Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/D19-1565 | EMNLP/IJCNLP (1) |
DocType | Volume | ISSN |
Conference | D19-1 | EMNLP-2019 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giovanni Da San Martino | 1 | 236 | 27.08 |
| Seunghak Yu | 2 | 4 | 1.07 |
| Alberto Barrón-Cedeño | 3 | 346 | 29.35 |
| Rostislav Petrov | 4 | 3 | 0.39 |
| Preslav I. Nakov | 5 | 1771 | 138.66 |