Culturally Adaptive Explainable LLM Assessment for Multilingual Information Disorder

Information disorder, understood as the deliberate or incidental spread of misleading, manipulative, or decontextualized content, does not manifest the same way across languages and cultures. The rhetorical strategies, ideological markers, and framing patterns that signal bias in one linguistic community may read as neutral reporting in another. Yet most automated detection systems apply the same models and reasoning patterns regardless of cultural context, producing what this thesis terms culturally misaligned rationales: fluent explanations that fail to engage with the specific discursive and cultural logic of the target community. Current large language models are predominantly English-centric and monocultural in their explanatory reasoning, and this thesis argues that addressing cultural blindness in automated information disorder assessment requires moving beyond static few-shot prompting toward community-aligned adaptive approaches. To this end, a Culturally Adaptive Retrieval-Based Framework is proposed and evaluated on the InDor corpus across Farsi and Italian using LLaMA4 Maverick and Mixtral-8x22B-Instruct, comparing four prompting conditions across three evaluation tasks: severity classification, span detection, and rationale generation quality. Native speakers of both languages rated retrieval-based rationales higher than the static baseline in a blind A/B human evaluation, with the improvement more pronounced in Farsi. All code, prompts, evaluation scripts, and supplementary resources, including a curated landscape review of 108 fake news and information disorder datasets, are made publicly available.