<ul><li>Detecting abusive language in social media conversations is challenging due to the contextual nature of abusiveness.</li><li>Traditional Abusive Language Detection (ALD) models often overlook the conversational context, leading to unreliable performance metrics.</li><li>A novel approach is proposed in this paper using graph neural networks (GNNs) to model social media conversations as graphs, capturing comment relationships.</li><li>The GNN model outperforms context-agnostic baselines and linear context-aware methods, achieving significant improvements in F1 scores.</li></ul>

Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights

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