TWEETSPIN: Fine-grained Propaganda Detection in Social Media Using Multi-View Representations

Prashanth Vijayaraghavan, Soroush Vosoughi


Abstract
Recently, several studies on propaganda detection have involved document and fragment-level analyses of news articles. However, there are significant data and modeling challenges dealing with fine-grained detection of propaganda on social media. In this work, we present TWEETSPIN, a dataset containing tweets that are weakly annotated with different fine-grained propaganda techniques, and propose a neural approach to detect and categorize propaganda tweets across those fine-grained categories. These categories include specific rhetorical and psychological techniques, ranging from leveraging emotions to using logical fallacies. Our model relies on multi-view representations of the input tweet data to (a) extract different aspects of the input text including the context, entities, their relationships, and external knowledge; (b) model their mutual interplay; and (c) effectively speed up the learning process by requiring fewer training examples. Our method allows for representation enrichment leading to better detection and categorization of propaganda on social media. We verify the effectiveness of our proposed method on TWEETSPIN and further probe how the implicit relations between the views impact the performance. Our experiments show that our model is able to outperform several benchmark methods and transfer the knowledge to relatively low-resource news domains.
Anthology ID:
2022.naacl-main.251
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3433–3448
Language:
URL:
https://aclanthology.org/2022.naacl-main.251
DOI:
10.18653/v1/2022.naacl-main.251
Bibkey:
Cite (ACL):
Prashanth Vijayaraghavan and Soroush Vosoughi. 2022. TWEETSPIN: Fine-grained Propaganda Detection in Social Media Using Multi-View Representations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3433–3448, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
TWEETSPIN: Fine-grained Propaganda Detection in Social Media Using Multi-View Representations (Vijayaraghavan & Vosoughi, NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.251.pdf