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http://dx.doi.org/10.6109/jkiice.2020.24.12.1595

Bias & Hate Speech Detection Using Deep Learning: Multi-channel CNN Modeling with Attention  

Lee, Wonseok (Department of MIS, Keimyung University)
Lee, Hyunsang (School of Business, Kyungpook National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.12, 2020 , pp. 1595-1603 More about this Journal
Abstract
Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.
Keywords
Bias and hate expressions; CNN; Multi-channel; Attention; Internet news comments;
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Times Cited By KSCI : 4  (Citation Analysis)
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