A review on abusive content automatic detection: approaches, challenges and opportunities
The increasing use of social media has led to the emergence of a new challenge in the form of abusive content. There are many forms of abusive content such as hate speech, cyberbullying, offensive language, and abusive language. This article will present a review of abusive content automatic detecti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2022-11-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1142.pdf |
| _version_ | 1798015440697425920 |
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| author | Bedour Alrashidi Amani Jamal Imtiaz Khan Ali Alkhathlan |
| author_facet | Bedour Alrashidi Amani Jamal Imtiaz Khan Ali Alkhathlan |
| author_sort | Bedour Alrashidi |
| collection | DOAJ |
| description | The increasing use of social media has led to the emergence of a new challenge in the form of abusive content. There are many forms of abusive content such as hate speech, cyberbullying, offensive language, and abusive language. This article will present a review of abusive content automatic detection approaches. Specifically, we are focusing on the recent contributions that were using natural language processing (NLP) technologies to detect the abusive content in social media. Accordingly, we adopt PRISMA flow chart for selecting the related papers and filtering process with some of inclusion and exclusion criteria. Therefore, we select 25 papers for meta-analysis and another 87 papers were cited in this article during the span of 2017–2021. In addition, we searched for the available datasets that are related to abusive content categories in three repositories and we highlighted some points related to the obtained results. Moreover, after a comprehensive review this article propose a new taxonomy of abusive content automatic detection by covering five different aspects and tasks. The proposed taxonomy gives insights and a holistic view of the automatic detection process. Finally, this article discusses and highlights the challenges and opportunities for the abusive content automatic detection problem. |
| first_indexed | 2024-04-11T15:34:32Z |
| format | Article |
| id | doaj.art-5a355427bfc0470696951519d85576be |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-11T15:34:32Z |
| publishDate | 2022-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-5a355427bfc0470696951519d85576be2022-12-22T04:16:03ZengPeerJ Inc.PeerJ Computer Science2376-59922022-11-018e114210.7717/peerj-cs.1142A review on abusive content automatic detection: approaches, challenges and opportunitiesBedour Alrashidi0Amani Jamal1Imtiaz Khan2Ali Alkhathlan3Department of Computer Science, King Abdul Aziz University, Jeddah, Saudi ArabiaDepartment of Computer Science, King Abdul Aziz University, Jeddah, Saudi ArabiaDepartment of Computer Science, Cardiff Metropolitan University, Cardiff, UKDepartment of Computer Science, King Abdul Aziz University, Jeddah, Saudi ArabiaThe increasing use of social media has led to the emergence of a new challenge in the form of abusive content. There are many forms of abusive content such as hate speech, cyberbullying, offensive language, and abusive language. This article will present a review of abusive content automatic detection approaches. Specifically, we are focusing on the recent contributions that were using natural language processing (NLP) technologies to detect the abusive content in social media. Accordingly, we adopt PRISMA flow chart for selecting the related papers and filtering process with some of inclusion and exclusion criteria. Therefore, we select 25 papers for meta-analysis and another 87 papers were cited in this article during the span of 2017–2021. In addition, we searched for the available datasets that are related to abusive content categories in three repositories and we highlighted some points related to the obtained results. Moreover, after a comprehensive review this article propose a new taxonomy of abusive content automatic detection by covering five different aspects and tasks. The proposed taxonomy gives insights and a holistic view of the automatic detection process. Finally, this article discusses and highlights the challenges and opportunities for the abusive content automatic detection problem.https://peerj.com/articles/cs-1142.pdfAbusive contentOffensive languageHate speechMachine learningNLP |
| spellingShingle | Bedour Alrashidi Amani Jamal Imtiaz Khan Ali Alkhathlan A review on abusive content automatic detection: approaches, challenges and opportunities PeerJ Computer Science Abusive content Offensive language Hate speech Machine learning NLP |
| title | A review on abusive content automatic detection: approaches, challenges and opportunities |
| title_full | A review on abusive content automatic detection: approaches, challenges and opportunities |
| title_fullStr | A review on abusive content automatic detection: approaches, challenges and opportunities |
| title_full_unstemmed | A review on abusive content automatic detection: approaches, challenges and opportunities |
| title_short | A review on abusive content automatic detection: approaches, challenges and opportunities |
| title_sort | review on abusive content automatic detection approaches challenges and opportunities |
| topic | Abusive content Offensive language Hate speech Machine learning NLP |
| url | https://peerj.com/articles/cs-1142.pdf |
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