Improvement performance by using Machine learning algorithms for fake news detection
The prevalence of internet use and the volume of actual-time data created and shared on social media sites and applications have raised the risk of spreading harmful or misunderstanding content, engaging in unlawful activity, abusing others, and disseminating false information. As of today, some...
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| Format: | Article |
| Language: | English |
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Salahaddin University-Erbil
2023-04-01
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| Series: | Zanco Journal of Pure and Applied Sciences |
| Subjects: | |
| Online Access: | https://zancojournal.su.edu.krd/index.php/JPAS/article/view/425 |
| _version_ | 1797367782871924736 |
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| author | Eman Shekhan Hamsheen , Laith R.Flah |
| author_facet | Eman Shekhan Hamsheen , Laith R.Flah |
| author_sort | Eman Shekhan Hamsheen , Laith R.Flah |
| collection | DOAJ |
| description | The prevalence of internet use and the volume of actual-time data created and shared on social media sites and applications have raised the risk of spreading harmful or misunderstanding content, engaging in unlawful activity, abusing others, and disseminating false information. As of today, some studies have been done on fake news recognition in the Kurdish language. For extremely resourced languages like Arabic, English, and other international languages, false news detection is a well-researched research subject. Less resourced languages, however, stay out of attention because there is no labeled fake corpus, no fact-checking website, and no access to NPL tools. This paper illustrates the process of identifying fake news, using two components of the dataset for fake news and actual news. Several classifiers were then applied to the quantity after using identifiers as a highlight of selection. Results of the proposed study demonstrated that Passive-Aggressive Classifier (PAC) outperformed the other classifiers on both datasets the dataset with an accuracy score of 93.0 percent and other classifiers were less in some percentage that show high accuracy as well since it is 90 percent. |
| first_indexed | 2024-03-08T17:22:07Z |
| format | Article |
| id | doaj.art-c8179ef60f5e45e28adca0c5547aa626 |
| institution | Directory Open Access Journal |
| issn | 2218-0230 2412-3986 |
| language | English |
| last_indexed | 2024-03-08T17:22:07Z |
| publishDate | 2023-04-01 |
| publisher | Salahaddin University-Erbil |
| record_format | Article |
| series | Zanco Journal of Pure and Applied Sciences |
| spelling | doaj.art-c8179ef60f5e45e28adca0c5547aa6262024-01-03T04:28:42ZengSalahaddin University-ErbilZanco Journal of Pure and Applied Sciences2218-02302412-39862023-04-0135210.21271/ZJPAS.35.2.6Improvement performance by using Machine learning algorithms for fake news detectionEman Shekhan Hamsheen , Laith R.Flah The prevalence of internet use and the volume of actual-time data created and shared on social media sites and applications have raised the risk of spreading harmful or misunderstanding content, engaging in unlawful activity, abusing others, and disseminating false information. As of today, some studies have been done on fake news recognition in the Kurdish language. For extremely resourced languages like Arabic, English, and other international languages, false news detection is a well-researched research subject. Less resourced languages, however, stay out of attention because there is no labeled fake corpus, no fact-checking website, and no access to NPL tools. This paper illustrates the process of identifying fake news, using two components of the dataset for fake news and actual news. Several classifiers were then applied to the quantity after using identifiers as a highlight of selection. Results of the proposed study demonstrated that Passive-Aggressive Classifier (PAC) outperformed the other classifiers on both datasets the dataset with an accuracy score of 93.0 percent and other classifiers were less in some percentage that show high accuracy as well since it is 90 percent.https://zancojournal.su.edu.krd/index.php/JPAS/article/view/425fake news detectionkurdish languagemachine learningclassifierspassive-aggressive. |
| spellingShingle | Eman Shekhan Hamsheen , Laith R.Flah Improvement performance by using Machine learning algorithms for fake news detection Zanco Journal of Pure and Applied Sciences fake news detection kurdish language machine learning classifiers passive-aggressive. |
| title | Improvement performance by using Machine learning algorithms for fake news detection |
| title_full | Improvement performance by using Machine learning algorithms for fake news detection |
| title_fullStr | Improvement performance by using Machine learning algorithms for fake news detection |
| title_full_unstemmed | Improvement performance by using Machine learning algorithms for fake news detection |
| title_short | Improvement performance by using Machine learning algorithms for fake news detection |
| title_sort | improvement performance by using machine learning algorithms for fake news detection |
| topic | fake news detection kurdish language machine learning classifiers passive-aggressive. |
| url | https://zancojournal.su.edu.krd/index.php/JPAS/article/view/425 |
| work_keys_str_mv | AT emanshekhanhamsheenlaithrflah improvementperformancebyusingmachinelearningalgorithmsforfakenewsdetection |