用社区规范做无监督预训练，让社交模型更懂群体规则。
Community Norms in the Spotlight: Enabling Task-Agnostic Unsupervised Pre-Training to Benefit Online Social Media
- 基于社区规范构建无监督预训练框架，替代依赖标注数据的微调。
- 在多个社交平台数据集上实现优于有监督方法的对话建模效果。
- 适合关注社会良知AI、反谣言与反歧视研究的学者和开发者。
建模在线社交平台的复杂动态对应对仇恨言论和虚假信息等挑战至关重要。尽管讨论变换器（Discussion Transformers）将对话建模为图结构，展现出良好前景，但其应用严重受限于高质量人工标注数据的稀缺。本文主张从任务特定微调转向基于全新社区规范理念的无监督预训练范式。该框架不仅缓解了数据短缺问题，还使人工智能系统决策背后的社交规范具备可解释性。我们相信这一方向为社会向善的AI发展提供了广阔机遇。
原文摘要 · Abstract (English)
Modelling the complex dynamics of online social platforms is critical for addressing challenges such as hate speech and misinformation. While Discussion Transformers, which model conversations as graph structures, have emerged as a promising architecture, their potential is severely constrained by reliance on high-quality, human-labelled datasets. In this paper, we advocate a paradigm shift from task-specific fine-tuning to unsupervised pretraining, grounded in an entirely novel consideration of community norms. We posit that this framework not only mitigates data scarcity but also enables interpretation of the social norms underlying the decisions made by such an AI system. Ultimately, we believe that this direction offers many opportunities for AI for Social Good.
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