Detecting abusive language in social media conversations poses significant challenges, as identifying abusiveness often depends on the conversational context, characterized by the content and topology of preceding comments. Traditional Abusive Language Detection (ALD) models often overlook this context, which can lead to unreliable performance metrics. Recent Natural Language Processing (NLP) methods that integrate conversational context often depend on limited and simplified representations, and report inconsistent results. In this paper, we propose a novel approach that utilize graph neural networks (GNNs) to model social media conversations as graphs, where nodes represent comments, and edges capture reply structures. We systematically investigate various graph representations and context windows to identify the optimal configuration for ALD. Our GNN model outperform both context-agnostic baselines and linear context-aware methods, achieving significant improvements in F1 scores. These findings demonstrate the critical role of structured conversational context and establish GNNs as a robust framework for advancing context-aware abusive language detection.

本研究针对社交媒体对话中滥用语言检测面临的挑战，提出了一种新颖的方法，利用图神经网络将社交媒体对话建模为图结构，节点表示评论，边代表回复关系。通过系统研究各种图表示和上下文窗口，研究表明该方法在滥用语言检测中显著提高了F1得分，体现了结构化对话上下文的重要性。

图形化讨论：揭示社交媒体中的滥用语言