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GUIDEUnderstanding fake news, misinformation & disinformation

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2026 DeepfakeGenome: Toward Next-Generation Deepfake Attribution Wang et al. · NeurIPS 2026 · Deepfakes & multimodal, Datasets & evaluation2026 Birds of a Feather Flock Together: Network-Based Detection of Coordinated Disinformation Campaigns on Telegram Tsagkarakis et al. · SECRYPT · Bots & coordinated campaigns, Spread & networks2026 VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation Huang et al. · NeurIPS 2026, Track on Evaluations and Datasets · LLMs & generative AI, Fact-checking & verification2026 Setting the clock: Evaluating temporal window parameters for coordinated behavior detection Panayiotou et al. · AIDEM Workshop, ECML-PKDD 2026 · Bots & coordinated campaigns, Datasets & evaluation2026 From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild Zeng et al. · ACM International Conference · Datasets & evaluation, Deepfakes & multimodal2026 Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models Wu et al. · ICLR 2026 · Deepfakes & multimodal, Detection methods2026 Beyond Speculation: Measuring the Growing Presence of LLM-Generated Texts in Multilingual Disinformation Macko et al. · IEEE Computer · LLMs & generative AI, Datasets & evaluation2026 Spectral Analysis of Fake News Propagation Cai & Zafarani · SIAM · Spread & networks, Detection methods2025 Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection Wang et al. · CIKM · Detection methods, LLMs & generative AI2025 A Guide to Misinformation Detection Data and Evaluation Thibault et al. · KDD '25 — 31st ACM SIGKDD Conference on… · Datasets & evaluation, Detection methods2023 A Survey on Evaluation of Large Language Models Chang et al. · Journal of the ACM · LLMs & generative AI, Datasets & evaluation2023 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 & evaluation2022 Deepfake Detection: A Systematic Literature Review Rana et al. · IEEE Access · Deepfakes & multimodal, Datasets & evaluation2021 Prevalence of Health Misinformation on Social Media: Systematic Review Suarez-Lledo & Alvarez-Galvez · Journal of Medical Internet Research · Spread & networks, Datasets & evaluation2021 A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances Zhou et al. · ACM Computing Surveys · Spread & networks, Datasets & evaluation2021 MDFEND: Multi-domain Fake News Detection Nan et al. · CIKM '21: The 30th ACM International… · Detection methods, Datasets & evaluation2021 DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection Mayank et al. · IEEE/ACM International Conference on Advances… · Detection methods, Datasets & evaluation2021 FakeBERT: Fake News Detection in Social Media with a BERT-based Deep Learning Approach Kaliyar et al. · Multimedia Tools and Applications · Detection methods, LLMs & generative AI2021 KAN: Knowledge-aware Attention Network for Fake News Detection Dun et al. · AAAI 2021 · Detection methods, LLMs & generative AI2021 Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multimodal Data Silva et al. · AAAI 2021 · Detection methods, Deepfakes & multimodal2020 On the Detection of Disinformation Campaign Activity with Network Analysis Vargas et al. · Cloud Computing Security Workshop · Spread & networks, Bots & coordinated campaigns2020 Uncovering Coordinated Networks on Social Media: Methods and Case Studies Pacheco et al. · ICWSM · Bots & coordinated campaigns, Spread & networks2020 Fighting an Infodemic: COVID-19 Fake News Dataset Patwa et al. · Springer · Datasets & evaluation, Detection methods2020 A Decade of Social Bot Detection Cresci · Communications of the ACM · Bots & coordinated campaigns, Datasets & evaluation