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.
arXiv:2609.36902v1 Announce Type: new Abstract: Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level re...
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...
arXiv:2609.36850v1 Announce Type: new Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipul...
arXiv:2607. 18080v1 Announce Type: cross Abstract: Multimodal video misinformation detection is commonly formulated as a holistic video-understanding task, where the entire video and its associated content are processed and judged in a single pass.
arXiv:2608. 06732v1 Announce Type: new Abstract: Recent text-to-video (T2V) generation models enable fake news videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled from existing footage.
arXiv:2601. 14954v3 Announce Type: replace Abstract: Social media increasingly disseminates information through mixed image text posts, but rumors often exploit subtle inconsistencies and forged content, making detection based solely on post content difficult.
Multimodal video misinformation detection is commonly formulated as a holistic video-understanding task, where the entire video and its associated content are processed and judged in a single pass. However, real-world misinformation often exhibits a sparse and compositional evidence structure: a reliable decision may depend on only a few coupled clues, while most video content contributes limited additional information.
MiniVer‑V is a new benchmark for short‑video fact‑checking that contains 195 videos, each annotated with a supported, refuted, or insufficient verdict and 5,510 multimodal evidence units (visual keyframes, speech transcripts, and web‑retrieved sources). The authors propose a two‑layer verification framework that first checks claim‑video consistency using internal evidence, then determines the factual verdict with external corroboration, and employs a sufficiency‑driven greedy search to assemble only the minimal evidence needed for a confident decision. Using Claude Sonnet 4, the method achieves a Macro‑F1 of 0.510 with an average of 4.5 evidence units—about 16 % of the full set—while matching the full‑evidence baseline and improving detection of insufficient cases.
arXiv:2510.23508v4 Announce Type: replace Abstract: Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover only one or two languages, f...
Clips2Story is a training‑free, LLM‑based pipeline that lets users generate short videos from a pool of clips by specifying a keyword. It first extracts multimodal descriptors from each clip, retrieves the most relevant ones, and then uses an LLM to select, arrange, and narrate them into an editable storyboard. The final video is assembled by combining the chosen clips with visuals that match the narration. Evaluation on a synthetic dataset of shuffled clips from 100 long‑form videos shows that Clips2Story achieves better narrative coherence, keyword alignment, and realism than a strong baseline.
The paper introduces Multi-Level Consistency and Conflict Decoupling (MLCCD), a method for detecting fake news in multimodal content by leveraging the layered knowledge of multimodal large language models (MLLMs). MLCCD first selects relevant visual and textual tokens using the MLLM’s final decision representation, then identifies tokens whose representations change sharply across adjacent layers to capture layer‑specific knowledge. Finally, it decouples this knowledge into consistency and conflict evidence, aggregating each across layers with dedicated routers that adapt to varying conflict levels, achieving superior performance on three benchmarks.