# Images and Misinformation in Political Groups: Evidence from WhatsApp in India

arXiv: https://arxiv.org/abs/2005.09784 · HTML: https://mlsanity.com/paper/2005.09784/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 11 of 11)

- [Multimodal Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2305.13507/) (2023)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Claim Matching Beyond English to Scale Global Fact-Checking](https://mlsanity.com/paper/2106.00853/) (2021)
- [Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video](https://mlsanity.com/paper/2202.12883/) (2022)
- ["Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups](https://mlsanity.com/paper/2102.03870/) (2021)
- [AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images](https://mlsanity.com/paper/2404.14244/) (2024)
- [Challenges and Considerations with Code-Mixed NLP for Multilingual Societies](https://mlsanity.com/paper/2106.07823/) (2021)
- [Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsApp](https://mlsanity.com/paper/2106.04726/) (2021)
- [PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter](https://mlsanity.com/paper/2407.20987/) (2024)
- [Dataset of News Articles with Provenance Metadata for Media Relevance Assessment](https://mlsanity.com/paper/2506.09847/) (2025)
- [WhatsApp Tiplines and Multilingual Claims in the 2021 Indian Assembly Elections](https://mlsanity.com/paper/2507.16298/) (2025)

## Abstract (arXiv)

WhatsApp is a key medium for the spread of news and rumors, often shared as images. We study a large collection of politically-oriented WhatsApp groups in India, focusing on the period leading up to the 2019 Indian national elections. By labeling samples of random and popular images, we find that around 13% of shared images are known misinformation and most fall into three types of images. Machine learning methods can be used to predict whether a viral image is misinformation, but are brittle to shifts in content over time.