NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification
The widespread dissemination of fake news in the digital era significantly impacts public opinion, economies, and political outcomes. Traditional fake news detection methods often struggle to balance accuracy and interpretability, limiting their effectiveness. To address this challenge, this paper i...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10908835/ |
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| author | Borra Vineetha Munirathinam Nirmala |
| author_facet | Borra Vineetha Munirathinam Nirmala |
| author_sort | Borra Vineetha |
| collection | DOAJ |
| description | The widespread dissemination of fake news in the digital era significantly impacts public opinion, economies, and political outcomes. Traditional fake news detection methods often struggle to balance accuracy and interpretability, limiting their effectiveness. To address this challenge, this paper introduces NeuroFuzDetect, an Intelligent Fuzzy-Neural Network that integrates Fuzzy Logic and Long Short-Term Memory (LSTM) to enhance both accuracy and transparency in fake news detection. NeuroFuzDetect leverages advanced computational reasoning to analyze uncertainty and ambiguity in textual data by evaluating critical linguistic features, including credibility, emotional tone, and language patterns, ensuring interpretability by providing justifications for its predictions. The model was evaluated on a widely recognized dataset, demonstrating an accuracy of 87.2%, along with significant improvements in precision, recall, and F1-score. Future research will focus on refining the model by incorporating multimodal analysis, integrating visual and contextual cues, and adapting it for real-time detection on digital platforms while enhancing robustness against adversarial attacks and extending applicability across diverse linguistic and cultural contexts. By achieving a balance between efficiency and transparency, NeuroFuzDetect presents a scalable and reliable solution for combating fake news in real-world applications. |
| format | Article |
| id | doaj-art-7c651d0c38b44bc4b0eaf8463b1a8db5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7c651d0c38b44bc4b0eaf8463b1a8db52025-08-20T03:08:40ZengIEEEIEEE Access2169-35362025-01-0113628206283010.1109/ACCESS.2025.354712310908835NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News ClassificationBorra Vineetha0Munirathinam Nirmala1https://orcid.org/0000-0002-7581-0150School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaThe widespread dissemination of fake news in the digital era significantly impacts public opinion, economies, and political outcomes. Traditional fake news detection methods often struggle to balance accuracy and interpretability, limiting their effectiveness. To address this challenge, this paper introduces NeuroFuzDetect, an Intelligent Fuzzy-Neural Network that integrates Fuzzy Logic and Long Short-Term Memory (LSTM) to enhance both accuracy and transparency in fake news detection. NeuroFuzDetect leverages advanced computational reasoning to analyze uncertainty and ambiguity in textual data by evaluating critical linguistic features, including credibility, emotional tone, and language patterns, ensuring interpretability by providing justifications for its predictions. The model was evaluated on a widely recognized dataset, demonstrating an accuracy of 87.2%, along with significant improvements in precision, recall, and F1-score. Future research will focus on refining the model by incorporating multimodal analysis, integrating visual and contextual cues, and adapting it for real-time detection on digital platforms while enhancing robustness against adversarial attacks and extending applicability across diverse linguistic and cultural contexts. By achieving a balance between efficiency and transparency, NeuroFuzDetect presents a scalable and reliable solution for combating fake news in real-world applications.https://ieeexplore.ieee.org/document/10908835/Fake newsfuzzy inference designlong short-term memoryneural networksclassificationdetection |
| spellingShingle | Borra Vineetha Munirathinam Nirmala NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification IEEE Access Fake news fuzzy inference design long short-term memory neural networks classification detection |
| title | NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification |
| title_full | NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification |
| title_fullStr | NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification |
| title_full_unstemmed | NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification |
| title_short | NeuroFuzDetect–A Neural and Fuzzy Inference System for Fake News Classification |
| title_sort | neurofuzdetect x2013 a neural and fuzzy inference system for fake news classification |
| topic | Fake news fuzzy inference design long short-term memory neural networks classification detection |
| url | https://ieeexplore.ieee.org/document/10908835/ |
| work_keys_str_mv | AT borravineetha neurofuzdetectx2013aneuralandfuzzyinferencesystemforfakenewsclassification AT munirathinamnirmala neurofuzdetectx2013aneuralandfuzzyinferencesystemforfakenewsclassification |