Guerra, Alessio, Lepre, Marcello and Karakus, Oktay  ORCID: https://orcid.org/0000-0001-8009-9319
2025.
Quantifying extreme opinions on Reddit amidst the 2023 Israeli-Palestinian conflict.
Natural Language Processing Journal
11
, 100156.
10.1016/j.nlp.2025.100156
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Abstract
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. Our findings show that posts exhibiting extreme sentiment surged up to 80% (an increase of 0.3 in extremism score above the average of 0.405 at the end of October) during key conflict events. Compared to recent studies that have not explicitly quantified extremism in an unsupervised manner, we contribute to the literature by addressing this gap through a novel extremism score, derived from sentiment polarity, anger, and subjectivity, to analyse Reddit discourse surrounding the 2023 Israel–Palestine conflict. This approach captures the complex interplay between real-world events and online reactions, while acknowledging the inherent challenges of measuring extremism in dynamic social media environments. Our approach also enables scalable monitoring of public sentiment extremity, providing valuable insights for policymakers and conflict researchers.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Elsevier |
| ISSN: | 2949-7191 |
| Date of First Compliant Deposit: | 8 December 2025 |
| Date of Acceptance: | 25 May 2025 |
| Last Modified: | 08 Dec 2025 16:07 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/182965 |
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