# Large Language Models and Provenance Metadata for Determining the Relevance of Images and Videos in News Stories

arXiv: https://arxiv.org/abs/2502.09689 · HTML: https://mlsanity.com/paper/2502.09689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Dataset of News Articles with Provenance Metadata for Media Relevance Assessment](https://mlsanity.com/paper/2506.09847/) (2025)

## Abstract (arXiv)

The most effective misinformation campaigns are multimodal, often combining text with images and videos taken out of context -- or fabricating them entirely -- to support a given narrative. Contemporary methods for detecting misinformation, whether in deepfakes or text articles, often miss the interplay between multiple modalities. Built around a large language model, the system proposed in this paper addresses these challenges. It analyzes both the article's text and the provenance metadata of included images and videos to determine whether they are relevant. We open-source the system prototype and interactive web interface.