Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

Cem Baydogan ,Bilal Alataş

doi:10.17559/tv-20210708143535

Abstract

Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

Highlights

The COVID-19 outbreak that emerged in the city of Wuhan has had a great anxiety impact on social media platforms all over the world
The performance measure criteria yielded with ten different Shallow Learning (SL) and Deep Learning (DL) algorithms laboured for the COVID-19 dataset-2 are indicated in Tab. 3
The studies and the methods applied show that Hate Speech Detection (HSD) is of importance that needs to be studied in more depth

Summary

Introduction

The COVID-19 outbreak that emerged in the city of Wuhan (in China) has had a great anxiety impact on social media platforms all over the world. As our most important goal, more effective solutions were sought to prevent the spread of HS In accordance with this purpose, we could summarize the contribution of the applied models and our article to the literature and science to satisfy this existing need as follows: (1) In order to increase the solution performance in the HSD problem, by applying DL methods in addition to SL models, higher performance results were obtained for HSD related to COVID-19. (6) It is shown that by most of the essential measurement metrics, DL networks characteristically outperformed SL approaches After this part of the article, it is organized as follows: In the second section of the study, studies on the spread and detection of HS during the COVID-19 outbreak are. All aspects of HSD research related to COVID-19 were highlighted

Methods

Results

Conclusion