# SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles

arXiv: https://arxiv.org/abs/2009.02696 · HTML: https://mlsanity.com/paper/2009.02696/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2020.

## Built on by (most cited first, 30 of 81)

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- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
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- [Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models](https://mlsanity.com/paper/2410.20940/) (2024)
- [Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification](https://mlsanity.com/paper/2308.07282/) (2023)
- [PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent](https://mlsanity.com/paper/2409.18997/) (2024)
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- [PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks](https://mlsanity.com/paper/2210.08855/) (2022)

## Abstract (arXiv)

We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about Span Identification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250 teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles.