A survey of textual cyber abuse detection using cutting-edge language models and large language models
It addresses the problem of online abuse detection for social media platforms and researchers, but it is a survey paper, so it is incremental in summarizing existing work.
This paper surveys the various forms of textual cyber abuse on social media, such as hate speech and cyberbullying, and analyzes how language models and large language models are used for both detecting and generating abusive content, aiming to contribute to online safety and ethics discussions.
The success of social media platforms has facilitated the emergence of various forms of online abuse within digital communities. This abuse manifests in multiple ways, including hate speech, cyberbullying, emotional abuse, grooming, and sexting. In this paper, we present a comprehensive analysis of the different forms of abuse prevalent in social media, with a particular focus on how emerging technologies, such as Language Models (LMs) and Large Language Models (LLMs), are reshaping both the detection and generation of abusive content within these networks. We delve into the mechanisms through which social media abuse is perpetuated, exploring the psychological and social impact. Additionally, we examine the dual role of advanced language models-highlighting their potential to enhance automated detection systems for abusive behavior while also acknowledging their capacity to generate harmful content. This paper aims to contribute to the ongoing discourse on online safety and ethics, offering insights into the evolving landscape of cyberabuse and the technological innovations that both mitigate and exacerbate it.