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：本研究提出了一种预测社交媒体中仇恨言论的方法，强调了分析帖子后续讨论对于成功检测仇恨言论的关键必要性。通过结合图Transformer网络、注意力建模及BERT级自然语言处理，我们的方法能够捕捉上下文并预测即将发生的反社会行为。本文对该解决方案在社交网络仇恨言论检测中的性能进行了详细的定性分析，揭示了该方法相较于竞品最突出的表现场景，并指出了实现理想性能面临的挑战。研究还探讨了当前社交媒体中泛滥的各类帖子类型，包括仇恨图像的使用，这为扩展模型以增强全面性提供了方向。核心见解在于，对上下文概念的推理使我们能够支持对在线帖子的多模态分析。最后，我们反思了所研究的问题与动态变化这一关键主题的高度相关性——动态变化是所有具备社会影响力的人工智能解决方案的核心关切。我们还简要论述了如何通过本工作推进心理健康福祉，即对帖子中的仇恨程度进行内容精选调控。