• DOI:10.18653/v1/D19-1565
• Corpus ID: 202788575

Fine-Grained Analysis of Propaganda in News Article

@inproceedings{DaSanMartino2019FineGrainedAO,
  title={Fine-Grained Analysis of Propaganda in News Article},
  author={Giovanni Da San Martino and Seunghak Yu and Alberto Barr{\'o}n-Cede{\~n}o and Rostislav Petrov and Preslav Nakov},
  booktitle={Conference on Empirical Methods in Natural Language Processing},
  year={2019}
}

• Giovanni Da San Martino, Seunghak Yu, +2 authors Preslav Nakov
• Published in
  Conference on Empirical…
  1 October 2019
• Computer Science

Propaganda aims at influencing people’s mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at fragment level with eighteen propaganda techniques and propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.