CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online Communities

TL;DR

CPL-NoViD introduces a prompt-based, context-aware approach for detecting norm violations in online communities, outperforming existing methods and demonstrating adaptability across different rules, communities, and few-shot scenarios.

Contribution

The paper presents a novel prompt-based learning method that effectively detects norm violations across diverse online community rules and contexts, surpassing previous benchmarks.

Findings

Outperforms baseline models in norm violation detection

Effective in cross-rule-type and cross-community scenarios

Excels in few-shot learning settings

Abstract

Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions. Existing machine learning approaches often struggle to adapt to the diverse rules and interpretations across different communities due to the inherent challenges of fine-tuning models for such context-specific tasks. In this paper, we introduce Context-aware Prompt-based Learning for Norm Violation Detection (CPL-NoViD), a novel method that employs prompt-based learning to detect norm violations across various types of rules. CPL-NoViD outperforms the baseline by incorporating context through natural language prompts and demonstrates improved performance across different rule types. Significantly, it not only excels in cross-rule-type and cross-community norm violation detection but also exhibits adaptability in few-shot learning scenarios. Most notably, it establishes a new state-of-the-art in norm violation detection, surpassing existing benchmarks. Our work highlights the potential of prompt-based learning for context-sensitive norm violation detection and paves the way for future research on more adaptable, context-aware models to better support online community moderators.