Antisocial Online Behavior Detection Using Deep Learning

Elizaveta Zinovyeva, Wolfgang Härdle and Stefan Lessmann

No 2019-029, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: The shift of human communication to online platforms brings many benefits to society due to the ease of publication of opinions, sharing experience, getting immediate feedback and the opportunity to discuss the hottest topics. Besides that, it builds up a space for antisocial behavior such as harassment, insult and hate speech. This research is dedicated to detection of antisocial online behavior detection (AOB) - an umbrella term for cyberbullying, hate speech, cyberaggression and use of any hateful textual content. First, we provide a benchmark of deep learning models found in the literature on AOB detection. Deep learning has already proved to be efficient in different types of decision support: decision support from financial disclosures, predicting process behavior, text-based emoticon recognition. We compare methods of traditional machine learning with deep learning, while applying important advancements of natural language processing: we examine bidirectional encoding, compare attention mechanisms with simpler reduction techniques, and investigate whether the hierarchical representation of the data and application of attention on different layers might improve the predictive performance. As a partial contribution of the final hierarchical part, we introduce pseudo-sentence hierarchical attention network, an extension of hierarchical attention network – a recent advancement in document classification.

Keywords: Deep Learning; Cyberbullying; Antisocial Online Behavior; Attention Mechanism; Text Classification (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2019029

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