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arXiv · 2601.02367

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

Despoina Antonakaki·Sotiris Ioannidis
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Abstract

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.

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Despoina Antonakaki, Sotiris Ioannidis. 2025-11-27. Cross-Platform Digital Discourse Analysis of the Israel-Hamas Conflict: Sentiment, Topics, and Event Dynamics. https://arxiv.org/abs/2601.02367

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