Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection

TL;DR

This paper introduces a novel approach for propaganda detection in news articles by integrating declarative knowledge in both first-order logic and natural language to improve model accuracy.

Contribution

The method incorporates declarative knowledge into the training process, using logical consistency and technique definitions to enhance fine-grained propaganda detection.

Findings

Achieves superior performance on Propaganda Techniques Corpus

Regularizes training with logical consistency constraints

Utilizes natural language definitions for class representation

Abstract

We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.