mlsanityData to 7 Oct 2026

Cross-Platform Digital Discourse Analysis of the Israel-Hamas Conflict: Sentiment, Topics, and Event Dynamics

arXiv 2601.02367 ↗First posted 27 Nov 2025cs.CY cs.AI cs.CLcited by 1

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Abstract (arXiv)

The Israeli-Palestinian conflict remains one of the most polarizing geopolitical issues, with the October 2023 escalation intensifying online debate. Social media platforms, particularly Telegram, have become central to real-time news sharing, advocacy, and propaganda. In this study, we analyze Telegram, Twitter/X, and Reddit to examine how conflict narratives are produced, amplified, and contested across different digital spheres. Building on our previous work on Telegram discourse during the 2023 escalation, we extend the analysis longitudinally and cross-platform using an updated dataset spanning October 2023 to mid-2025. The corpus includes more than 187,000 Telegram messages, 2.1 million Reddit comments, and curated Twitter/X posts. We combine Latent Dirichlet Allocation (LDA), BERTopic, and transformer-based sentiment and emotion models to identify dominant themes, emotional dynamics, and propaganda strategies. Telegram channels provide unfiltered, high-intensity documentation of events; Twitter/X amplifies frames to global audiences; and Reddit hosts more reflective and deliberative discussions. Our findings reveal persistent negative sentiment, strong coupling between humanitarian framing and solidarity expressions, and platform-specific pathways for the diffusion of pro-Palestinian and pro-Israeli narratives. This paper offers three contributions: (1) a multi-platform, FAIR-compliant dataset on the Israel-Hamas war, (2) an integrated pipeline combining topic modeling, sentiment and emotion analysis, and spam filtering for large-scale conflict discourse, and (3) empirical insights into how platform affordances and affective publics shape the evolution of digital conflict communication.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 38 references that are in the map. First posted on arXiv 27 Nov 2025.

Builds on

6 of 38 references.

References · 6

  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  2. 2020The Pushshift Reddit Datasetcited by 289
  3. 2018On the Origins of Memes by Means of Fringe Web Communitiescited by 45
  4. 2015Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combinationcited by 11
  5. 2021Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memescited by 9
  6. 2025Israel-Hamas war through Telegram, Reddit and Twittercited by 3

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