FinD: Fine-grained discrepancy-based fake news detection enhanced by event abstract generation

Devlin, 2018 Devlin, Jacob, Chang, Ming-Wei, Lee, Kenton, Toutanova, Kristina, 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Vol. 1. Long and Short Papers, pp. 4171–4186. Gad-Elrab, M.H., Stepanova, D., Urbani, J., Weikum, G., 2019. ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text. In: The Twelfth ACM International Conference. Gambino, 2019, Predicting emotional reactions to news articles in social networks, Comput. Speech Lang., 58, 280, 10.1016/j.csl.2019.03.004 Ghanem, Bilal, Ponzetto, Simone Paolo, Rosso, Paolo, Rangel, Francisco, 2021. FakeFlow: Fake News Detection by Modeling the Flow of Affective Information. In: EACL. Guo, Mingfei, Chen, Xiuying, Li, Juntao, Zhao, Dongyan, Yan, Rui, 2021. How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution. In: Companion Proceedings of the Web Conference 2021. pp. 407–411. Ibeke, Emmanuel Ebuka, Lin, Chenghua, Wyner, Adam Zachary, Barawi, Mohamad Hardyman Bin, 2017. Extracting and Understanding Contrastive Opinion through Topic Relevant Sentences.. In: International Joint Conference on Natural Language Processing. Karimi, 2019, Learning hierarchical discourse-level structure for fake news detection, 3432 Ke, 2017, Lightgbm: A highly efficient gradient boosting decision tree, Adv. Neural Inf. Process. Syst., 30 Kononenko, 1991, Semi-naive Bayesian classifier, 206 Lin, 2004, Rouge: A package for automatic evaluation of summaries, 74 Liu, 2020, Fned: a deep network for fake news early detection on social media, ACM Trans. Inform. Syst. (TOIS), 38, 1, 10.1145/3386253 Ma, 2019 Nie, 2018 Popat, 2018, Declare: Debunking fake news and false claims using evidence-aware deep learning, 22 Przybyła, Piotr, 2020. Capturing the Style of Fake News. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. (01), pp. 490–497. Qian, 2021, Knowledge-aware multi-modal adaptive graph convolutional networks for fake news detection, ACM Trans. Multimedia Comput. Commun. Appl. (TOMM), 17, 1, 10.1145/3451215 Qian, Shengsheng, Wang, Jinguang, Hu, Jun, Fang, Quan, Xu, Changsheng, 2021b. Hierarchical multi-modal contextual attention network for fake news detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 153–162. Samadi, 2021, Deep contextualized text representation and learning for fake news detection, Inf. Process. Manage., 58, 10.1016/j.ipm.2021.102723 Shiralkar, 2017 Shu, 2017, Fake news detection on social media: A data mining perspective, ACM SIGKDD Explor. Newsl., 19, 22, 10.1145/3137597.3137600 Shu, Kai, Zheng, Guoqing, Li, Yichuan, Mukherjee, Subhabrata, Awadallah, Ahmed Hassan, Ruston, Scott, Liu, Huan, 2020. Early detection of fake news with multi-source weak social supervision. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 650–666. Silva, Amila, Luo, Ling, Karunasekera, Shanika, Leckie, Christopher, 2021. Embracing domain differences in fake news: Cross-domain fake news detection using multi-modal data. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. (1), pp. 557–565. Vaibhav, Vaibhav, Mandyam, Raghuram, Hovy, Eduard, 2019. Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification. In: Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing. TextGraphs-13, pp. 134–139. Vedula, Nikhita, Parthasarathy, Srinivasan, 2021. Face-keg: Fact checking explained using knowledge graphs. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. pp. 526–534. Volkova, Svitlana, Shaffer, Kyle, Jang, Jin Yea, Hodas, Nathan, 2017. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Volume 2: Short Papers, pp. 647–653. Vosoughi, 2018, The spread of true and false news online, Science, 359, 1146, 10.1126/science.aap9559 Wei, 2021, A method of hot event detection based on improved TOPSIS, 290 Wu, Lianwei, Rao, Yuan, Sun, Ling, He, Wangbo, 2021. Evidence inference networks for interpretable claim verification. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. (16), pp. 14058–14066. Xue, 2021, Detecting fake news by exploring the consistency of multimodal data, Inf. Process. Manage., 58, 10.1016/j.ipm.2021.102610 Yang, Zichao, Yang, Diyi, Dyer, Chris, He, Xiaodong, Smola, Alex, Hovy, Eduard, 2016. Hierarchical Attention Networks for Document Classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 1480–1489. Zhang, Xueyao, Cao, Juan, Li, Xirong, Sheng, Qiang, Zhong, Lei, Shu, Kai, 2021. Mining dual emotion for fake news detection. In: Proceedings of the Web Conference 2021. pp. 3465–3476. Zhou, J., Han, X., Yang, C., Liu, Z., Sun, M., 2019. GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.