Analysis of Socially Unacceptable Discourse with Zero-shot Learning

TL;DR

This paper explores the use of zero-shot learning with pre-trained transformer models to detect and analyze socially unacceptable discourse online, aiming to improve tools for responsible communication.

Contribution

It introduces an entailment-based zero-shot classification approach for SUD detection, demonstrating its effectiveness without requiring labeled training data.

Findings

Good generalization to unseen data

Effective in characterizing extremist narratives

Supports development of robust SUD analysis tools

Abstract

Socially Unacceptable Discourse (SUD) analysis is crucial for maintaining online positive environments. We investigate the effectiveness of Entailment-based zero-shot text classification (unsupervised method) for SUD detection and characterization by leveraging pre-trained transformer models and prompting techniques. The results demonstrate good generalization capabilities of these models to unseen data and highlight the promising nature of this approach for generating labeled datasets for the analysis and characterization of extremist narratives. The findings of this research contribute to the development of robust tools for studying SUD and promoting responsible communication online.