@inproceedings{assem-2026-whose,
    title = "Whose Pragmatics? Cultural Grounding as a Bottleneck for Stereotype Detection in {E}gyptian {A}rabic Social Media",
    author = "Assem, Samar A.",
    editor = "Ma, Weicheng  and
      Vosoughi, Soroush  and
      Gillani, Nabeel  and
      Coto-Solano, Rolando",
    booktitle = "Proceedings of the 1st Workshop on Stereotypes Across Cultures in Language Technologies ({S}tere{AC}u{LT} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.stereacult-1.7/",
    pages = "69--78",
    ISBN = "979-8-89176-408-8",
    abstract = "Stereotype detection benchmarks assume that stereotyping occurs through what is said {---} via lexical co-occurrence between demographic terms and stereotypical attributes. We argue that stereotyping is often conveyed by what is meant: through presupposition, implicature, and speech-act framing that leave surface content unchanged while embedding prejudice in the pragmatic layer. We call this phenomenon pragmatic stereotyping. Evaluating GPT-4 and Claude 3.5 Sonnet on a stratified sample of 500 Egyptian Arabic social media comments annotated with a seven-tag sentiment/(im)politeness taxonomy, we find that cultural grounding is the critical bottleneck in detecting pragmatic stereotyping in non-English discourse. About 35{\%} of LLM errors result from cultural grounding gaps, leading to a 15-percentage-point F1 difference between explicit tags (0.81) and implicit tags (0.66). These failures are bidirectional: on the author side, LLMs under-detect prejudice encoded through concessive presupposition and backhanded compliments; on the model side, LLMs apply English-based pragmatic assumptions, misinterpreting genuine polite criticism as sarcasm and positive-intended impoliteness as conflictive. Our five-layer Chain-of-Thought diagnostic framework localizes these failures to the culture-dependent inference layers. These results extend stereotype evaluation beyond lexical benchmarks and have direct implications for content moderation pipelines serving Arabic-speaking communities."
}