arXiv Computation and Language
Rethinking Multimodal Fake News Detection in the Generative AI Era
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
4d ago
Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?
The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
arXiv Machine Learning
Jun 9
KITE: A Tri-Modal Transformer Integrating Text, Images, and Knowledge Graphs for Fake News Detection
arXiv:2606. 07651v1 Announce Type: new Abstract: Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manipulated visuals, and factually incorrect claims.
Hugging Face Trending Papers
Sep 16
Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection
Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which require substantial labeled data and exhibit limit...
arXiv AI
Sep 17
Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection
The paper introduces MAGER, a multi-agent genetic evolution framework that automatically discovers meta-paths for large language models (LLMs) to reason about fake news propagation graphs. By compressing complex propagation structures into informative subgraphs, MAGER reduces modality mismatch and information overload, enabling frozen LLMs to perform structure-aware veracity reasoning. The authors also propose a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to enhance classification and reasoning, and report that MAGER significantly improves LLM performance in data‑efficient settings.
arXiv Computation and Language
Sep 22
RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Adaptive LLM-Generated Fake News Detection
arXiv:2601.03981v3 Announce Type: replace Abstract: To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversar...
arXiv AI
Aug 25
Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku
arXiv:2608.22832v1 Announce Type: new Abstract: The social interactions among crowds via \textit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint confli...