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

TL;DR

This paper presents a system for detecting propaganda techniques in news articles by leveraging sentence-level emotional salience features, achieving improved accuracy over BERT-only models.

Contribution

The study introduces the use of emotional salience features combined with BERT for propaganda detection, demonstrating their effectiveness in classifying multiple propaganda techniques.

Findings

Emotion features improve F1-score from 0.548 to 0.570.

System achieves 0.558 overall F1-score on test data.

High detection accuracy for 'loaded language' and 'name calling' techniques.

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).