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Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches
With the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech has become a major concern for society, individuals, policymakers, and researchers. Combating hate speech and fake news are the mo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117834/ https://www.ncbi.nlm.nih.gov/pubmed/35607418 http://dx.doi.org/10.1186/s40537-022-00619-x |
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author | Demilie, Wubetu Barud Salau, Ayodeji Olalekan |
author_facet | Demilie, Wubetu Barud Salau, Ayodeji Olalekan |
author_sort | Demilie, Wubetu Barud |
collection | PubMed |
description | With the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech has become a major concern for society, individuals, policymakers, and researchers. Combating hate speech and fake news are the most pressing societal issues. It is difficult to expose false claims before they cause significant harm. Automatic fact or claim verification has recently piqued the interest of various research communities. Despite efforts to use automatic approaches for detection and monitoring, their results are still unsatisfactory, and that requires more research work in the area. Fake news and hate speech messages are any messages on social media platforms that spread negativity in society about sex, caste, religion, politics, race, disability, sexual orientation, and so on. Thus, the type of massage is extremely difficult to detect and combat. This work aims to analyze the optimal approaches for this kind of problem, as well as the relationship between the approaches, dataset type, size, and accuracy. Finally, based on the analysis results of the implemented approaches, deep learning (DL) approaches have been recommended for other Ethiopian languages to increase the performance of all evaluation metrics from different social media platforms. Additionally, as the review results indicate, the combination of DL and machine learning (ML) approaches with a balanced dataset can improve the detection and combating performance of the system. |
format | Online Article Text |
id | pubmed-9117834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-91178342022-05-19 Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches Demilie, Wubetu Barud Salau, Ayodeji Olalekan J Big Data Survey With the proliferation of social media platforms that provide anonymity, easy access, online community development, and online debate, detecting and tracking hate speech has become a major concern for society, individuals, policymakers, and researchers. Combating hate speech and fake news are the most pressing societal issues. It is difficult to expose false claims before they cause significant harm. Automatic fact or claim verification has recently piqued the interest of various research communities. Despite efforts to use automatic approaches for detection and monitoring, their results are still unsatisfactory, and that requires more research work in the area. Fake news and hate speech messages are any messages on social media platforms that spread negativity in society about sex, caste, religion, politics, race, disability, sexual orientation, and so on. Thus, the type of massage is extremely difficult to detect and combat. This work aims to analyze the optimal approaches for this kind of problem, as well as the relationship between the approaches, dataset type, size, and accuracy. Finally, based on the analysis results of the implemented approaches, deep learning (DL) approaches have been recommended for other Ethiopian languages to increase the performance of all evaluation metrics from different social media platforms. Additionally, as the review results indicate, the combination of DL and machine learning (ML) approaches with a balanced dataset can improve the detection and combating performance of the system. Springer International Publishing 2022-05-19 2022 /pmc/articles/PMC9117834/ /pubmed/35607418 http://dx.doi.org/10.1186/s40537-022-00619-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Survey Demilie, Wubetu Barud Salau, Ayodeji Olalekan Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches |
title | Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches |
title_full | Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches |
title_fullStr | Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches |
title_full_unstemmed | Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches |
title_short | Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches |
title_sort | detection of fake news and hate speech for ethiopian languages: a systematic review of the approaches |
topic | Survey |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117834/ https://www.ncbi.nlm.nih.gov/pubmed/35607418 http://dx.doi.org/10.1186/s40537-022-00619-x |
work_keys_str_mv | AT demiliewubetubarud detectionoffakenewsandhatespeechforethiopianlanguagesasystematicreviewoftheapproaches AT salauayodejiolalekan detectionoffakenewsandhatespeechforethiopianlanguagesasystematicreviewoftheapproaches |