CPL-NoViD: Context-Aware Prompt-based Learning for Norm Violation Detection in Online Communities
This work addresses the challenge of adapting machine learning models to diverse community rules for online moderators, representing a novel method for a known bottleneck rather than a foundational advancement.
The paper tackled the problem of detecting norm violations in online communities by introducing CPL-NoViD, a context-aware prompt-based learning method that outperforms baselines and establishes a new state-of-the-art, with improved performance across different rule types and adaptability in few-shot learning scenarios.
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.