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GUIDEUnderstanding fake news, misinformation & disinformation

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2023 Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media Mills et al. · arXiv · Spread & networks, Platforms, policy & society2023 FLIRT: Feedback Loop In-context Red Teaming Mehrabi et al. · arXiv · LLMs & generative AI, Deepfakes & multimodal2023 Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective Mazurczyk et al. · ACM Publication · Platforms, policy & society, LLMs & generative AI2023 Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment Liu et al. · arXiv · LLMs & generative AI, Platforms, policy & society2023 Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations Goldstein et al. · arXiv · LLMs & generative AI, Platforms, policy & society2023 Toxicity in ChatGPT: Analyzing Persona-assigned Language Models Deshpande et al. · arXiv · LLMs & generative AI, Platforms, policy & society2023 A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability Dai et al. · ACM Computing Surveys · LLMs & generative AI, Detection methods2023 A Survey on Evaluation of Large Language Models Chang et al. · Journal of the ACM · LLMs & generative AI, Datasets & evaluation2023 A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT Cao et al. · J. ACM, Vol. 37, No. 4 · LLMs & generative AI, Deepfakes & multimodal2023 Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection Asai et al. · arXiv · LLMs & generative AI, Detection methods2023 Disinformation Capabilities of Large Language Models Vykopal et al. · arXiv · LLMs & generative AI, Detection methods2023 Can AI-Generated Text be Reliably Detected? Sadasivan et al. · arXiv · LLMs & generative AI, Detection methods2023 On the Risk of Misinformation Pollution with Large Language Models Pan et al. · arXiv · LLMs & generative AI, Detection methods2023 A Watermark for Large Language Models Kirchenbauer et al. · arXiv · LLMs & generative AI, Detection methods2023 Combating Misinformation in the Age of LLMs: Opportunities and Challenges Chen & Shu · arXiv · LLMs & generative AI, Detection methods2022 "This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders Zhou et al. · ACM Web Conference 2022 · Spread & networks, Detection methods2022 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 methods2022 Red Teaming Language Models with Language Models Perez et al. · arXiv · LLMs & generative AI, Bots & coordinated campaigns2022 Discovering Language Model Behaviors with Model-Written Evaluations Perez et al. · LLMs & generative AI, Datasets & evaluation2022 Causal Machine Learning: A Survey and Open Problems Kaddour et al. · arXiv · LLMs & generative AI, Detection methods2022 A Comprehensive Survey on Trustworthy Recommender Systems Fan et al. · ACM Computing Surveys · Platforms, policy & society, Detection methods2022 Right-Wing YouTube: A Supply and Demand Perspective Munger & Phillips · The International Journal of Press/Politics · Spread & networks, Platforms, policy & society2022 The psychological drivers of misinformation belief and its resistance to correction Ecker et al. · Nature Reviews Psychology · Psychology & interventions, Fact-checking & verification2021 Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA Loomba et al. · Nature Human Behaviour · Psychology & interventions, Platforms, policy & society