Arbeitspapier

Antisocial Online Behavior Detection Using Deep Learning

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.

Language: Englisch

Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2019-029

Classification: Wirtschaft
Mathematical and Quantitative Methods: General

Subject: Deep Learning
Cyberbullying
Antisocial Online Behavior
Attention Mechanism
Text Classification

Event: Geistige Schöpfung

(who): Zinovyeva, Elizaveta
Härdle, Wolfgang Karl
Lessmann, Stefan

Event: Veröffentlichung

(who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

(where): Berlin

(when): 2019

Handle: http://hdl.handle.net/10419/230805

Last update: 10.03.2025, 11:43 AM CET

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Arbeitspapier

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Zinovyeva, Elizaveta
Härdle, Wolfgang Karl
Lessmann, Stefan
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Time of origin

2019

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Antisocial Online Behavior Detection Using Deep Learning

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