Algorithmic Panic, Cultural Misalignment, and Multimodal Over Filtration in AI Processing of Obfuscated Arabic Political Image-Texts about the USA–Israel–Iran Conflict

Abstract

This study examines how four AI models decode and interpret Arabic political texts containing obfuscated words embedded in 138 images extracted from YouTube and Instagram videos posted during the USA–Israel–Iran War (March–April 2026). It compares Gemini, Copilot, Claude, and Qwen 3.7 Plus, representing distinct political, cultural, and safety alignment ecosystems. The study also explores algorithmic panic behaviors triggered under uncertainty, and sheds light on cultural misalignment, geopolitical censorship, multimodal over filtration, and structural bias across the models. The 138 images contained 374 obfuscated Arabic words. Analysis of the translated texts revealed substantial variation in recognition and interpretation. Gemini translated Arabic texts in 94% of the images and correctly decoded 98% of the obfuscated words, exhibiting no signs of algorithmic panic, geopolitical censorship, or cultural misalignment. Claude correctly translated 54% of the images and 67% of the words, produced partially hallucinated translations (14%), generated faulty translations for 33%, deleted 9%, and replaced 13%. Qwen translated 54% of the images, yielded correct translations for 72% of the words, and produced fully hallucinated translations for 4%. It generated faulty translations for 27.5% of the words, including deletions (6%) and replaced words (8.5%). Claude and Qwen’s performance reflects soft moderation behaviors, including selective omission, substitution, and semantic weakening. Copilot successfully translated the Arabic texts in 65% of the images, and 72% of the words, but blocked 24% of the images and produced faulty translations for 11%, including hallucinations, deletions, and additions. The anomalous behavioral patterns exhibited by Copilot—specifically its hyper reactive filtration protocols, stochastic blocking, and systematic generation of non-semantic strings—indicate a deeper sociopolitical and structural phenomenon involving the intersection of geopolitical anxiety, keyword panic, and algorithmic censorship when processing grassroots, organically obfuscated Arabic journalistic and cultural texts. Results are discussed in terms of why Copilot’s filters blocked certain images but not others, why Claude and Qwen hallucinate instead of blocking, why they exhibit selective deletions and substitutions, and how these behaviors relate to sociocultural misalignment, Eurocentric algorithmic prejudices, keyword panic, and censorship as empirical proof of linguistic competence, among other factors.