# IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict

arXiv: https://arxiv.org/abs/2401.08202 · HTML: https://mlsanity.com/paper/2401.08202/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2024.

## Built on by (most cited first, 6 of 6)

- [COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities](https://mlsanity.com/paper/2406.12074/) (2024)
- [Evaluating Large Language Models for Detecting Antisemitism](https://mlsanity.com/paper/2509.18293/) (2025)
- [NLP Case Study on Predicting the Before and After of the Ukraine-Russia and Hamas-Israel Conflicts](https://mlsanity.com/paper/2410.06427/) (2024)
- [Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict](https://mlsanity.com/paper/2412.10913/) (2024)
- [Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments](https://mlsanity.com/paper/2502.00414/) (2025)
- [Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media](https://mlsanity.com/paper/2608.17987/) (2026)

## Abstract (arXiv)

The conflict between Israel and Palestinians significantly escalated after the October 7, 2023 Hamas attack, capturing global attention. To understand the public discourse on this conflict, we present a meticulously compiled dataset-IsamasRed-comprising nearly 400,000 conversations and over 8 million comments from Reddit, spanning from August 2023 to November 2023. We introduce an innovative keyword extraction framework leveraging a large language model to effectively identify pertinent keywords, ensuring a comprehensive data collection. Our initial analysis on the dataset, examining topics, controversy, emotional and moral language trends over time, highlights the emotionally charged and complex nature of the discourse. This dataset aims to enrich the understanding of online discussions, shedding light on the complex interplay between ideology, sentiment, and community engagement in digital spaces.