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• W2978871345 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." @default.
• W2978871345 created "2019-10-10" @default.
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• W2978871345 date "2019-10-06" @default.
• W2978871345 modified "2023-09-23" @default.
• W2978871345 title "Fine-Grained Analysis of Propaganda in News Articles" @default.
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