PropXplain: Can LLMs Enable Explainable Propaganda Detection?
It addresses the need for explainable AI in propaganda detection for researchers and practitioners, though it is incremental by building on existing detection methods.
The paper tackled the lack of explainability in propaganda detection by creating a multilingual dataset with explanations and an LLM model for detection and explanation generation, achieving comparable performance while providing rationales.
There has been significant research on propagandistic content detection across different modalities and languages. However, most studies have primarily focused on detection, with little attention given to explanations justifying the predicted label. This is largely due to the lack of resources that provide explanations alongside annotated labels. To address this issue, we propose a multilingual (i.e., Arabic and English) explanation-enhanced dataset, the first of its kind. Additionally, we introduce an explanation-enhanced LLM for both label detection and rationale-based explanation generation. Our findings indicate that the model performs comparably while also generating explanations. We will make the dataset and experimental resources publicly available for the research community (https://github.com/firojalam/PropXplain).