Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions
Abstract Social media usage has reached its peak across all age groups, resulting in vast quantities of data being generated daily, some internet users disseminate fake information for their own benefit. Manually checking each user profile is time-consuming, necessitating automated solutions. This p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07548-3 |
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| author | Sapana Yakkundi Rudragoud Patil Sangeeta Sangani R. H. Goudar Swetha Indudhar Goudar Aijazahamed Qazi |
| author_facet | Sapana Yakkundi Rudragoud Patil Sangeeta Sangani R. H. Goudar Swetha Indudhar Goudar Aijazahamed Qazi |
| author_sort | Sapana Yakkundi |
| collection | DOAJ |
| description | Abstract Social media usage has reached its peak across all age groups, resulting in vast quantities of data being generated daily, some internet users disseminate fake information for their own benefit. Manually checking each user profile is time-consuming, necessitating automated solutions. This paper addresses multimodal fake news detection, examining evolving landscape of fake information on social media. It highlights the limitations of techniques that rely on single modality. This review underscores the necessity of automated systems capable of detecting and monitoring fake information in multimodal content on social media platforms. The variety of multimodal datasets, tools, and machine learning techniques used to determine the authenticity and virality of online social media content. The research outlines standard performance indicators to evaluate content, aiming to enhance the accuracy of this ongoing global issue. Furthermore, this study highlights the main challenges that are identified and suggests directions for future research to mitigate its harmful social impact. This paper emphasizes the importance of automating the verification of online information to ensure authenticity, thereby contributing to a more secure digital atmosphere. |
| format | Article |
| id | doaj-art-99a8c41fe6be4474b3e9a2d7262f3432 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-99a8c41fe6be4474b3e9a2d7262f34322025-08-20T04:03:00ZengSpringerDiscover Applied Sciences3004-92612025-08-017811910.1007/s42452-025-07548-3Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directionsSapana Yakkundi0Rudragoud Patil1Sangeeta Sangani2R. H. Goudar3Swetha Indudhar Goudar4Aijazahamed Qazi5Department of Computer Science and Engineering, KLS Gogte Institute of TechnologyDepartment of Computer Science and Engineering, KLS Gogte Institute of TechnologyManipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationDepartment of Computer Science and Engineering, Visvesvaraya Technological UniversityDepartment. of Master of Computer Applications, KLS Gogte Institute of TechnologyDepartment of Artificial Intelligence and Data Science, Angadi Institute of Technology and ManagementAbstract Social media usage has reached its peak across all age groups, resulting in vast quantities of data being generated daily, some internet users disseminate fake information for their own benefit. Manually checking each user profile is time-consuming, necessitating automated solutions. This paper addresses multimodal fake news detection, examining evolving landscape of fake information on social media. It highlights the limitations of techniques that rely on single modality. This review underscores the necessity of automated systems capable of detecting and monitoring fake information in multimodal content on social media platforms. The variety of multimodal datasets, tools, and machine learning techniques used to determine the authenticity and virality of online social media content. The research outlines standard performance indicators to evaluate content, aiming to enhance the accuracy of this ongoing global issue. Furthermore, this study highlights the main challenges that are identified and suggests directions for future research to mitigate its harmful social impact. This paper emphasizes the importance of automating the verification of online information to ensure authenticity, thereby contributing to a more secure digital atmosphere.https://doi.org/10.1007/s42452-025-07548-3Fake newsMachine learningMultimodal dataReal-time processingSocial media |
| spellingShingle | Sapana Yakkundi Rudragoud Patil Sangeeta Sangani R. H. Goudar Swetha Indudhar Goudar Aijazahamed Qazi Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions Discover Applied Sciences Fake news Machine learning Multimodal data Real-time processing Social media |
| title | Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions |
| title_full | Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions |
| title_fullStr | Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions |
| title_full_unstemmed | Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions |
| title_short | Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions |
| title_sort | exploring machine learning for fake news detection techniques tools challenges and future research directions |
| topic | Fake news Machine learning Multimodal data Real-time processing Social media |
| url | https://doi.org/10.1007/s42452-025-07548-3 |
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