SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda using Sentence-Level Emotional Salience Features
This work addresses propaganda detection for media analysis, but it is incremental as it builds on existing methods with emotional features.
The paper tackled detecting propaganda techniques in news articles by using emotional salience features, finding that combining these with BERT features improved F1-score from 0.548 to 0.570 and achieved a micro-averaged F1-score of 0.558 on test data.
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).