# Multimodal Detection of Information Disorder from Social Media

arXiv: https://arxiv.org/abs/2105.15165 · HTML: https://mlsanity.com/paper/2105.15165/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2021.

## Built on by (most cited first, 4 of 4)

- [Multimodal Fake News Detection](https://mlsanity.com/paper/2112.04831/) (2021)
- [Rethinking Multimodal Content Moderation from an Asymmetric Angle with Mixed-modality](https://mlsanity.com/paper/2305.10547/) (2023)
- [VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning](https://mlsanity.com/paper/2406.10289/) (2024)
- [Multimodal rumor detection enhanced by external evidence and forgery features](https://mlsanity.com/paper/2601.14954/) (2026)

## Abstract (arXiv)

Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the problem of multimodal fake news detection on a largescale multimodal dataset. We propose a multimodal network architecture that enables different levels and types of information fusion. In addition to the textual and visual content of a posting, we further leverage secondary information, i.e. user comments and metadata. We fuse information at multiple levels to account for the specific intrinsic structure of the modalities. Our results show that multimodal analysis is highly effective for the task and all modalities contribute positively when fused properly.