Digitalization shifts human communication to online platforms, which has many benefits but also builds up a space for antisocial online behavior (AOB) such as harassment, insult and other forms of hateful textual content. Online platforms have good reasons to monitor and moderate such content. The paper examines the viability of automatic content monitoring using deep machine learning and natural language processing (NLP). More specifically, we consolidate prior work in the field of antisocial online behavior detection and compare relevant approaches to recent NLP models in an empirical study. Covering important methodological advancements in NLP including bidirectional encoding, attention, hierarchical text representations, and pre-trained transformer-based language models, and extending previous approaches by introducing a pseudo-sentence hierarchical attention network, the paper provides a comprehensive summary of the state-of-affairs in NLP-based AOB detection, clarifies the detection accuracy that is attainable with today's technology, discusses whether this degree is sufficient for deploying deep learning-based text screening systems, and approaches the interpretability topic.

数字化将人类交流转移到在线平台上，这不仅有很多好处，而且还为反社会在线行为（AOB）（例如骚扰，侮辱和其他形式的可恨文本内容）提供了空间。在线平台有充分的理由来监视和管理此类内容。本文研究了使用深度机器学习和自然语言处理（NLP）进行自动内容监视的可行性。更具体地说，我们在反社会在线行为检测领域巩固了先前的工作，并在实证研究中将相关方法与最新的NLP模型进行了比较。涵盖了NLP的重要方法学进步，包括双向编码，注意，分层文本表示以及基于变压器的预训练语言模型，