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Fake news detection plays a crucial role in protecting social media users and maintaining a healthy news ecosystem.
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Fake News: A Definition
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dEFEND: Explainable Fake News Detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . Association for Computing Machinery, 395–405
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SpotFake: A Multi-modal Framework for Fake News Detection. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) . 39–47
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Unsupervised Fake News Detection on Social Media: A Generative Approach
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Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Association for Computational Linguistics, 5077–5089
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FNED: A Deep Network for Fake News Early Detection on Social Media
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Capturing the Style of Fake News
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FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media
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Leveraging multi-source weak social supervision for early detection of fake news
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Adaptive Interaction Fusion Networks for Fake News Detection. In Proceedings of the 24th European Conference on Artificial Intelligence
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Fake News Detection with Generated Comments for News Articles. In 2020 IEEE 24th International Conference on Intelligent Engineering Systems . 85–90
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Gender Differences in Tackling Fake News: Different Degrees of Concern, but Same Problems
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Countering the Cognitive, Linguistic, and Psychological Underpinnings Behind Susceptibility to Fake News: A Review of Current Literature With Special Focus on the Role of Age and Digital Literacy
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FakeBERT: Fake News Detection in Social Media with a BERT-based Deep Learning Approach
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Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues. In Proceedings of the 29th ACM International Conference on Multimedia . Association for Computing Machinery, 1212–1220
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Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection
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Controllable News Comment Generation based on Attribute Level Contrastive Learning. In 2023 IEEE International Conference on Intelligence and Security Informatics . 1–6
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The Dark Side of Language Models: Exploring the Potential of LLMs in Multimedia Disinformation Generation and Dissemination
Dipto Barman, Ziyi Guo, and Owen Conlan. 2024 · 2024
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Gemma: Open Models Based on Gemini Research and Technology
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False Rumor of Explosion at White House Causes Stocks to Briefly Plunge; AP Confirms Its Twitter Feed Was Hacked
Patti Domm. 2013 · 2024
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Integrating Large Language Models with Graphical Session-Based Recommendation
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Mining Dual Emotion for Fake News Detection. In Proceedings of the Web Conference 2021 . Association for Computing Machinery, 3465–3476
Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. 2021 · 2021
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Deep learning for fake news detection: A comprehensive survey
Linmei Hu, Siqi Wei, Ziwang Zhao, and Bin Wu. 2022 · 2022
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HC-COVID: A Hierarchical Crowdsource Knowledge Graph Approach to Explainable COVID-19 Misinformation Detection
Ziyi Kou, Lanyu Shang, Yang Zhang, and Dong Wang. 2022 · 2022
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Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo
Qiang Sheng, Juan Cao, H Russell Bernard, Kai Shu, Jintao Li, and Huan Liu. 2022a · 2022
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Zoom Out and Observe: News Environment Perception for Fake News Detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, 4543–4556
Qiang Sheng, Juan Cao, Xueyao Zhang, Rundong Li, Danding Wang, and Yongchun Zhu. 2022b · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
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A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics . International Committee on Computational Linguistics, Gyeongju, Republic of Korea, 2608–2621
Zhiwei Yang, Jing Ma, Hechang Chen, Hongzhan Lin, Ziyang Luo, and Yi Chang. 2022 · 2022
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Generalizing to the Future: Mitigating Entity Bias in Fake News Detection. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . Association for Computing Machinery, 2120–2125
Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, and Fuzhen Zhuang. 2022 · 2022
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Naicheng Guo, Hongwei Cheng, Qianqiao Liang, Linxun Chen, and Bing Han. 2024 · 2024
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Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi. 2024 · 2024
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Large Language Model Interaction Simulator for Cold-Start Item Recommendation
Feiran Huang, Zhenghang Yang, Junyi Jiang, Yuanchen Bei, Yijie Zhang, and Hao Chen. 2024 · 2024
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Disinformation Detection: An Evolving Challenge in the Age of LLMs. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) . SIAM, 427–435
Bohan Jiang, Zhen Tan, Ayushi Nirmal, and Huan Liu. 2024 · 2024
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Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One
Tianlin Li, Xiaoyu Zhang, Chao Du, Tianyu Pang, Qian Liu, Qing Guo, Chao Shen, and Yang Liu. 2024 · 2024
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TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
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Can Large Language Models Detect Rumors on Social Media?
Qiang Liu, Xiang Tao, Junfei Wu, Shu Wu, and Liang Wang. 2024b · 2024
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From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News
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Explainable Fake News Detection with Large Language Model via Defense Among Competing Wisdom. In Proceedings of the ACM on Web Conference 2024 . Association for Computing Machinery, New York, NY, USA, 2452–2463
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