Abusive Language Detection and Characterization of Twitter Behavior

Authors

  • Dincy Davis Dept. of Computer Science and Engineering, Government Engineering College, Palakkad, India
  • Reena Murali Dept. of Computer Science and Engineering, Government Engineering College, Palakkad, India
  • Remesh Babu KR Dept. of Information Technology, Government Engineering College, Palakkad, India

DOI:

https://doi.org/10.26438/ijcse/v8i7.155161

Keywords:

text classification, abusive language, BiRNN, deep learning, natural language processing

Abstract

Abusive language refers to an insult or vulgarity which harass or deceive the target. Social media is a famous platform for the people to express their opinions publicly and to interact with other people in the world. Some of them may misuse their freedom of speech to bully others through abusive language. This will leads to the need for detecting abusive speech. Otherwise, it may severely impact the user’s online experience. It may be a time-consuming task if the detection and removal of such offensive material are done manually. Also, human supervision is unable to deal with large quantities of data. Therefore automatic abusive speech detection has become essential to be addressed effectively. For detecting abusive speech, context accompanying abusive speech is very useful. In this work, abusive language detection in online content is performed using Bidirectional Recurrent Neural Network (BiRNN) method. Here the main objective is to focus on various forms of abusive behaviors on Twitter and to detect whether a speech is abusive or not. The results are compared for various abusive behaviors in social media, with Convolutional Neural Netwrok (CNN) and Recurrent Neural Network (RNN) methods and proved that the proposed BiRNN is a better deep learning model for automatic abusive speech detection.

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Published

2020-07-31
CITATION
DOI: 10.26438/ijcse/v8i7.155161
Published: 2020-07-31

How to Cite

[1]
D. Davis, R. Murali, and R. B. KR, “Abusive Language Detection and Characterization of Twitter Behavior”, Int. J. Comp. Sci. Eng., vol. 8, no. 7, pp. 155–161, Jul. 2020.

Issue

Vol. 8 No. 7 (2020): IJCSE July Edition

Section

Research Article

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