# Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict

arXiv: https://arxiv.org/abs/2412.10913 · HTML: https://mlsanity.com/paper/2412.10913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 108 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence](https://mlsanity.com/paper/2108.13293/) (2021)
- [No Echo in the Chambers of Political Interactions on Reddit](https://mlsanity.com/paper/2102.05477/) (2021)
- [IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict](https://mlsanity.com/paper/2401.08202/) (2024)
- [Sentiment Analysis for Measuring Hope and Fear from Reddit Posts During the 2022 Russo-Ukrainian Conflict](https://mlsanity.com/paper/2301.08347/) (2023)
- [Sentiment Analysis of Twitter Posts on Global Conflicts](https://mlsanity.com/paper/2312.03715/) (2023)

## Abstract (arXiv)

This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict, utilising a comprehensive dataset of over 450,000 posts from four Reddit subreddits (r/Palestine, r/Judaism, r/IsraelPalestine, and r/worldnews). A lexicon-based, unsupervised methodology was developed to measure "extreme opinions" by considering factors such as anger, polarity, and subjectivity. The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events, such as the IDF's bombings of Al Quds Hospital and the Jabalia Refugee Camp, and the end of a ceasefire following a terrorist attack. Additionally, this study explores the distribution and correlation of these scores across different subreddits and over time, providing insights into the propagation of polarised sentiments in response to conflict events. By examining the quantitative effects of each score on extremism and analysing word cloud similarities through Jaccard indices, the research offers a nuanced understanding of the factors driving extreme online opinions. This approach underscores the potential of social media analytics in capturing the complex interplay between real-world events and online discourse, while also highlighting the limitations and challenges of measuring extremism in social media contexts.