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，强调了考虑帖子后续讨论对于成功检测仇恨性话语的关键必要性。该方法结合图Transformer网络、注意力机制建模及BERT级自然语言处理技术，能够捕捉上下文语境并预判潜在的反社会行为。本文对社交网络中这一仇恨言论检测方案进行了详细的定性分析，揭示了该方法相较于竞争方案在哪些场景下表现出最显著优势，并识别了实现理想性能时面临挑战的情景。研究同时探讨了当今社交媒体中泛滥的各类帖子类型，包括仇恨性图像的使用，这为扩展模型以提升全面性指明了方向。核心洞见在于，对语境推理概念的聚焦使该方法能够有效支持多模态网络帖子分析。我们最后反思了所研究问题与"动态变化"主题的高度契合性——这是所有面向社会影响的人工智能解决方案的关键关切。我们还简要说明了本工作如何通过基于帖子仇恨程度进行内容筛选，助力心理健康福祉的提升。