# Multilingual Multimodal Digital Deception Detection and Disinformation Spread across Social Platforms

arXiv: https://arxiv.org/abs/1909.05838 · HTML: https://mlsanity.com/paper/1909.05838/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images](https://mlsanity.com/paper/2105.09284/) (2021)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [MemeIntel: Explainable Detection of Propagandistic and Hateful Memes](https://mlsanity.com/paper/2502.16612/) (2025)
- [ArMeme: Propagandistic Content in Arabic Memes](https://mlsanity.com/paper/2406.03916/) (2024)
- [Detection of Propaganda Techniques in Visuo-Lingual Metaphor in Memes](https://mlsanity.com/paper/2205.02937/) (2022)
- [IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes](https://mlsanity.com/paper/2404.04520/) (2024)

## Abstract (arXiv)

Our main contribution in this work is novel results of multilingual models that go beyond typical applications of rumor or misinformation detection in English social news content to identify fine-grained classes of digital deception across multiple languages (e.g. Russian, Spanish, etc.). In addition, we present models for multimodal deception detection from images and text and discuss the limitations of image only and text only models. Finally, we elaborate on the ongoing work on measuring deceptive content (in particular disinformation) spread across social platforms.