{"slug": "a-social-media-analysis-of-discourse-on-the-israel-palestine-conflict-on", "title": "A Social Media Analysis of Discourse on the Israel--Palestine Conflict on Telegram", "summary": "A computational analysis of 87,617 messages from sixteen Telegram channels, eight pro-Israel and eight pro-Palestine, spanning May 2021 to June 2026, found that both communities use the same death- and victim-related vocabulary in opposite emotional registers: pro-Israel channels are predominantly neutral and report-style, while pro-Palestine channels are markedly more negative. The study, posted on arXiv (2608.21385v1), evaluated three stance detection methods against 736 manually annotated messages, with a fine-tuned BERTweet model performing best at 72.1% accuracy and 0.721 macro F1, outperforming label-free baselines by 8 to 11 points.", "body_md": "arXiv:2608.21385v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/a-social-media-analysis-of-discourse-on-the-israel-palestine-conflict-on", "canonical_source": "https://arxiv.org/abs/2608.21385", "published_at": "2026-08-25 04:00:00+00:00", "updated_at": "2026-08-25 04:15:02.990301+00:00", "lang": "en", "topics": ["natural-language-processing", "machine-learning"], "entities": ["Telegram", "arXiv", "DeBERTa", "BERTweet"], "alternates": {"html": "https://wpnews.pro/news/a-social-media-analysis-of-discourse-on-the-israel-palestine-conflict-on", "markdown": "https://wpnews.pro/news/a-social-media-analysis-of-discourse-on-the-israel-palestine-conflict-on.md", "text": "https://wpnews.pro/news/a-social-media-analysis-of-discourse-on-the-israel-palestine-conflict-on.txt", "jsonld": "https://wpnews.pro/news/a-social-media-analysis-of-discourse-on-the-israel-palestine-conflict-on.jsonld"}}