| Autor: |
Martino, Giovanni Da San, Yu, Seunghak, Barrón-Cedeño, Alberto, Petrov, Rostislav, Nakov, Preslav |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
EMNLP-2019 |
| Druh dokumentu: |
Working Paper |
| Popis: |
Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the 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 the fragment level with eighteen propaganda techniques and we 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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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