# FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas War

arXiv: https://arxiv.org/abs/2401.16625 · HTML: https://mlsanity.com/paper/2401.16625/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2024.

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

- [Defining, Understanding, and Detecting Online Toxicity: Challenges and Machine Learning Approaches](https://mlsanity.com/paper/2509.14264/) (2025)

## Abstract (arXiv)

We contribute the first publicly available dataset of factual claims from different platforms and fake YouTube videos on the 2023 Israel-Hamas war for automatic fake YouTube video classification. The FakeClaim data is collected from 60 fact-checking organizations in 30 languages and enriched with metadata from the fact-checking organizations curated by trained journalists specialized in fact-checking. Further, we classify fake videos within the subset of YouTube videos using textual information and user comments. We used a pre-trained model to classify each video with different feature combinations. Our best-performing fine-tuned language model, Universal Sentence Encoder (USE), achieves a Macro F1 of 87%, which shows that the trained model can be helpful for debunking fake videos using the comments from the user discussion. The dataset is available on Githubfootnotehttps://github.com/Gautamshahi/FakeClaim