Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when hateful discourse may arise. Using graph transformer networks, coupled with modelling attention and BERT-level natural language processing, our approach can capture context and anticipate upcoming anti-social behaviour. In this paper, we offer a detailed qualitative analysis of this solution for hate speech detection in social networks, leading to insights into where the method has the most impressive outcomes in comparison with competitors and identifying scenarios where there are challenges to achieving ideal performance. Included is an exploration of the kinds of posts that permeate social media today, including the use of hateful images. This suggests avenues for extending our model to be more comprehensive. A key insight is that the focus on reasoning about the concept of context positions us well to be able to support multi-modal analysis of online posts. We conclude with a reflection on how the problem we are addressing relates especially well to the theme of dynamic change, a critical concern for all AI solutions for social impact. We also comment briefly on how mental health well-being can be advanced with our work, through curated content attuned to the extent of hate in posts.

翻译：我们的工作提出了一种预测社交媒体中仇恨言论的方法，强调了考虑帖子后续讨论对成功检测仇恨性话语出现的关键必要性。通过结合图Transformer网络、注意力建模与BERT级别的自然语言处理，我们的方法能够捕捉上下文并预测即将出现的反社会行为。本文对该解决方案在社交网络仇恨言论检测中的表现进行了详细的定性分析，揭示了该方法相较于竞争对手最突出的成效场景，并识别出实现理想性能时面临挑战的情形。研究还探讨了当今社交媒体中泛滥的各类帖子类型，包括仇恨图像的使用，这为扩展模型以提升其全面性提供了思路。一个关键启示在于，对上下文推理概念的关注使我们能够很好地支持在线帖子的多模态分析。最后，我们反思了所解决问题的核心与动态变化主题的紧密关联——这是所有具有社会影响力的人工智能解决方案的关键考量。此外，我们简要阐述了如何通过适配帖子中仇恨程度的策展内容，推动心理健康福祉的进步。