社区规范受到关注：实现任务无关的无监督预训练以造福在线社交媒体
Community Norms in the Spotlight: Enabling Task-Agnostic Unsupervised Pre-Training to Benefit Online Social Media
AI总结：
本文提出通过无监督预训练实现任务无关的在线社交媒体AI系统，以更好地理解和应对社区规范问题。
摘要：
对在线社交平台复杂动态进行建模对于解决仇恨言论和虚假信息等挑战至关重要。尽管讨论转换器（将对话建模为图结构）已出现作为有前途的架构，但其潜力受到对高质量、人工标注数据集的依赖的严重限制。在本文中，我们倡导从任务特定微调转向无监督预训练的范式转变，这基于完全新的社区规范考虑。我们提出，这种框架不仅缓解了数据稀缺问题，还使能够解释人工智能系统所基于的社会规范。最终，我们相信这种方向为人工智能的社会公益提供了许多机会。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
