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
The use of various online social media platforms rising day by day caused an increase in the correct or incorrect information shared by users, especially during COVID-19. The introduction of COVID-19 on the world agenda gave rise to an overall bad reaction against East Asia (esp. China) in online so...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/390880 |
| _version_ | 1797206804629815296 |
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| author | Cem Baydogan Bilal Alatas* |
| author_facet | Cem Baydogan Bilal Alatas* |
| author_sort | Cem Baydogan |
| collection | DOAJ |
| description | The use of various online social media platforms rising day by day caused an increase in the correct or incorrect information shared by users, especially during COVID-19. The introduction of COVID-19 on the world agenda gave rise to an overall bad reaction against East Asia (esp. China) in online social media platforms. The social media users who spread degrading, racist, disrespectful, abusive, discriminatory, critical, abuse, harsh, offensive, etc. posts accused the Asian people of being responsible for the outbreak of COVID-19. For this reason, the development of the Hate Speech Detection (HSD) system was necessary in order to prevent the spread of these posts about COVID-19. In this article, a textual-based study on COVID-19-related hate speech (HS) sharing in online social networks was carried out with Shallow Learning (SL) and Deep Learning (DL) methods. In the first step of this study, typical Natural Language Processing (NLP) pipeline was applied for gathered two different datasets. This NLP pipeline was performed using bag of words, term frequency, document matrix, etc. techniques for features extraction representing datasets. Then, ten different SL and DL models were fine-tuned for HS datasets related to COVID-19. Accuracy, precision, sensitivity, and F-score performance measurement criteria were calculated to compare the performance of the SL and DL algorithms for the problem of HSD. The RNN, one of the models proposed for the first and second dataset in HSD, prevailed with the highest accuracy values of 78.7% and 90.3%, respectively. Due to the promising results of all approaches operated in the HSD, they are forecasted to be chosen in the solution of many other social media and network problems related to COVID-19. |
| first_indexed | 2024-04-24T09:12:50Z |
| format | Article |
| id | doaj.art-87ba2dbb4a4541d8a63be6418ad767ce |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:12:50Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-87ba2dbb4a4541d8a63be6418ad767ce2024-04-15T17:26:54ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-0129114915610.17559/TV-20210708143535Deep-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 ModelsCem Baydogan0Bilal Alatas*1Department of Software Engineering, Faculty of Technology, Firat University, Elazig, 23119, TurkeyDepartment of Software Engineering, Faculty of Technology, Firat University, Elazig, 23119, TurkeyThe use of various online social media platforms rising day by day caused an increase in the correct or incorrect information shared by users, especially during COVID-19. The introduction of COVID-19 on the world agenda gave rise to an overall bad reaction against East Asia (esp. China) in online social media platforms. The social media users who spread degrading, racist, disrespectful, abusive, discriminatory, critical, abuse, harsh, offensive, etc. posts accused the Asian people of being responsible for the outbreak of COVID-19. For this reason, the development of the Hate Speech Detection (HSD) system was necessary in order to prevent the spread of these posts about COVID-19. In this article, a textual-based study on COVID-19-related hate speech (HS) sharing in online social networks was carried out with Shallow Learning (SL) and Deep Learning (DL) methods. In the first step of this study, typical Natural Language Processing (NLP) pipeline was applied for gathered two different datasets. This NLP pipeline was performed using bag of words, term frequency, document matrix, etc. techniques for features extraction representing datasets. Then, ten different SL and DL models were fine-tuned for HS datasets related to COVID-19. Accuracy, precision, sensitivity, and F-score performance measurement criteria were calculated to compare the performance of the SL and DL algorithms for the problem of HSD. The RNN, one of the models proposed for the first and second dataset in HSD, prevailed with the highest accuracy values of 78.7% and 90.3%, respectively. Due to the promising results of all approaches operated in the HSD, they are forecasted to be chosen in the solution of many other social media and network problems related to COVID-19.https://hrcak.srce.hr/file/390880COVID-19hate speech detectionshallow and deep learning modelssocial media analysissocial network problems |
| spellingShingle | Cem Baydogan Bilal Alatas* 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 Tehnički Vjesnik COVID-19 hate speech detection shallow and deep learning models social media analysis social network problems |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | COVID-19 hate speech detection shallow and deep learning models social media analysis social network problems |
| url | https://hrcak.srce.hr/file/390880 |
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