Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 029

Antisocial Online Behavior Detection Using Deep Learning

Elizaveta Zinovyeva
Wolfgang Karl Härdle
Stefan Lessmann

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.

Deep Learning, Cyberbullying, Antisocial Online Behavior, Attention Mechanism,
Text Classification

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