Antisocial online behavior detection using deep learning

• Published in: Decision Support Systems 2020-11, Vol.138, p.113362, Article 113362
• Main Authors: Zinovyeva, Elizaveta, Härdle, Wolfgang Karl, Lessmann, Stefan
• Format: Article
• Language: English
• Subjects: Antisocial online behavior; Attention mechanism; Cyberbullying; Deep learning; Digitization; Human communication; Machine learning; Natural language processing; Text classification
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2020.113362

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. •Comprehensive benchmark on deep learning regimes for AOB detection.•Usage of transformer-based language models.•Interpretability module to understand the model's logic.•Interpretability module to detect unintended bias.