Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas
The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic...
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MDPI AG
2024-11-01
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| author | Sheetal Harris Hassan Jalil Hadi Naveed Ahmad Mohammed Ali Alshara |
| author_facet | Sheetal Harris Hassan Jalil Hadi Naveed Ahmad Mohammed Ali Alshara |
| author_sort | Sheetal Harris |
| collection | DOAJ |
| description | The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation. |
| format | Article |
| id | doaj-art-115749de5b5f4d3497d8eaccf60b0eb0 |
| institution | OA Journals |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Technologies |
| spelling | doaj-art-115749de5b5f4d3497d8eaccf60b0eb02025-08-20T01:54:04ZengMDPI AGTechnologies2227-70802024-11-01121122210.3390/technologies12110222Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research AgendasSheetal Harris0Hassan Jalil Hadi1Naveed Ahmad2Mohammed Ali Alshara3School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, ChinaSchool of Cyber Science and Engineering, Wuhan University, Wuhan 430072, ChinaPrince Sultan University, Riyadh 12435, Saudi ArabiaPrince Sultan University, Riyadh 12435, Saudi ArabiaThe emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.https://www.mdpi.com/2227-7080/12/11/222fake news detectiondataset evaluationmachine learningdeep learningnatural language processingsocial networks |
| spellingShingle | Sheetal Harris Hassan Jalil Hadi Naveed Ahmad Mohammed Ali Alshara Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas Technologies fake news detection dataset evaluation machine learning deep learning natural language processing social networks |
| title | Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas |
| title_full | Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas |
| title_fullStr | Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas |
| title_full_unstemmed | Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas |
| title_short | Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas |
| title_sort | fake news detection revisited an extensive review of theoretical frameworks dataset assessments model constraints and forward looking research agendas |
| topic | fake news detection dataset evaluation machine learning deep learning natural language processing social networks |
| url | https://www.mdpi.com/2227-7080/12/11/222 |
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