Detection of Propaganda Using Logistic Regression

Jinfen Li, Zhihao Ye, Lu Xiao


Abstract
Various propaganda techniques are used to manipulate peoples perspectives in order to foster a predetermined agenda such as by the use of logical fallacies or appealing to the emotions of the audience. In this paper, we develop a Logistic Regression-based tool that automatically classifies whether a sentence is propagandistic or not. We utilize features like TF-IDF, BERT vector, sentence length, readability grade level, emotion feature, LIWC feature and emphatic content feature to help us differentiate these two categories. The linguistic and semantic features combination results in 66.16% of F1 score, which outperforms the baseline hugely.
Anthology ID:
D19-5017
Volume:
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | NLP4IF | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
119–124
Language:
URL:
https://aclanthology.org/D19-5017
DOI:
10.18653/v1/D19-5017
Bibkey:
Cite (ACL):
Jinfen Li, Zhihao Ye, and Lu Xiao. 2019. Detection of Propaganda Using Logistic Regression. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 119–124, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Detection of Propaganda Using Logistic Regression (Li et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5017.pdf