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Journals / TRT Akademi / 2026 / Cilt: 11 Sayı: 28

Event-Window Analysis of Public Discourse via Multi-Target Stance Classification During the 12-Day Iran–Israel War

İran–İsrail 12 Gün Savaşı Sırasında Çok Hedefli Duruş Sınıflandırması Yoluyla Kamusal Söylemin Olay Penceresi Analizi

Celal Erbay(Milli İstihbarat Akademisi)0000-0001-8501-3908
Pages
1170–1209
Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results show that discussion most often focused on U.S. strikes, while Israel and Iran were less frequently evaluated directly.

📌 3 Key Takeaways

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Objective & Hypothesis:This study analyzes how online audiences expressed stance and interpretive frames toward three related targets, U.S. strikes, Israel, and Iran, during the 12-day Iran–Israel war period through YouTube comments on prominent U.S. news videos.
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Key Findings & Data:A corpus of 112,000 comments was divided into pre-event, event-window, and post-event periods.
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Conclusion & Impact:Results show that discussion most often focused on U.S. strikes, while Israel and Iran were less frequently evaluated directly.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Duruş Tespiti · Olay Merkezli Analiz · Youtube Yorumları

Abstract

This study analyzes how online audiences expressed stance and interpretive frames toward three related targets, U.S. strikes, Israel, and Iran, during the 12-day Iran–Israel war period through YouTube comments on prominent U.S. news videos. A corpus of 112,000 comments was divided into pre-event, event-window, and post-event periods. Stance was modeled separately for each target using four labels: Support, Oppose, Unclear, and Not Applicable, allowing evaluation and target salience to be distinguished. A humanlabeled, double-coded sample of 2,500 comments supported annotation reliability testing and model training. Classifiers were evaluated with leakage-resistant protocols controlling for video overlap and temporal generalization, then applied to the full corpus. Under these evaluation settings, TF-IDF with Logistic Regression achieved the strongest average four-way Macro-F1, outperforming the hierarchical DeBERTa model, particularly under temporal generalization. Results show that discussion most often focused on U.S. strikes, while Israel and Iran were less frequently evaluated directly. During the event window, support for U.S. strikes increased among applicable comments. Topic modeling identified recurring frames of escalation, legitimacy, accountability, and regional security.

Özet

Bu çalışma, İran–İsrail 12 gün savaşı sürecinde çevrim içi izleyicilerin ABD’nin saldırıları, İsrail ve İran olmak üzere birbiriyle ilişkili üç hedefe yönelik duruşlarını ve yorumlayıcı çerçevelerini, önde gelen ABD haber videolarındaki YouTube yorumları üzerinden analiz etmektedir. 112.000 yorumdan oluşan derlem, olay öncesi, olay penceresi ve olay sonrası dönemlere ayrılmıştır. Duruş, her hedef için ayrı ayrı Destek, Karşıtlık, Belirsiz ve Uygulanamaz olmak üzere dört etiket kullanılarak modellenmiş ve böylece değerlendirme yönü ile hedefin söylemdeki görünürlüğünün birbirinden ayrıştırılması mümkün olmuştur. İnsanlar tarafından etiketlenen ve çift kodlanan 2.500 yorumluk örneklem, anotasyon güvenilirliği testleri ve model eğitimi için kullanılmıştır. Sınıflandırıcılar, video örtüşmesini kontrol eden ve zamansal genelleştirilebilirliği dikkate alan veri sızıntısına dirençli protokollerle değerlendirilmiş, ardından tüm derleme uygulanmıştır. Bu değerlendirme koşullarında, TF-IDF ile Lojistik Regresyon modeli ortalama dört sınıflı Macro-F1 performansında en yüksek sonucu elde etmiş ve özellikle zamansal genelleme açısından hiyerarşik DeBERTa modelinden daha güçlü performans göstermiştir. Bulgular, tartışmanın çoğunlukla ABD saldırılarına odaklandığını; İsrail ve İran’ın ise daha seyrek biçimde doğrudan değerlendirildiğini göstermektedir. Olay penceresi sırasında, uygulanabilir yorumlar arasında ABD saldırılarına yönelik destek artmıştır. Konu modellemesi, tırmanma, meşruiyet, hesap verebilirlik ve bölgesel güvenlik gibi tekrar eden çerçeveleri ortaya koymuştur

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Cite This Article

Erbay, C. (2026). Event-Window Analysis of Public Discourse via Multi-Target Stance Classification During the 12-Day Iran–Israel War. TRT Akademi, Cilt: 11 Sayı: 28, 1170–1209. https://doi.org/10.37679/trta.1952921

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