@inproceedings{krishnamurthy-etal-2020-soccogcom,
    title = "{S}oc{C}og{C}om at {S}em{E}val-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features",
    author = "Krishnamurthy, Gangeshwar  and
      Gupta, Raj Kumar  and
      Yang, Yinping",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.semeval-1.235/",
    doi = "10.18653/v1/2020.semeval-1.235",
    pages = "1793--1801",
    abstract = "This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the {\textquotedblleft}loaded language{\textquotedblright} and {\textquotedblleft}slogan{\textquotedblright} techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, {\textquotedblleft}flag waving{\textquotedblright} and {\textquotedblleft}appeal to fear-prejudice{\textquotedblright} have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting {\textquotedblleft}loaded language{\textquotedblright} (F1 = 0.772), {\textquotedblleft}name calling and labeling{\textquotedblright} (F1 = 0.673), {\textquotedblleft}doubt{\textquotedblright} (F1 = 0.604) and {\textquotedblleft}flag waving{\textquotedblright} (F1 = 0.543)."
}