A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction
Itishree Panda, Sitam Biswal, Surya Prakash Das | International Journal of Software Computing and Testing | Vol 12, Issue 01 | pp. 8-14 | ISSN: 2456-2351
Abstract
The broad sharing of information throughout human history was greatly accelerated by the emergence of the World Wide Web and the swift rise of social networking platforms such as Facebook and Twitter. Due to the extensive use of social media, users now generate and distribute vast amounts of content at an unprecedented rate, and a portion of this information is inaccurate or detached from factual reality. Automatically classifying a written article as misinformation or disinformation can be challenging. Even a subject-matter expert must take several aspects into account before determining the authenticity of an article. This study introduces an automated news classification framework based on machine learning techniques. The research examines multiple linguistic features that help differentiate authentic news from misleading or fabricated content. These features are used to train and evaluate several traditional machine learning algorithms as well as deep learning architectures. Among the tested models, the Deep Neural Network demonstrated the best performance, achieving an accuracy rate of 97%.
Keywords
Fake News, PCA, sentence embedding, K-means
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1. Fernández-Reyes FC, Shinde S. Evaluating deep neural networks for automatic fake news detection in political domain. In: Ibero-American Conference on Artificial Intelligence. Cham: Springer International Publishing; 2018 Nov 13. p. 206–216. 2. Perez-Rosas V, Kazemi A, Mihalcea R, Hou R, Byrne N, Loeb S. MP64-02 fake news about prostate cancer: Distinguishing language patterns in misinformative online videos. J Urol. 2020 Apr 1;203:e963. 3. Wang WY. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv [Preprint]. 2017 May 1:arXiv:1705.00648. 4. Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R. Automatic detection of fake news. In: Proceedings of the 27th International Conference on Computational Linguistics; 2018 Aug. p. 3391–3401. 5. Rubin VL, Conroy N, Chen Y, Cornwell S. Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection; 2016 Jun. p. 7–17. 6. Sharma U, Saran S, Patil SM. Fake news detection using machine learning algorithms. Int J Creat Res Thoughts (IJCRT). 2020 Jun 6;8(6):509–518. 7. Yi J, Nasukawa T, Bunescu R, Niblack W. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: Third IEEE International Conference on Data Mining; 2003 Nov 22. p. 427–434. 8. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor Newsl. 2017 Sep 1;19(1):22–36. 9. Arunthavachelvan K, Raza S, Ding C. A deep neural network approach for fake news detection using linguistic and psychological features. User Model User-Adapt Interact. 2024 Sep 1;34(4):1043–1070. 10. Saikh T, De A, Ekbal A, Bhattacharyya P. A deep learning approach for automatic detection of fake news. In: Proceedings of the 16th International Conference on Natural Language Processing; 2019 Dec. p. 230–238.
How to cite this article
APA
Panda, I., Biswal, S., & Das, S. P. (2026). A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction. International Journal of Software Computing and Testing, 12(01), 8-14.
MLA
Panda, Itishree, et al. “A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction.” International Journal of Software Computing and Testing, vol. 12, no. 01, 2026, pp. 8-14.
Chicago
Itishree Panda, Sitam Biswal, and Surya Prakash Das. “A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction.” International Journal of Software Computing and Testing 12, no. 01 (2026): 8-14.
Vancouver
Panda I, Biswal S, Das SP. A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction. International Journal of Software Computing and Testing. 2026;12(01):8-14.
BibTeX
@article{PandaI2026,
author = {Itishree Panda and Sitam Biswal and Surya Prakash Das},
title = {A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction},
journal = {International Journal of Software Computing and Testing},
year = {2026},
volume = {12},
number = {01},
pages = {8--14},
issn = {2456-2351},
url = {https://journalspub.com/publication/uncategorized/article=26504}
}
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Itishree Panda, Sitam Biswal, Surya Prakash Das | International Journal of Software Computing and Testing | Vol 12, Issue 01 | pp. 8-14 | ISSN: 2456-2351
Abstract
The broad sharing of information throughout human history was greatly accelerated by the emergence of the World Wide Web and the swift rise of social networking platforms such as Facebook and Twitter. Due to the extensive use of social media, users now generate and distribute vast amounts of content at an unprecedented rate, and a portion of this information is inaccurate or detached from factual reality. Automatically classifying a written article as misinformation or disinformation can be challenging. Even a subject-matter expert must take several aspects into account before determining the authenticity of an article. This study introduces an automated news classification framework based on machine learning techniques. The research examines multiple linguistic features that help differentiate authentic news from misleading or fabricated content. These features are used to train and evaluate several traditional machine learning algorithms as well as deep learning architectures. Among the tested models, the Deep Neural Network demonstrated the best performance, achieving an accuracy rate of 97%.
Keywords
Fake News, PCA, sentence embedding, K-means
🔒 This is a subscription article
Full text is available to subscribers and institutional members. Please choose an option below to access it.
