@inproceedings{li-etal-2019-detection,
    title = "Detection of Propaganda Using Logistic Regression",
    author = "Li, Jinfen  and
      Ye, Zhihao  and
      Xiao, Lu",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5017",
    doi = "10.18653/v1/D19-5017",
    pages = "119--124",
    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.",
}