Abstract
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.
Abstract (translated)
我们的研究工作推动了在社交媒体上预测仇恨言论的方法,强调了成功检测仇恨言论时需要考虑后续评论的必要性。利用图形Transformer网络建模注意力和BERT级别的自然语言处理,我们的方法可以捕获上下文并预测即将到来的反社会行为。在本文中,我们提供了对这个解决方案在社交媒体上仇恨言论检测的详细定性分析,从而揭示了该方法与竞争对手相比表现出最令人印象深刻的结果的地方,并识别了实现理想表现所面临的挑战的场景。其中还包括了对当今社交媒体上流行的 posts(包括仇恨图像的使用)的深入研究,这暗示了扩展我们模型更为全面的方法的途径。一个重要的 insights 是专注于推理上下文概念的位置,使我们能够支持在线评论的多模态分析。我们的结论是,我们所面临的问题特别与动态变化的主题相关,这是所有对社会影响AI解决方案的一个关键关注点。我们还简要讨论了通过编辑内容调整其对 post 仇恨程度的理解,如何通过我们的工作促进心理健康福祉。
URL
https://arxiv.org/abs/2301.10871
