发表机构
Institute of Computer Science, Foundation for Research and Technology; Technical University of Crete（希腊研究与技术基金会计算机科学研究所; 克里特技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过多方法计算分析Telegram上的87617条以巴冲突相关消息，对比亲以和亲巴社群的话语差异，发现二者使用相同死亡与受害者相关词汇但情感倾向相反，为冲突话语研究提供了新视角。
AI 中文摘要
社交媒体已成为武装冲突博弈的核心场域，但尚未有大规模系统对比Telegram上的亲以色列和亲巴勒斯坦社群——其广播架构能产出异常直接的刻意政治交流记录。本研究对2021年5月至2026年6月期间、覆盖多次冲突升级的16个Telegram频道（8个亲以色列、8个亲巴勒斯坦）的87617条消息开展多方法计算分析，结合情感分析、三种不同范式的立场检测方法（关键词匹配、通过自然语言推理的零样本DeBERTa、微调后的BERTweet模型），以及框架分析，所有方法均以736条人工标注消息为基准进行评估。微调模型表现最佳（5折交叉验证下准确率72.1%、宏F1值0.721），比无标签基线模型高出8至11个百分点；基线模型准确率停留在中低60%区间，表明未适配领域内语言的立场检测存在性能上限。核心发现需结合情感、立场与框架分析方能得出：两个社群使用相同的与死亡、受害者相关的词汇，但情感倾向相反——亲以色列频道以中性、报道式表达为主，亲巴勒斯坦频道则明显更为负面，与二者作为行动方和受影响方的不同话语立场一致。
英文摘要
Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose broadcast architecture yields an unusually direct record of deliberate political communication, have not been systematically compared at scale. This study presents a multi-method computational analysis of 87,617 messages from sixteen Telegram channels, eight pro-Israel and eight pro-Palestine, spanning May 2021 to June 2026 and covering multiple conflict escalations. It combines sentiment analysis, three stance detection methods drawn from distinct paradigms (keyword matching, zero-shot DeBERTa via natural language inference, and a fine-tuned BERTweet model), and a framing analysis, all evaluated against 736 manually annotated messages. The fine-tuned model performed best (72.1% accuracy, 0.721 macro F1 under 5-fold cross-validation), outperforming both label-free baselines by 8 to 11 points; the baselines stalled in the low-to-mid 60s, indicating a hard ceiling for stance detection not adapted to in-domain language. The central finding emerges only when sentiment, stance, and framing are read together: the two communities deploy the same death- and victim-related vocabulary in opposite emotional registers, pro-Israel channels predominantly neutral and report-style, pro-Palestine channels markedly more negative, consistent with writing from the distinct discourse positions of acting party and affected party.
