@inproceedings{hasanain-etal-2025-propxplain,
title = "{P}rop{X}plain: Can {LLM}s Enable Explainable Propaganda Detection?",
author = "Hasanain, Maram and
Hasan, Md Arid and
Kmainasi, Mohamed Bayan and
Sartori, Elisa and
Shahroor, Ali Ezzat and
Da San Martino, Giovanni and
Alam, Firoj",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1296/",
doi = "10.18653/v1/2025.findings-emnlp.1296",
pages = "23855--23863",
ISBN = "979-8-89176-335-7",
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 (\url{https://github.com/firojalam/PropXplain})."
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<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 (https://github.com/firojalam/PropXplain).</abstract>
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%0 Conference Proceedings
%T PropXplain: Can LLMs Enable Explainable Propaganda Detection?
%A Hasanain, Maram
%A Hasan, Md Arid
%A Kmainasi, Mohamed Bayan
%A Sartori, Elisa
%A Shahroor, Ali Ezzat
%A Da San Martino, Giovanni
%A Alam, Firoj
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F hasanain-etal-2025-propxplain
%X 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).
%R 10.18653/v1/2025.findings-emnlp.1296
%U https://aclanthology.org/2025.findings-emnlp.1296/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1296
%P 23855-23863
Markdown (Informal)
[PropXplain: Can LLMs Enable Explainable Propaganda Detection?](https://aclanthology.org/2025.findings-emnlp.1296/) (Hasanain et al., Findings 2025)
ACL
- Maram Hasanain, Md Arid Hasan, Mohamed Bayan Kmainasi, Elisa Sartori, Ali Ezzat Shahroor, Giovanni Da San Martino, and Firoj Alam. 2025. PropXplain: Can LLMs Enable Explainable Propaganda Detection?. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 23855–23863, Suzhou, China. Association for Computational Linguistics.