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        <title>“Don’t discuss”: Investigating Semantic and Argumentative Features for Supervised Propagandist Message Detection and Classification</title>
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    <abstract>One of the mechanisms through which disinformation is spreading online, in particular through social media, is by employing propaganda techniques. These include specific rhetorical and psychological strategies, ranging from leveraging on emotions to exploiting logical fallacies. In this paper, our goal is to push forward research on propaganda detection based on text analysis, given the crucial role these methods may play to address this main societal issue. More precisely, we propose a supervised approach to classify textual snippets both as propaganda messages and according to the precise applied propaganda technique, as well as a detailed linguistic analysis of the features characterising propaganda information in text (e.g., semantic, sentiment and argumentation features). Extensive experiments conducted on two available propagandist resources (i.e., NLP4IF’19 and SemEval’20-Task 11 datasets) show that the proposed approach, leveraging different language models and the investigated linguistic features, achieves very promising results on propaganda classification, both at sentence- and at fragment-level.</abstract>
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%0 Conference Proceedings
%T “Don’t discuss”: Investigating Semantic and Argumentative Features for Supervised Propagandist Message Detection and Classification
%A Vorakitphan, Vorakit
%A Cabrio, Elena
%A Villata, Serena
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F vorakitphan-etal-2021-dont
%X One of the mechanisms through which disinformation is spreading online, in particular through social media, is by employing propaganda techniques. These include specific rhetorical and psychological strategies, ranging from leveraging on emotions to exploiting logical fallacies. In this paper, our goal is to push forward research on propaganda detection based on text analysis, given the crucial role these methods may play to address this main societal issue. More precisely, we propose a supervised approach to classify textual snippets both as propaganda messages and according to the precise applied propaganda technique, as well as a detailed linguistic analysis of the features characterising propaganda information in text (e.g., semantic, sentiment and argumentation features). Extensive experiments conducted on two available propagandist resources (i.e., NLP4IF’19 and SemEval’20-Task 11 datasets) show that the proposed approach, leveraging different language models and the investigated linguistic features, achieves very promising results on propaganda classification, both at sentence- and at fragment-level.
%U https://aclanthology.org/2021.ranlp-1.168
%P 1498-1507

Markdown (Informal)

[“Don’t discuss”: Investigating Semantic and Argumentative Features for Supervised Propagandist Message Detection and Classification](https://aclanthology.org/2021.ranlp-1.168) (Vorakitphan et al., RANLP 2021)

• “Don’t discuss”: Investigating Semantic and Argumentative Features for Supervised Propagandist Message Detection and Classification (Vorakitphan et al., RANLP 2021)

ACL

• Vorakit Vorakitphan, Elena Cabrio, and Serena Villata. 2021. “Don’t discuss”: Investigating Semantic and Argumentative Features for Supervised Propagandist Message Detection and Classification. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1498–1507, Held Online. INCOMA Ltd..