The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed repeatedly becomes cyberbullying a social problem taking place in virtual environments, yet with real-world consequences, such as depression, withdrawal, or even suicide attempts of its victims. Systems for the automatic detection and mitigation of cyberbullying have been developed but, unfortunately, the vast majority of them are for the English language, with only a handful available for low-resource languages. To estimate the present state of research and recognize the needs for further development, in this paper we present a comprehensive systematic survey of studies done so far for automatic cyberbullying detection in low-resource languages. We analyzed all studies on this topic that were available. We investigated more than seventy published studies on automatic detection of cyberbullying or related language in low-resource languages and dialects that were published between around 2017 and January 2023. There are 23 low-resource languages and dialects covered by this paper, including Bangla, Hindi, Dravidian languages and others. In the survey, we identify some of the research gaps of previous studies, which include the lack of reliable definitions of cyberbullying and its relevant subcategories, biases in the acquisition, and annotation of data. Based on recognizing those research gaps, we provide some suggestions for improving the general research conduct in cyberbullying detection, with a primary focus on low-resource languages. Based on those proposed suggestions, we collect and release a cyberbullying dataset in the Chittagonian dialect of Bangla and propose a number of initial ML solutions trained on that dataset. In addition, pre-trained transformer-based the BanglaBERT model was also attempted.

翻译：社交媒体平台在及时审核内容方面面临的困境，使用户滥用此类平台传播粗俗或攻击性语言的现象屡禁不止。当这种语言行为反复出现时，便演变为网络欺凌——一种发生在虚拟环境中，却可能带来现实后果的社会问题，例如受害者出现抑郁、社交退缩甚至自杀倾向。针对网络欺凌的自动检测与缓解系统已有开发成果，但遗憾的是，绝大多数系统仅适用于英语，而面向低资源语言的系统屈指可数。为评估当前研究现状并识别未来发展需求，本文对低资源语言中网络欺凌自动检测的相关研究进行了系统性综述。我们分析了该领域所有可获取的研究成果，调查了约2017年至2023年1月间发表的七十余篇关于低资源语言及方言中网络欺凌或相关语言自动检测的文献。本文涵盖23种低资源语言及方言，包括孟加拉语、印地语、德拉维达语族等。通过综述，我们识别出先前研究存在的若干空白，例如网络欺凌及其相关子类别缺乏可靠定义，数据采集与标注中存在偏差等。基于对这些研究空白的认知，我们提出若干改进建议，以期提升网络欺凌检测领域（尤其聚焦低资源语言）的整体研究规范。依据所提建议，我们收集并发布了基于孟加拉语吉大港方言的网络欺凌数据集，并提出了多个基于该数据集的初始机器学习解决方案。此外，我们还尝试使用了基于预训练Transformer的BanglaBERT模型。