Misinformation Detection: A Review for High and Low-Resource Languages
The rapid spread of misinformation on platforms like Twitter, and Facebook, and in news headlines highlights the urgent need for effective ways to detect it. Currently, researchers are increasingly using machine learning (ML) and deep learning (DL) techniques to tackle misinformation detection (MID)...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Informatics Department, Faculty of Computer Science Bina Darma University
2024-12-01
|
| Series: | Journal of Information Systems and Informatics |
| Subjects: | |
| Online Access: | https://journal-isi.org/index.php/isi/article/view/931 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849709205201616896 |
|---|---|
| author | Seani Rananga Bassey Isong Abiodun Modupe Vukosi Marivate |
| author_facet | Seani Rananga Bassey Isong Abiodun Modupe Vukosi Marivate |
| author_sort | Seani Rananga |
| collection | DOAJ |
| description | The rapid spread of misinformation on platforms like Twitter, and Facebook, and in news headlines highlights the urgent need for effective ways to detect it. Currently, researchers are increasingly using machine learning (ML) and deep learning (DL) techniques to tackle misinformation detection (MID) because of their proven success. However, this task is still challenging due to the complexity of deceptive language, digital editing tools, and the lack of reliable linguistic resources for non-English languages. This paper provides a comprehensive analysis of relevant research, providing insights into advanced techniques for MID. It covers dataset assessments, the importance of using multiple forms of data (multimodality), and different language representations. By applying the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) methodology, the study identified and analyzed literature from 2019 to 2024 across five databases: Google Scholar, Springer, Elsevier, ACM, and IEEE Xplore. The study selected thirty-one papers and examined the effectiveness of various ML and DL approaches with a focal point on performance metrics, datasets, and false or misleading information detection challenges. The findings indicate that most current MID models are heavily dependent on DL techniques, with approximately 81% of studies preferring these over traditional ML methods. In addition, most studies are text-based, with much less attention given to audio, speech, images, and videos. The most effective models are mainly designed for high-resource languages, with English datasets being the most used (67%), followed by Arabic (14%), Chinese (11%), and others. Less than 10% of the studies focus on low-resource languages (LRLs). Therefore, the study highlighted the need for robust datasets and interpretable, scalable MID models for LRLs. It emphasizes the critical need to prioritize and advance MID research for LRLs across all data types, including text, audio, speech, images, videos, and multimodal approaches. This study aims to support ongoing efforts to combat misinformation and promote a more informed understanding of under-resourced African languages. |
| format | Article |
| id | doaj-art-61599dca28754e61b1f2def81b257fdd |
| institution | DOAJ |
| issn | 2656-5935 2656-4882 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Informatics Department, Faculty of Computer Science Bina Darma University |
| record_format | Article |
| series | Journal of Information Systems and Informatics |
| spelling | doaj-art-61599dca28754e61b1f2def81b257fdd2025-08-20T03:15:24ZengInformatics Department, Faculty of Computer Science Bina Darma UniversityJournal of Information Systems and Informatics2656-59352656-48822024-12-01642892292210.51519/journalisi.v6i4.931931Misinformation Detection: A Review for High and Low-Resource LanguagesSeani Rananga0Bassey Isong1Abiodun Modupe2Vukosi Marivate3North-West University and University of PretoriaNorth-West UniversityUniversity of PretoriaUniversity of PretoriaThe rapid spread of misinformation on platforms like Twitter, and Facebook, and in news headlines highlights the urgent need for effective ways to detect it. Currently, researchers are increasingly using machine learning (ML) and deep learning (DL) techniques to tackle misinformation detection (MID) because of their proven success. However, this task is still challenging due to the complexity of deceptive language, digital editing tools, and the lack of reliable linguistic resources for non-English languages. This paper provides a comprehensive analysis of relevant research, providing insights into advanced techniques for MID. It covers dataset assessments, the importance of using multiple forms of data (multimodality), and different language representations. By applying the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) methodology, the study identified and analyzed literature from 2019 to 2024 across five databases: Google Scholar, Springer, Elsevier, ACM, and IEEE Xplore. The study selected thirty-one papers and examined the effectiveness of various ML and DL approaches with a focal point on performance metrics, datasets, and false or misleading information detection challenges. The findings indicate that most current MID models are heavily dependent on DL techniques, with approximately 81% of studies preferring these over traditional ML methods. In addition, most studies are text-based, with much less attention given to audio, speech, images, and videos. The most effective models are mainly designed for high-resource languages, with English datasets being the most used (67%), followed by Arabic (14%), Chinese (11%), and others. Less than 10% of the studies focus on low-resource languages (LRLs). Therefore, the study highlighted the need for robust datasets and interpretable, scalable MID models for LRLs. It emphasizes the critical need to prioritize and advance MID research for LRLs across all data types, including text, audio, speech, images, videos, and multimodal approaches. This study aims to support ongoing efforts to combat misinformation and promote a more informed understanding of under-resourced African languages.https://journal-isi.org/index.php/isi/article/view/931misinformation detection, low-resource languages, high-resource languages, african languages. |
| spellingShingle | Seani Rananga Bassey Isong Abiodun Modupe Vukosi Marivate Misinformation Detection: A Review for High and Low-Resource Languages Journal of Information Systems and Informatics misinformation detection, low-resource languages, high-resource languages, african languages. |
| title | Misinformation Detection: A Review for High and Low-Resource Languages |
| title_full | Misinformation Detection: A Review for High and Low-Resource Languages |
| title_fullStr | Misinformation Detection: A Review for High and Low-Resource Languages |
| title_full_unstemmed | Misinformation Detection: A Review for High and Low-Resource Languages |
| title_short | Misinformation Detection: A Review for High and Low-Resource Languages |
| title_sort | misinformation detection a review for high and low resource languages |
| topic | misinformation detection, low-resource languages, high-resource languages, african languages. |
| url | https://journal-isi.org/index.php/isi/article/view/931 |
| work_keys_str_mv | AT seanirananga misinformationdetectionareviewforhighandlowresourcelanguages AT basseyisong misinformationdetectionareviewforhighandlowresourcelanguages AT abiodunmodupe misinformationdetectionareviewforhighandlowresourcelanguages AT vukosimarivate misinformationdetectionareviewforhighandlowresourcelanguages |