[1] Hunt Allcott and Matthew Gentzkow. Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2):211–36, May 2017.
[2] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Comput. Surv., 41(3), July 2009.
[3] Xinran Chen and Sei-Ching Joanna Sin. ’misinformation? what of it?’: Motivations and individual differences in misinformation sharing on social media. In Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries, ASIST ’13, USA, 2013. American Society for Information Science.
[4] Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014, 2014.
[5] Limeng Cui, Kai Shu, Suhang Wang, Dongwon Lee, and Huan Liu. Defend: A system for explainable fake news detection. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM ’19, page 2961–2964, New York, NY, USA, 2019. Association for Computing Machinery.
[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 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 Computa- tional Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics.
[7] Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, and Lichao Sun. User Preference- aware Fake News Detection. arXiv e-prints, page arXiv:2104.12259, April 2021.
[8] Song Feng, Ritwik Banerjee, and Yejin Choi. Syntactic stylometry for deception detec- tion. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 171–175, Jeju Island, Korea, July 2012. Association for Computational Linguistics.
[9] Siva Charan Reddy Gangireddy, Deepak P, Cheng Long, and Tanmoy Chakraborty. Unsupervised fake news detection: A graph-based approach. In Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT ’20, page 75–83, New York, NY, USA, 2020. Association for Computing Machinery.
[10] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS’14, page 2672–2680, Cambridge, MA, USA, 2014. MIT Press.
[11] Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. Deepfm: A factorization-machine based neural network for ctr prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI’17, page 1725– 1731. AAAI Press, 2017.
[12] Yi Han, Shanika Karunasekera, and Christopher Leckie. Graph Neural Networks with Continual Learning for Fake News Detection from Social Media. arXiv e-prints, page arXiv:2007.03316, July 2020.
[13] Xiangnan He and Tat-Seng Chua. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’17, page 355–364, New York, NY, USA, 2017. Association for Computing Machinery.
[14] G. E. Hinton. Reducing the dimensionality of data with neural networks. Science, 313(5786):504–507, July 2006.
[15] MarianneE.Jaeger,SusanAnthony,andRalphL.Rosnow.Whohearswhatfromwhom and with what effect: A study of rumor. Personality and Social Psychology Bulletin, 6(3):473–478, 1980.
[16] Rohit Kumar Kaliyar, Anurag Goswami, and Pratik Narang. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools and Applications, 80(8):11765–11788, January 2021.
[17] Yoon Kim. Convolutional neural networks for sentence classification. In Proceed- ings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746–1751, Doha, Qatar, October 2014. Association for Computa- tional Linguistics.
[18] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015.
[19] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convo- lutional networks. In International Conference on Learning Representations (ICLR), 2017.
[20] VivekKulkarni,JuntingYe,SteveSkiena,andWilliamYangWang.Multi-viewmodels for political ideology detection of news articles. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3518–3527, Brussels, Belgium, October-November 2018. Association for Computational Linguistics.
[21] JunhyunLee,InyeopLee,andJaewooKang.Self-attentiongraphpooling.InKamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Re- search, pages 3734–3743. PMLR, 09–15 Jun 2019.
[22] Dun Li, Haimei Guo, Zhenfei Wang, and Zhiyun Zheng. Unsupervised fake news de- tection based on autoencoder. IEEE Access, 9:29356–29365, 2021.
[23] Jiawen Li, Shiwen Ni, and Hung-Yu Kao. Birds of a feather rumor together? exploring homogeneity and conversation structure in social media for rumor detection. IEEE Access, 8:212865–212875, 2020.
[24] Yi-Ju Lu and Cheng-Te Li. GCAN: Graph-aware co-attention networks for explain- able fake news detection on social media. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 505–514, Online, July 2020. Association for Computational Linguistics.
[25] Jing Ma, Wei Gao, and Kam-Fai Wong. Detect rumors in microblog posts using prop- agation structure via kernel learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 708–717, Vancouver, Canada, July 2017. Association for Computational Linguistics.
[26] JingMa,WeiGao,andKam-FaiWong.RumordetectiononTwitterwithtree-structured recursive neural networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1980–1989, Melbourne, Australia, July 2018. Association for Computational Linguistics.
[27] Larry M. Manevitz and Malik Yousef. One-class svms for document classification. J. Mach. Learn. Res., 2:139–154, March 2002.
[28] Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M Bronstein. Fake news detection on social media using geometric deep learning. ICLR, 2019.
[29] Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, and Min-Yen Kan. Fang: Leveraging social context for fake news detection using graph representation. In Pro- ceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM ’20, page 1165–1174, New York, NY, USA, 2020. Association for Computing Machinery.
[30] Raymond S. Nickerson. Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2):175–220, 1998.
[31] Steffen Rendle. Factorization machines. In 2010 IEEE International Conference on Data Mining, pages 995–1000, 2010.
[32] Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. Dropedge: Towards deep graph convolutional networks on node classification. In International Conference on Learning Representations, 2020.
[33] Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kai-Cheng Yang, Alessan- dro Flammini, and Filippo Menczer. The spread of low-credibility content by social bots. Nature Communications, 9(1), November 2018.
[34] Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, and Huan Liu. The role of user profiles for fake news detection. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’19, page 436–439, New York, NY, USA, 2019. Association for Computing Machinery.
[35] Yun-Zhu Song, Yi-Syuan Chen, Yi-Ting Chang, Shao-Yu Weng, and Hong-Han Shuai. Adversary-aware rumor detection. In ACL-IJCNLP: Findings, 2021.
[36] DavidM.J.TaxandRobertP.W.Duin.Supportvectordatadescription.MachineLearn- ing, 54(1):45–66, January 2004.
[37] Onur Varol, Emilio Ferrara, Clayton Allen Davis, Filippo Menczer, and Alessandro Flammini. Online human-bot interactions: Detection, estimation, and characterization. In Proc. Intl. AAAI Conf. on Web and Social Media (ICWSM), pages 280–289, 2017.
[38] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, undefinedukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Hook, NY, USA, 2017. Curran Associates Inc.
[39] Soroush Vosoughi, Deb Roy, and Sinan Aral. The spread of true and false news online. Science, 359(6380):1146–1151, March 2018.
[40] Less Wright. Ranger - a synergistic optimizer. https://github.com/lessw2020/ Ranger-Deep-Learning-Optimizer, 2019.
[41] Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. Automatic detection of rumor on sina weibo. In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS ’12, New York, NY, USA, 2012. Association for Computing Machinery.
[42] Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, and Huan Liu. Unsupervised fake news detection on social media: A generative approach. Proceedings of the AAAI Conference on Artificial Intelligence, 33:5644–5651, July 2019.
[43] Xinyi Zhou, Atishay Jain, Vir V. Phoha, and Reza Zafarani. Fake news early detection: A theory-driven model. Digital Threats: Research and Practice, 1(2), June 2020.