SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features

Gangeshwar Krishnamurthy, Raj Kumar Gupta, Yinping Yang


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 “loaded language” and “slogan” techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, “flag waving” and “appeal to fear-prejudice” 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 “loaded language” (F1 = 0.772), “name calling and labeling” (F1 = 0.673), “doubt” (F1 = 0.604) and “flag waving” (F1 = 0.543).
Anthology ID:
2020.semeval-1.235
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1793–1801
Language:
URL:
https://aclanthology.org/2020.semeval-1.235
DOI:
10.18653/v1/2020.semeval-1.235
Bibkey:
Cite (ACL):
Gangeshwar Krishnamurthy, Raj Kumar Gupta, and Yinping Yang. 2020. SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1793–1801, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features (Krishnamurthy et al., SemEval 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.235.pdf
Code
 gangeshwark/PropagandaNews