# PropXplain: Can LLMs Enable Explainable Propaganda Detection?

arXiv: https://arxiv.org/abs/2502.16550 · HTML: https://mlsanity.com/paper/2502.16550/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 23 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech](https://mlsanity.com/paper/2302.07736/) (2023)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [Generating Fact Checking Explanations](https://mlsanity.com/paper/2004.05773/) (2020)
- [Evaluating GPT-3 Generated Explanations for Hateful Content Moderation](https://mlsanity.com/paper/2305.17680/) (2023)
- [Benchmarking the Generation of Fact Checking Explanations](https://mlsanity.com/paper/2308.15202/) (2023)
- [Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles](https://mlsanity.com/paper/2402.17478/) (2024)
- [ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text](https://mlsanity.com/paper/2311.03179/) (2023)
- [Interpretable Propaganda Detection in News Articles](https://mlsanity.com/paper/2108.12802/) (2021)
- [ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?](https://mlsanity.com/paper/2403.17368/) (2024)
- [Proppy: A System to Unmask Propaganda in Online News](https://mlsanity.com/paper/1912.06810/) (2019)
- [ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content](https://mlsanity.com/paper/2407.04247/) (2024)
- [Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Tool](https://mlsanity.com/paper/2402.19135/) (2024)
- [Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models](https://mlsanity.com/paper/2206.06356/) (2022)

## Built on by (most cited first, 8 of 8)

- [MemeLens: Multilingual Multitask VLMs for Memes](https://mlsanity.com/paper/2601.12539/) (2026)
- [Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation](https://mlsanity.com/paper/2604.05083/) (2026)
- [LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems](https://mlsanity.com/paper/2601.16890/) (2026)
- [CritiSense: Critical Digital Literacy and Resilience Against Misinformation](https://mlsanity.com/paper/2603.16672/) (2026)
- [Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas](https://mlsanity.com/paper/2605.13663/) (2026)
- [AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes](https://mlsanity.com/paper/2607.27393/) (2026)
- [ProBel: Propaganda Detection with Techniques, Spans, and Explanations](https://mlsanity.com/paper/2608.22388/) (2026)
- [Reasoning for Propaganda Detection via Label-Guided CoT Optimisation](https://mlsanity.com/paper/2610.17271/) (2026)

## Abstract (arXiv)

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).