Large Language Models for Multi-label Propaganda Detection

TL;DR

This paper presents an ensemble-based approach for multi-label propaganda detection in text, identifying 21 propaganda techniques with a micro-F1 score of 59.73%, advancing automated misinformation detection.

Contribution

It introduces an ensemble model for multi-label propaganda detection and provides comprehensive ablation studies and future research directions.

Findings

Ensemble of five models achieves best performance.

Micro-F1 score of 59.73% on the task.

Detailed ablation studies conducted.

Abstract

The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. There are a total of 21 propaganda techniques to be detected. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73%. We also conduct comprehensive ablations and propose various future directions for this work.