PropXplain: Can LLMs Enable Explainable Propaganda Detection?

ACL ARR 2025 May Submission5059 Authors

Abstract: 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 (anonymous.com).

Paper Type: Short

Research Area: Resources and Evaluation

Research Area Keywords: misinformation detection and analysis, hateful meme detection, corpus creation, datasets for low resource languages

Contribution Types: Approaches to low-resource settings, Publicly available software and/or pre-trained models, Data resources

Languages Studied: Arabic, English

Submission Number: 5059