Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field.

中文翻译：

---

自动检测网络欺凌的方法：一项调查

近年来，对网络欺凌检测的研究有所增加，部分原因是网络欺凌在社交媒体上的扩散及其对年轻人的不利影响。关于网络欺凌检测的自动化方法的工作越来越多。这些方法利用机器学习和自然语言处理技术来识别网络欺凌交换的特征，并通过将文本数据与识别的特征进行匹配来自动检测网络欺凌。在本文中，我们对已发表的关于网络欺凌检测方法的研究（通过 Scopus、ACM 和 IEEE Xplore 书目数据库确定）进行了系统回顾。在我们广泛的文献综述的基础上，我们将现有方法分为 4 个主要类别，即监督学习、基于词典、基于规则、和混合倡议方法。基于监督学习的方法通常使用 SVM 和朴素贝叶斯等分类器来开发网络欺凌检测的预测模型。基于词典的系统利用单词列表并使用列表中的单词来检测网络欺凌。基于规则的方法将文本与预定义的规则相匹配以识别欺凌行为，混合方案将基于人类的推理与上述一种或多种方法相结合。我们发现缺乏标记数据集和研究人员在开发检测系统时对网络欺凌的非整体考虑是网络欺凌检测研究面临的两个关键挑战。