使用图Transformer网络和公共上下文预测Reddit上的仇恨讨论
Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
摘要：
我们提出了一个系统来预测社交媒体平台上的有害讨论。我们的解决方案使用上下文深度语言模型，并提出了集成最先进的图Transformer网络来分析初始帖子之后的所有对话的新想法。该框架还支持随着对话的展开而适应未来的评论。此外，我们还研究了针对特定社区的仇恨言论分析是否能更有效地检测仇恨讨论。我们对来自不同社区的333，487个Reddit讨论进行了评估。我们发现，特定于社区的建模提高了两倍的性能，与有限的上下文模型相比，捕获更广泛讨论的上下文的模型将准确率提高了28%（对于最讨厌的内容为35%）。
英文摘要：
We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and proposes the novel idea of integrating state-of-the-art Graph Transformer Networks to analyze all conversations that follow an initial post. This framework also supports adapting to future comments as the conversation unfolds. In addition, we study whether a community-specific analysis of hate speech leads to more effective detection of hateful discussions. We evaluate our approach on 333,487 Reddit discussions from various communities. We find that community-specific modeling improves performance two-fold and that models which capture wider-discussion context improve accuracy by 28\% (35\% for the most hateful content) compared to limited context models.
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。
