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

• URL: http://arxiv.org/abs/2601.02367v1
• Date: Thu, 27 Nov 2025 10:11:59 GMT
• Title: Cross-Platform Digital Discourse Analysis of the Israel-Hamas Conflict: Sentiment, Topics, and Event Dynamics
• Authors: Despoina Antonakaki, Sotiris Ioannidis,
• Abstract summary: Israeli-Palestinian conflict remains one of the most polarizing geopolitical issues.<n>Social media platforms have become central to real-time news sharing, advocacy, and propaganda.<n>We analyze Telegram, Twitter/X, and Reddit to examine how conflict narratives are produced, amplified, and contested.
• Score: 4.282746516699565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• Israel-Hamas war through Telegram, Reddit and Twitter [9.020777839880571]
  The study will cover an analysis of the related discussion in relation to different participants of the conflict and sentiment represented in those discussion.<n>We apply a volume analysis across the three datasets, entity extraction and then proceed to BERT topic analysis.<n>Our findings hint at polarized narratives as the hallmark of how political factions and outsiders mold public opinion.
  arXiv  Detail & Related papers  (2025-01-30T08:20:26Z)
• Quantifying Extreme Opinions on Reddit Amidst the 2023 Israeli-Palestinian Conflict [3.2430260063115224]
  This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli-Palestinian conflict.<n>A lexicon-based, unsupervised methodology was developed to measure "extreme opinions"<n>The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events.
  arXiv  Detail & Related papers  (2024-12-14T17:52:28Z)
• More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram [0.0]
  We apply multimodal topic modeling for analyzing conspiracy theories in German-language Telegram channels.<n>We provide insights into unimodal and multimodal topic models by analyzing symmetry and intersections of topics across modalities.<n>We propose a conceptual framework for the analysis of textual and visual discursive strategies in the communication of conspiracy theories.
  arXiv  Detail & Related papers  (2024-10-11T09:10:26Z)
• The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor Recognition [64.5207572897806]
  The Multimodal Sentiment Analysis Challenge (MuSe) 2024 addresses two contemporary multimodal affect and sentiment analysis problems. In the Social Perception Sub-Challenge (MuSe-Perception), participants will predict 16 different social attributes of individuals. The Cross-Cultural Humor Detection Sub-Challenge (MuSe-Humor) dataset expands upon the Passau Spontaneous Football Coach Humor dataset.
  arXiv  Detail & Related papers  (2024-06-11T22:26:20Z)
• IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict [13.92311040225417]
  We present a meticulously compiled dataset-IsamasRed spanning from August 2023 to November 2023. 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.
  arXiv  Detail & Related papers  (2024-01-16T08:30:41Z)
• InfoPattern: Unveiling Information Propagation Patterns in Social Media [59.67008841974645]
  InfoPattern centers on the interplay between language and human ideology. The demo is capable of: (1) red teaming to simulate adversary responses from opposite ideology communities; (2) stance detection to identify the underlying political sentiments in each message; (3) information propagation graph discovery to reveal the evolution of claims across various communities over time.
  arXiv  Detail & Related papers  (2023-11-27T09:12:35Z)
• Cross-Platform Social Dynamics: An Analysis of ChatGPT and COVID-19 Vaccine Conversations [37.69303106863453]
  We analyzed over 12 million posts and news articles related to two significant events: the release of ChatGPT in 2022 and the global discussions about COVID-19 vaccines in 2021. Data was collected from multiple platforms, including Twitter, Facebook, Instagram, Reddit, YouTube, and GDELT. We employed topic modeling techniques to uncover the distinct thematic emphases on each platform, which reflect their specific features and target audiences.
  arXiv  Detail & Related papers  (2023-10-17T09:58:55Z)
• Computational Assessment of Hyperpartisanship in News Titles [55.92100606666497]
  We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection. Overall the Right media tends to use proportionally more hyperpartisan titles. We identify three major topics including foreign issues, political systems, and societal issues that are suggestive of hyperpartisanship in news titles.
  arXiv  Detail & Related papers  (2023-01-16T05:56:58Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.