Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability
Abstract In the era of widespread misinformation, detecting fake news has become a crucial challenge, particularly on social media platforms. This paper introduces an optimized approach for Fake News Detection, combining BERT and GloVe embeddings with Principal Component Analysis (PCA) and attention...
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-024-00730-2 |
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| author | Mahmoud AlJamal Rabee Alquran Ayoub Alsarhan Mohammad Aljaidi Wafa’ Q. Al-Jamal Ali Fayez Alkoradees |
| author_facet | Mahmoud AlJamal Rabee Alquran Ayoub Alsarhan Mohammad Aljaidi Wafa’ Q. Al-Jamal Ali Fayez Alkoradees |
| author_sort | Mahmoud AlJamal |
| collection | DOAJ |
| description | Abstract In the era of widespread misinformation, detecting fake news has become a crucial challenge, particularly on social media platforms. This paper introduces an optimized approach for Fake News Detection, combining BERT and GloVe embeddings with Principal Component Analysis (PCA) and attention mechanisms, enriched by social and temporal features for more effective text representation. Leveraging the CIC Truth Seeker Dataset 2023, we applied SHAP for feature selection and interpretability, ensuring transparency in the model’s predictions. Our methodology achieved a remarkable accuracy of 99.9% using a Random Forest classifier, showcasing the efficacy of this optimized hybrid approach. The integration of interpretability techniques such as LIME and SHAP provides deeper insights into the model’s decisions, making it a reliable tool for combating misinformation. This novel approach offers a robust and transparent solution to the growing threat of fake news, contributing significantly to the integrity of online information and public discourse on platforms like Twitter X. |
| format | Article |
| id | doaj-art-ca3a08a787284c4b85ada22cdef8de65 |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-ca3a08a787284c4b85ada22cdef8de652025-02-09T12:53:49ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-02-0118113610.1007/s44196-024-00730-2Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced InterpretabilityMahmoud AlJamal0Rabee Alquran1Ayoub Alsarhan2Mohammad Aljaidi3Wafa’ Q. Al-Jamal4Ali Fayez Alkoradees5Department of Cybersecurity, Science and Information Technology, Irbid National UniversityDepartment of Information Technology, Faculty of Prince Al-Hussien bin Abdullah for IT, The Hashemite UniversityDepartment of Information Technology, Faculty of Prince Al-Hussien bin Abdullah for IT, The Hashemite UniversityDepartment of Computer Science, Faculty of Information Technology, Zarqa UniversityFaculty of Science and Technology (FST), Universiti Sains Islam Malaysia (USIM)Unit of Scientific Research, Applied College, Qassim UniversityAbstract In the era of widespread misinformation, detecting fake news has become a crucial challenge, particularly on social media platforms. This paper introduces an optimized approach for Fake News Detection, combining BERT and GloVe embeddings with Principal Component Analysis (PCA) and attention mechanisms, enriched by social and temporal features for more effective text representation. Leveraging the CIC Truth Seeker Dataset 2023, we applied SHAP for feature selection and interpretability, ensuring transparency in the model’s predictions. Our methodology achieved a remarkable accuracy of 99.9% using a Random Forest classifier, showcasing the efficacy of this optimized hybrid approach. The integration of interpretability techniques such as LIME and SHAP provides deeper insights into the model’s decisions, making it a reliable tool for combating misinformation. This novel approach offers a robust and transparent solution to the growing threat of fake news, contributing significantly to the integrity of online information and public discourse on platforms like Twitter X.https://doi.org/10.1007/s44196-024-00730-2Text EmbeddingFake News DetectionTwitter XBERT and GloVe EmbeddingsAttention MechanismsSHAP and LIME |
| spellingShingle | Mahmoud AlJamal Rabee Alquran Ayoub Alsarhan Mohammad Aljaidi Wafa’ Q. Al-Jamal Ali Fayez Alkoradees Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability International Journal of Computational Intelligence Systems Text Embedding Fake News Detection Twitter X BERT and GloVe Embeddings Attention Mechanisms SHAP and LIME |
| title | Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability |
| title_full | Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability |
| title_fullStr | Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability |
| title_full_unstemmed | Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability |
| title_short | Optimized Novel Text Embedding Approach for Fake News Detection on Twitter X: Integrating Social Context, Temporal Dynamics, and Enhanced Interpretability |
| title_sort | optimized novel text embedding approach for fake news detection on twitter x integrating social context temporal dynamics and enhanced interpretability |
| topic | Text Embedding Fake News Detection Twitter X BERT and GloVe Embeddings Attention Mechanisms SHAP and LIME |
| url | https://doi.org/10.1007/s44196-024-00730-2 |
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