A Novel Temporal Footprints-Based Framework for Fake News Detection
With the evolution of social media platforms, the detection of fake news and misinformation is gaining popularity. Social media platforms are the fastest source of fake news propagation, whereas online news websites contribute to dissemination. In recent studies, the temporal features in text docume...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10741540/ |
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| author | Ali Raza Shafiq Ur Rehman Khan Raja Sher Afgun Usmani Ashok Kumar Das Shehzad Ashraf Chaudhry |
| author_facet | Ali Raza Shafiq Ur Rehman Khan Raja Sher Afgun Usmani Ashok Kumar Das Shehzad Ashraf Chaudhry |
| author_sort | Ali Raza |
| collection | DOAJ |
| description | With the evolution of social media platforms, the detection of fake news and misinformation is gaining popularity. Social media platforms are the fastest source of fake news propagation, whereas online news websites contribute to dissemination. In recent studies, the temporal features in text documents have gained valuable consideration from the natural language processing (NLP) research community. This study investigates the importance of temporal features in text documents for detecting fake news. Later, the temporal features are combined with the textual features to increase classifier performance. This research study uses Random Forest (RF) and Bi-LSTM techniques to classify fake news based on temporal features and textual features. A publicly available dataset was used to train and test the model. The experimental results demonstrated that the proposed method achieved 99% accuracy by combining temporal and textual features in fake news detection. |
| format | Article |
| id | doaj-art-f7b9eb2c2bc243a1ba80376567d19fa6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f7b9eb2c2bc243a1ba80376567d19fa62025-08-20T02:49:22ZengIEEEIEEE Access2169-35362024-01-011217241917242810.1109/ACCESS.2024.349055810741540A Novel Temporal Footprints-Based Framework for Fake News DetectionAli Raza0https://orcid.org/0000-0002-0591-1511Shafiq Ur Rehman Khan1https://orcid.org/0000-0002-1475-0190Raja Sher Afgun Usmani2Ashok Kumar Das3https://orcid.org/0000-0002-5196-9589Shehzad Ashraf Chaudhry4https://orcid.org/0000-0002-9321-6956Department of Software Engineering, University of Mianwali, Mianwali, PakistanDepartment of Computer Science, Namal University, Mianwali, PakistanDepartment of Computer Science, MY University, Islamabad, PakistanCenter for Security, Theory and Algorithmic Research, International Institute of Information Technology at Hyderabad, Hyderabad, IndiaDepartment of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab EmiratesWith the evolution of social media platforms, the detection of fake news and misinformation is gaining popularity. Social media platforms are the fastest source of fake news propagation, whereas online news websites contribute to dissemination. In recent studies, the temporal features in text documents have gained valuable consideration from the natural language processing (NLP) research community. This study investigates the importance of temporal features in text documents for detecting fake news. Later, the temporal features are combined with the textual features to increase classifier performance. This research study uses Random Forest (RF) and Bi-LSTM techniques to classify fake news based on temporal features and textual features. A publicly available dataset was used to train and test the model. The experimental results demonstrated that the proposed method achieved 99% accuracy by combining temporal and textual features in fake news detection.https://ieeexplore.ieee.org/document/10741540/Fake newsmachine learningtemporal featurestextual features |
| spellingShingle | Ali Raza Shafiq Ur Rehman Khan Raja Sher Afgun Usmani Ashok Kumar Das Shehzad Ashraf Chaudhry A Novel Temporal Footprints-Based Framework for Fake News Detection IEEE Access Fake news machine learning temporal features textual features |
| title | A Novel Temporal Footprints-Based Framework for Fake News Detection |
| title_full | A Novel Temporal Footprints-Based Framework for Fake News Detection |
| title_fullStr | A Novel Temporal Footprints-Based Framework for Fake News Detection |
| title_full_unstemmed | A Novel Temporal Footprints-Based Framework for Fake News Detection |
| title_short | A Novel Temporal Footprints-Based Framework for Fake News Detection |
| title_sort | novel temporal footprints based framework for fake news detection |
| topic | Fake news machine learning temporal features textual features |
| url | https://ieeexplore.ieee.org/document/10741540/ |
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