Fine-Grained Analysis of Propaganda in News Articles 


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Our previous work on propaganda has addressed propaganda detection at the document level.

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.


The paper describing in details the corpus and our algorithm. Please use the bibtex file if you want to cite our work. 

Code and Data

 The code used in the experiments is available here. 


Wanna see our system in action? Check our demo, which collects articles on a topic and shows statistics on the propaganda techniques as well as all techniques for every article. 

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