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2023 Adapting Fake News Detection to the Era of Large Language Models Su et al. · arXiv · LLMs & generative AI, Detection methods 2023 A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability Dai et al. · ACM Computing Surveys · LLMs & generative AI, Detection methods 2023 The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention Ayoobi et al. · 3rd ACM Conference on Hypertext and Social Media · LLMs & generative AI, Datasets & evaluation 2023 Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection Asai et al. · arXiv · LLMs & generative AI, Detection methods 2023 Disinformation Capabilities of Large Language Models Vykopal et al. · arXiv · LLMs & generative AI, Detection methods 2023 3HAN: A Deep Neural Network for Fake News Detection Singhania et al. · arXiv · Detection methods, LLMs & generative AI 2023 Can AI-Generated Text be Reliably Detected? Sadasivan et al. · arXiv · LLMs & generative AI, Detection methods 2023 On the Risk of Misinformation Pollution with Large Language Models Pan et al. · arXiv · LLMs & generative AI, Detection methods 2023 DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature Mitchell et al. · 40th International Conference on Machine… · LLMs & generative AI, Detection methods 2023 A Watermark for Large Language Models Kirchenbauer et al. · arXiv · LLMs & generative AI, Detection methods 2023 Combating Misinformation in the Age of LLMs: Opportunities and Challenges Chen & Shu · arXiv · LLMs & generative AI, Detection methods 2023 Linguistic-style-aware Neural Networks for Fake News Detection Zhou et al. · arXiv · Detection methods, LLMs & generative AI 2022 From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection Zhou et al. · ACM Conference · Detection methods, Datasets & evaluation 2022 "This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders Zhou et al. · ACM Web Conference 2022 · Spread & networks, Detection methods 2022 All the News That's Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation Kreps et al. · Journal of Experimental Political Science · LLMs & generative AI, Detection methods 2022 Discovering Latent Knowledge in Language Models Without Supervision Burns et al. · arXiv · LLMs & generative AI, Detection methods 2022 Botometer 101: Social bot practicum for computational social scientists Yang et al. · arXiv · Bots & coordinated campaigns, Datasets & evaluation 2022 Causal Machine Learning: A Survey and Open Problems Kaddour et al. · arXiv · LLMs & generative AI, Detection methods 2022 A Comprehensive Survey on Trustworthy Recommender Systems Fan et al. · ACM Computing Surveys · Platforms, policy & society, Detection methods 2022 Toxicity Detection with Generative Prompt-based Inference Wang & Chang · arXiv · Detection methods, LLMs & generative AI 2022 Deepfake Detection: A Systematic Literature Review Rana et al. · IEEE Access · Deepfakes & multimodal, Datasets & evaluation 2022 NELA-GT-2022: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles Gruppi et al. · arXiv · Datasets & evaluation, Detection methods 2022 Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text Dugan et al. · arXiv · LLMs & generative AI, Detection methods 2021 A Survey on Automated Fact-Checking Guo et al. · arXiv · Fact-checking & verification, Detection methods