# Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection

arXiv: https://arxiv.org/abs/1910.09982 · HTML: https://mlsanity.com/paper/1910.09982/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 24 of 24)

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- [Interpretable Propaganda Detection in News Articles](https://mlsanity.com/paper/2108.12802/) (2021)
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- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models](https://mlsanity.com/paper/2305.03336/) (2023)
- [Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study](https://mlsanity.com/paper/2310.14032/) (2023)
- [Discourse Structures Guided Fine-grained Propaganda Identification](https://mlsanity.com/paper/2310.18544/) (2023)
- [ArMeme: Propagandistic Content in Arabic Memes](https://mlsanity.com/paper/2406.03916/) (2024)
- [Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence](https://mlsanity.com/paper/2507.18343/) (2025)
- [Technological Approaches to Detecting Online Disinformation and Manipulation](https://mlsanity.com/paper/2108.11669/) (2021)
- [NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques](https://mlsanity.com/paper/2304.14179/) (2023)
- [Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse](https://mlsanity.com/paper/2402.09934/) (2024)
- [Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification](https://mlsanity.com/paper/2604.01936/) (2026)
- [Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques](https://mlsanity.com/paper/2305.19419/) (2023)
- [From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech](https://mlsanity.com/paper/2601.17132/) (2026)
- [Cultural Adaptation in Large Language Models for Political Discourse](https://mlsanity.com/paper/2605.23332/) (2026)
- [A Corpus of Persuasion Techniques in Slavic Languages](https://mlsanity.com/paper/2607.10715/) (2026)
- [Propaganda Forensics: Recovering the Generation Pipeline of an AI-Driven Influence Campaign](https://mlsanity.com/paper/2608.15746/) (2026)

## Abstract (arXiv)

We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at http://propaganda.qcri.org/nlp4if-shared-task/.