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Cyberbulling in the Online Data Storage Systems and Detecting Content through a Deep RNN Architecture

Baidya Nath Saha, Apurbalal Senapati
Concordia University of Edmonton, Edmonton AB T5B 4E4, Canada & Central Institute of Technology, Kokrajhar BTAD, Assam 783370, India

Abstract: Cyberbulling is increasingly becoming a threat in the online social data storing systems. This act affects the objective discussions in social media and adversely affects the outcome of the online social democratic processes. To detect the hate speech and offensive content, a particular model of Recurrent Neural Network (RNN) based deep learning approach called Long Short Term Memory (LSTM) is implemented. This task becomes a major challenge due to the abusive language which is quite subjective in nature and highly context dependent. The current work provides a language- agnostic solution in three Indo-European languages (English, German, and Hindi) since no pre-trained word embedding is used. The performance evaluation outcome provides interesting results and inferences.

Keywords: Hate Speech Detection, Offensive Content Identification, Long Short Term Memory (LSTM), Recurrent Neural Network (RNN) Cyberbulling in the Online Data Storage Systems and Detecting Content through a Deep RNN Architecture

DOI:https://doi.org/10.6025/jdp/2020/10/3/77-82

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