suprem, A., Vaidya, S., and Pu, C. (2022). Exploring generalizability of fine-tuned models for fake news detection. In 2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC), pages 82–88.
Tang, W., Ma, Z., Sun, H., and Wang, J. (2023). Learning sparse alignments via optimal transport for cross-domain fake news detection. In ICASSP 2023 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5.
Wang, D., Zhang, W., Wu, W., and Guo, X. (2023a). Soft-label for multi-domain fake news detection. IEEE Access, 11:98596–98606.
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., and Yu, P. S. (2023b). Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge & Data Engineering, 35(08):8052–8072.
Wang, X., Li, X., Liu, X., and Cheng, H. (2022). Using albert and multi-modal circulant fusion for fake news detection. In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2936–2942.
Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., and Gao, J. (2018). Eann: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18, page 849–857, New York, NY, USA. Association for Computing Machinery.
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Wardle, C. and Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policymaking, volume 27. Council of Europe Strasbourg.
Yu, W., Ge, J., Yang, Z., Dong, Y., Zheng, Y., and Dai, H. (2022). Multi-domain fake news detection for history news environment perception. In 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), pages 428–433.
Zeng, H., Yue, Z., Kou, Z., Shang, L., Zhang, Y., and Wang, D. (2022). Unsupervised domain adaptation for covid-19 information service with contrastive adversarial domain mixup. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 159–162.
Zhang, H., Fang, Q., Qian, S., and Xu, C. (2019). Multi-modal knowledge-aware event memory network for social media rumor detection. In Proceedings of the 27th ACM International Conference on Multimedia, MM ’19, page 1942–1951, New York, NY, USA. Association for Computing Machinery.
Zhang, H., Qian, S., Fang, Q., and Xu, C. (2021). Multimodal disentangled domain adaption for social media event rumor detection. IEEE Transactions on Multimedia, 23:4441–4454.
Zhang, T., Wang, D., Chen, H., Zeng, Z., Guo, W., Miao, C., and Cui, L. (2020). Bdann: Bert-based domain adaptation neural network for multi-modal fake news detection. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Zhou, X. and Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53.
Zhu, Y., Sheng, Q., Cao, J., Nan, Q., Shu, K., Wu, M., Wang, J., and Zhuang, F. (2023). Memory-guided multi-view multi-domain fake news detection. IEEE Transactions on Knowledge and Data Engineering, 35(7):7178–7191.
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. (2010). A theory of learning from different domains. Machine Learning, 79:151–175.
Birunda, S. S. and Devi, R. K. (2021). A novel score-based multi-source fake news detection using gradient boosting algorithm. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pages 406–414.
Blackledge, C. and Atapour-Abarghouei, A. (2021). Transforming fake news: Robust generalisable news classification using transformers. In 2021 IEEE International Conference on Big Data (Big Data), pages 3960–3968.
Ferreira, G., Santos, B., do Ó, M., Braz, R., and Digiampietri, L. (2021). Social bots detection in brazilian presidential elections using natural language processing. Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI).
Gautam, A. and Jerripothula, K. R. (2020). Sgg: Spinbot, grammarly and glove based fake news detection. In 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), pages 174–182.
Goel, P., Singhal, S., Aggarwal, S., and Jain, M. (2021). Multi domain fake news analysis using transfer learning. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pages 1230–1237.
Guo, Y., Ge, H., and Li, J. (2022). Fake news detection based on two-branch network and domain adversarial. In 2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET), pages 172–176.
Han, S., Gao, J., and Ciravegna, F. (2019). Neural language model based training data augmentation for weakly supervised early rumor detection. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 105–112.
Joshi, G., Srivastava, A., Yagnik, B., Hasan, M., Saiyed, Z., Gabralla, L. A., Abraham, A., Walambe, R., and Kotecha, K. (2023). Explainable misinformation detection across multiple social media platforms. IEEE Access, 11:23634–23646.
Kato, S., Yang, L., and Ikeda, D. (2022). Domain bias in fake news datasets consisting of fake and real news pairs. In 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI), pages 101–106.
Kim, Y., Kim, H. K., Kim, H., and Hong, J. B. (2020). Do many models make light work? evaluating ensemble solutions for improved rumor detection. IEEE Access, 8:150709–150724.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Kong, J. T. H., Wong, W. K., Juwono, F. H., and Apriono, C. (2023). Generating fake news detection model using a two-stage evolutionary approach. IEEE Access, 11:85067–85085.
Lazer, D., Baum, M., Benkler, Y., Berinsky, A., Greenhill, K., Menczer, F., Metzger, M., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S., Sunstein, C., Thorson, E., Watts, D., and Zittrain, J. (2018). The science of fake news. Science, 359:1094–1096.
