跨平台分析以巴冲突网络话语，揭示情绪、议题与传播差异。
Cross-Platform Digital Discourse Analysis of the Israel-Hamas Conflict: Sentiment, Topics, and Event Dynamics
- 融合多种模型分析三平台海量文本，识别核心议题与情绪模式。
- 发现人道主义叙事与声援高度关联，平台特性决定信息扩散路径。
- 适合关注数字舆论、冲突传播与社交媒体影响的研究者参考。
以巴冲突是全球最极化的地缘政治议题之一，2023年10月升级后加剧了网络争论。社交平台如Telegram成为实时新闻传播、倡导与宣传的核心阵地。本研究分析Telegram、Twitter/X和Reddit，探讨冲突叙事在不同数字空间中的生成、放大与争议。基于此前对2023年升级期Telegram话语的研究，本文扩展为纵向与跨平台分析，使用覆盖2023年10月至2025年中旬的更新数据集，包含超过18.7万条Telegram消息、210万条评论及精选的Twitter/X推文。采用LDA、BERTopic及基于Transformer的情感与情绪模型，识别主导主题、情感动态与宣传策略。Telegram提供高密度事件记录；Twitter/X将框架传播至全球；Reddit则呈现更反思性讨论。研究发现持续负向情绪，人道主义叙事与声援高度耦合，且各平台存在不同的亲巴与亲以叙事扩散路径。本文贡献包括：(1) 一个符合FAIR原则的以巴战争多平台语料库；(2) 一套集成话题建模、情感分析与垃圾内容过滤的大规模冲突话语分析流程；(3) 平台特性与情感公众如何塑造数字冲突传播演进的实证洞察。
原文摘要 · Abstract (English)
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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