# Interpretable Propaganda Detection in News Articles

arXiv: https://arxiv.org/abs/2108.12802 · HTML: https://mlsanity.com/paper/2108.12802/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 29 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://mlsanity.com/paper/2009.02696/) (2020)
- [A Survey on Computational Propaganda Detection](https://mlsanity.com/paper/2007.08024/) (2020)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context](https://mlsanity.com/paper/2005.04518/) (2020)
- [Argotario: Computational Argumentation Meets Serious Games](https://mlsanity.com/paper/1707.06002/) (2017)
- [Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection](https://mlsanity.com/paper/1910.09982/) (2019)
- [Prta: A System to Support the Analysis of Propaganda Techniques in the News](https://mlsanity.com/paper/2005.05854/) (2020)
- [Proppy: A System to Unmask Propaganda in Online News](https://mlsanity.com/paper/1912.06810/) (2019)
- [Transformers: "The End of History" for NLP?](https://mlsanity.com/paper/2105.00813/) (2021)
- [Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum](https://mlsanity.com/paper/1606.06356/) (2016)
- [Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection](https://mlsanity.com/paper/1904.03513/) (2019)
- [Experiments in Detecting Persuasion Techniques in the News](https://mlsanity.com/paper/1911.06815/) (2019)

## Built on by (most cited first, 17 of 17)

- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](https://mlsanity.com/paper/2212.07425/) (2022)
- [Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Tool](https://mlsanity.com/paper/2402.19135/) (2024)
- [PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent](https://mlsanity.com/paper/2409.18997/) (2024)
- [PropXplain: Can LLMs Enable Explainable Propaganda Detection?](https://mlsanity.com/paper/2502.16550/) (2025)
- [Exploring news intent and its application: A theory-driven approach](https://mlsanity.com/paper/2312.16490/) (2023)
- [Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram posts](https://mlsanity.com/paper/2301.10604/) (2023)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [Discourse Structures Guided Fine-grained Propaganda Identification](https://mlsanity.com/paper/2310.18544/) (2023)
- [GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text](https://mlsanity.com/paper/2407.11827/) (2024)
- [Human Values in a Single Sentence: Moral Presence, Hierarchies, and Transformer Ensembles on the Schwartz Continuum](https://mlsanity.com/paper/2601.14172/) (2026)
- [Detecting Propaganda Techniques in Code-Switched Social Media Text](https://mlsanity.com/paper/2305.14534/) (2023)
- [Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse](https://mlsanity.com/paper/2402.09934/) (2024)
- [Large Language Models for Multi-label Propaganda Detection](https://mlsanity.com/paper/2210.08209/) (2022)
- [Propaganda Forensics: Recovering the Generation Pipeline of an AI-Driven Influence Campaign](https://mlsanity.com/paper/2608.15746/) (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)

Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to achieve a healthier and safer online news and media consumption. Automatic systems are able to support humans in detecting such content; yet, a major impediment to their broad adoption is that besides being accurate, the decisions of such systems need also to be interpretable in order to be trusted and widely adopted by users. Since misleading and propagandistic content influences readers through the use of a number of deception techniques, we propose to detect and to show the use of such techniques as a way to offer interpretability. In particular, we define qualitatively descriptive features and we analyze their suitability for detecting deception techniques. We further show that our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.