Li, Y., Lee, K., Kordzadeh, N., Faber, B., Fiddes, C., Chen, E., and Shu, K. (2021). Multisource domain adaptation with weak supervision for early fake news detection. In 2021 IEEE International Conference on Big Data (Big Data), pages 668–676.
Liu, H., Wang, W., Sun, H., Rocha, A., and Li, H. (2024). Robust domain misinformation detection via multi-modal feature alignment. IEEE Transactions on Information Forensics and Security, 19:793–806.
Lu, M., Huang, Z., Li, B., Zhao, Y., Qin, Z., and Li, D. (2022). Sifter: A framework for robust rumor detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30:429–442.
Mosallanezhad, A., Karami, M., Shu, K., Mancenido, M. V., and Liu, H. (2022). Domain adaptive fake news detection via reinforcement learning. In Proceedings of the ACM Web Conference 2022, WWW ’22, page 3632–3640, New York, NY, USA. Association for Computing Machinery.
Omrani, P., Ebrahimian, Z., Toosi, R., and Akhaee, M. A. (2023). Bilingual covid-19 fake news detection based on lda topic modeling and bert transformer. In 2023 6th International Conference on Pattern Recognition and Image Analysis (IPRIA), pages 01–06.
Rastogi, S., Gill, S. S., and Bansal, D. (2021). An adaptive approach for fake news detection in social media: Single vs cross domain. In 2021 International Conference on Computational Science and Computational Intelligence (CSCI), pages 1401–1405.
Shang, L., Zhang, Y., Yue, Z., Choi, Y., Zeng, H., and Wang, D. (2022). A knowledgedriven domain adaptive approach to early misinformation detection in an emergent health domain on social media. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 34–41.
Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19.
Sicilia, R., Francini, L., and Soda, P. (2021). Representation and knowledge transfer for health-related rumour detection. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pages 591–596.
Suprem, A., Vaidya, S., and Pu, C. (2022). Exploring generalizability of fine-tuned models for fake news detection. In 2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC), pages 82–88.
Tang, W., Ma, Z., Sun, H., and Wang, J. (2023). Learning sparse alignments via optimal transport for cross-domain fake news detection. In ICASSP 2023 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5.
Wang, D., Zhang, W., Wu, W., and Guo, X. (2023a). Soft-label for multi-domain fake news detection. IEEE Access, 11:98596–98606.
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., and Yu, P. S. (2023b). Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge & Data Engineering, 35(08):8052–8072.
Wang, X., Li, X., Liu, X., and Cheng, H. (2022). Using albert and multi-modal circulant fusion for fake news detection. In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2936–2942.
Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., and Gao, J. (2018). Eann: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18, page 849–857, New York, NY, USA. Association for Computing Machinery.
Wang, Y., Ma, F., Wang, H., Jha, K., and Gao, J. (2021). Multimodal emergent fake news detection via meta neural process networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD ’21, page 3708–3716, New York, NY, USA. Association for Computing Machinery.
Wardle, C. and Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policymaking, volume 27. Council of Europe Strasbourg.
Yu, W., Ge, J., Yang, Z., Dong, Y., Zheng, Y., and Dai, H. (2022). Multi-domain fake news detection for history news environment perception. In 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), pages 428–433.
Zeng, H., Yue, Z., Kou, Z., Shang, L., Zhang, Y., and Wang, D. (2022). Unsupervised domain adaptation for covid-19 information service with contrastive adversarial domain mixup. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 159–162.
Zhang, H., Fang, Q., Qian, S., and Xu, C. (2019). Multi-modal knowledge-aware event memory network for social media rumor detection. In Proceedings of the 27th ACM International Conference on Multimedia, MM ’19, page 1942–1951, New York, NY, USA. Association for Computing Machinery.
Zhang, H., Qian, S., Fang, Q., and Xu, C. (2021). Multimodal disentangled domain adaption for social media event rumor detection. IEEE Transactions on Multimedia, 23:4441–4454.
Zhang, T., Wang, D., Chen, H., Zeng, Z., Guo, W., Miao, C., and Cui, L. (2020). Bdann: Bert-based domain adaptation neural network for multi-modal fake news detection. In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–8.
Zhou, X. and Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53.
Zhu, Y., Sheng, Q., Cao, J., Nan, Q., Shu, K., Wu, M., Wang, J., and Zhuang, F. (2023). Memory-guided multi-view multi-domain fake news detection. IEEE Transactions on Knowledge and Data Engineering, 35(7):7178–7191.
Publicado
21/07/2024
Como Citar
BRAZ, Rafael R.; DIGIAMPIETRI, Luciano A..
Detecção de Fake News em Domínios Cruzados: Uma Revisão Sistemática. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 13. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 75-88.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2024.2529.
