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2026
MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection
Modzelewski et al. · arXiv · Datasets & evaluation, LLMs & generative AI2026
Beyond Speculation: Measuring the Growing Presence of LLM-Generated Texts in Multilingual Disinformation
Macko et al. · IEEE Computer · LLMs & generative AI, Datasets & evaluation2026
'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions
Dycke et al. · arXiv · 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 methods2025
VLDBench: Evaluating Multimodal Disinformation with Regulatory Alignment
Raza et al. · arXiv · Deepfakes & multimodal, Datasets & evaluation2025
Neighborhood-Order Learning Graph Attention Network for Fake News Detection
Lakzaei et al. · arXiv · Detection methods, Datasets & evaluation2025
A Decision-Based Heterogeneous Graph Attention Network for Multi-Class Fake News Detection
Lakzaei et al. · arXiv · Detection methods, Datasets & evaluation2025
Is Less Really More? Fake News Detection with Limited Information
Cao et al. · arXiv · Detection methods, LLMs & generative AI2024
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
Mao et al. · arXiv · LLMs & generative AI, Datasets & evaluation2024
Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
Ba et al. · arXiv · Deepfakes & multimodal, Detection methods2023
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zhang et al. · arXiv · LLMs & generative AI, Datasets & evaluation2023
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Wu et al. · arXiv · LLMs & generative AI, Detection methods2023
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Wang et al. · arXiv · LLMs & generative AI, Fact-checking & verification2023
The Science of Detecting LLM-Generated Texts
Tang et al. · arXiv · LLMs & generative AI, Detection methods2023
Adapting Fake News Detection to the Era of Large Language Models
Su et al. · arXiv · LLMs & generative AI, Detection methods2023
The Perils & Promises of Fact-checking with Large Language Models
Quelle & Bovet · arXiv · Fact-checking & verification, 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
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 & evaluation2023
3HAN: A Deep Neural Network for Fake News Detection
Singhania et al. · arXiv · Detection methods, LLMs & generative AI2023
Linguistic-style-aware Neural Networks for Fake News Detection
Zhou et al. · arXiv · Detection methods, LLMs & generative AI2022
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