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.

本文介绍了一种利用图转换网络、注意力和 BERT 等自然语言处理技术来预测社交媒体中仇恨言论的方法，并分析了该方法在检测效果上的优劣和扩展性。研究发现，关注上下文可以支持多模态的在线帖子分析，从而解决社会影响的 AI 解决方案中面临的动态变化的问题，并通过策划内容来增进心理健康。

基于图变换的定性分析，应对仇恨言论：适应内容动态变化