Antisocial Online Behavior Detection Using Deep Learning

IRTG 1792 Discussion Paper 2019-029

33 Pages Posted: 28 Aug 2020

See all articles by Elizaveta Zinovyeva

Elizaveta Zinovyeva

Humboldt University of Berlin

Wolfgang Karl Härdle

Blockchain Research Center Humboldt-Universität zu Berlin; Charles University; National Yang Ming Chiao Tung University; Asian Competitiveness Institute; Academy of Economic Studies, Bucharest

Stefan Lessmann

School of Business and Economics, Humboldt-University of Berlin

Date Written: November 21, 2019

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 cyber-bullying, hate speech, cyber-aggression 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, Cyber-bullying, Antisocial Online Behavior, Attention Mechanism, Text Classification

JEL Classification: C00

Suggested Citation

Zinovyeva, Elizaveta and Härdle, Wolfgang Karl and Lessmann, Stefan, Antisocial Online Behavior Detection Using Deep Learning (November 21, 2019). IRTG 1792 Discussion Paper 2019-029, Available at SSRN: https://ssrn.com/abstract=3657352 or http://dx.doi.org/10.2139/ssrn.3657352

Elizaveta Zinovyeva (Contact Author)

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Wolfgang Karl Härdle

Blockchain Research Center Humboldt-Universität zu Berlin ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Asian Competitiveness Institute ( email )

Singapore

Academy of Economic Studies, Bucharest ( email )

Bucharest
Romania

Stefan Lessmann

School of Business and Economics, Humboldt-University of Berlin ( email )

Unter den Linden 6
Berlin, Berlin 10099
Germany

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