What are the propaganda types?
Although not all of the 18 types are listed, they include using loaded language or appeal to authority and slogans, using logical fallacies such as strawmen, hidden ad-hominen fallacies ad red herrings. 
Many recent political events, like the 2016 US Presidential elections or the 2018 Brazilian elections have raised the attention of institutions and of the general public on the role of Internet and social media in influencing the outcome of these events. We argue that a safe democracy is one in which citizens have tools to make them aware of propaganda campaigns. We propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.
Journalistic organisations, such as Media Bias/Fact Check, provide reports on news sources highlighting the ones that are propagandistic. Obviously, such analysis is time-consuming and possibly biased and it cannot be applied to the enormous amount of news that flood social media and the Internet. Research on detecting propaganda has focused primarily on classifying entire articles as propagandistic/non-propagandistic BIBREF0, BIBREF1, BIBREF2. Such learning systems are trained using gold labels obtained by transferring the label of the media source, as per Media Bias/Fact Check judgment, to each of its articles. Such distant supervision setting inevitably introduces noise in the learning process BIBREF3 and the resulting systems tend to lack explainability. We argue that in order to study propaganda in a sound and reliable way, we need to rely on high-quality trusted professional annotations and it is best to do so at the fragment level, targeting specific techniques rather than using a label for an entire document or an entire news outlet. Therefore, we propose a novel task: identifying specific instances of propaganda techniques used within an article. In particular, we design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines. Our corpus could enable research in propagandistic and non-objective news, including the development of explainable AI systems. A system that can detect instances of use of specific propagandistic techniques would be able to make it explicit to the users why a given article was predicted to be propagandistic. It could also help train the users to spot the use of such techniques in the news.