1. Fernández-Reyes FC, Shinde S. Evaluating deep neural networks for automatic fake news detection in political domain. In: Ibero-American Conference on Artificial Intelligence. Cham: Springer International Publishing; 2018 Nov 13. p. 206–216. 2. Perez-Rosas V, Kazemi A, Mihalcea R, Hou R, Byrne N, Loeb S. MP64-02 fake news about prostate cancer: Distinguishing language patterns in misinformative online videos. J Urol. 2020 Apr 1;203:e963. 3. Wang WY. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv [Preprint]. 2017 May 1:arXiv:1705.00648. 4. Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R. Automatic detection of fake news. In: Proceedings of the 27th International Conference on Computational Linguistics; 2018 Aug. p. 3391–3401. 5. Rubin VL, Conroy N, Chen Y, Cornwell S. Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection; 2016 Jun. p. 7–17. 6. Sharma U, Saran S, Patil SM. Fake news detection using machine learning algorithms. Int J Creat Res Thoughts (IJCRT). 2020 Jun 6;8(6):509–518. 7. Yi J, Nasukawa T, Bunescu R, Niblack W. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: Third IEEE International Conference on Data Mining; 2003 Nov 22. p. 427–434. 8. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor Newsl. 2017 Sep 1;19(1):22–36. 9. Arunthavachelvan K, Raza S, Ding C. A deep neural network approach for fake news detection using linguistic and psychological features. User Model User-Adapt Interact. 2024 Sep 1;34(4):1043–1070. 10. Saikh T, De A, Ekbal A, Bhattacharyya P. A deep learning approach for automatic detection of fake news. In: Proceedings of the 16th International Conference on Natural Language Processing; 2019 Dec. p. 230–238.
How to cite this article
APA
Panda, I., Biswal, S., & Das, S. P. (2026). A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction. International Journal of Software Computing and Testing, 12(01), 8-14.
MLA
Panda, Itishree, et al. “A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction.” International Journal of Software Computing and Testing, vol. 12, no. 01, 2026, pp. 8-14.
Chicago
Itishree Panda, Sitam Biswal, and Surya Prakash Das. “A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction.” International Journal of Software Computing and Testing 12, no. 01 (2026): 8-14.
Vancouver
Panda I, Biswal S, Das SP. A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction. International Journal of Software Computing and Testing. 2026;12(01):8-14.
BibTeX
@article{PandaI2026,
author = {Itishree Panda and Sitam Biswal and Surya Prakash Das},
title = {A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction},
journal = {International Journal of Software Computing and Testing},
year = {2026},
volume = {12},
number = {01},
pages = {8--14},
issn = {2456-2351},
url = {https://journalspub.com/publication/uncategorized/article=26504}
}
Itishree Panda, Sitam Biswal, Surya Prakash Das | International Journal of Software Computing and Testing | Vol 12, Issue 01 | pp. 8-14 | ISSN: 2456-2351
Abstract
The broad sharing of information throughout human history was greatly accelerated by the emergence of the World Wide Web and the swift rise of social networking platforms such as Facebook and Twitter. Due to the extensive use of social media, users now generate and distribute vast amounts of content at an unprecedented rate, and a portion of this information is inaccurate or detached from factual reality. Automatically classifying a written article as misinformation or disinformation can be challenging. Even a subject-matter expert must take several aspects into account before determining the authenticity of an article. This study introduces an automated news classification framework based on machine learning techniques. The research examines multiple linguistic features that help differentiate authentic news from misleading or fabricated content. These features are used to train and evaluate several traditional machine learning algorithms as well as deep learning architectures. Among the tested models, the Deep Neural Network demonstrated the best performance, achieving an accuracy rate of 97%.
Keywords
Fake News, PCA, sentence embedding, K-means
🔒 This is a subscription article
Full text is available to subscribers and institutional members. Please choose an option below to access it.
1. Fernández-Reyes FC, Shinde S. Evaluating deep neural networks for automatic fake news detection in political domain. In: Ibero-American Conference on Artificial Intelligence. Cham: Springer International Publishing; 2018 Nov 13. p. 206–216. 2. Perez-Rosas V, Kazemi A, Mihalcea R, Hou R, Byrne N, Loeb S. MP64-02 fake news about prostate cancer: Distinguishing language patterns in misinformative online videos. J Urol. 2020 Apr 1;203:e963. 3. Wang WY. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv [Preprint]. 2017 May 1:arXiv:1705.00648. 4. Pérez-Rosas V, Kleinberg B, Lefevre A, Mihalcea R. Automatic detection of fake news. In: Proceedings of the 27th International Conference on Computational Linguistics; 2018 Aug. p. 3391–3401. 5. Rubin VL, Conroy N, Chen Y, Cornwell S. Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of the Second Workshop on Computational Approaches to Deception Detection; 2016 Jun. p. 7–17. 6. Sharma U, Saran S, Patil SM. Fake news detection using machine learning algorithms. Int J Creat Res Thoughts (IJCRT). 2020 Jun 6;8(6):509–518. 7. Yi J, Nasukawa T, Bunescu R, Niblack W. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: Third IEEE International Conference on Data Mining; 2003 Nov 22. p. 427–434. 8. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor Newsl. 2017 Sep 1;19(1):22–36. 9. Arunthavachelvan K, Raza S, Ding C. A deep neural network approach for fake news detection using linguistic and psychological features. User Model User-Adapt Interact. 2024 Sep 1;34(4):1043–1070. 10. Saikh T, De A, Ekbal A, Bhattacharyya P. A deep learning approach for automatic detection of fake news. In: Proceedings of the 16th International Conference on Natural Language Processing; 2019 Dec. p. 230–238.
How to cite this article
APA
Panda, I., Biswal, S., & Das, S. P. (2026). A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction. International Journal of Software Computing and Testing, 12(01), 8-14.
MLA
Panda, Itishree, et al. “A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction.” International Journal of Software Computing and Testing, vol. 12, no. 01, 2026, pp. 8-14.
Chicago
Itishree Panda, Sitam Biswal, and Surya Prakash Das. “A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction.” International Journal of Software Computing and Testing 12, no. 01 (2026): 8-14.
Vancouver
Panda I, Biswal S, Das SP. A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction. International Journal of Software Computing and Testing. 2026;12(01):8-14.
BibTeX
@article{PandaI2026,
author = {Itishree Panda and Sitam Biswal and Surya Prakash Das},
title = {A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction},
journal = {International Journal of Software Computing and Testing},
year = {2026},
volume = {12},
number = {01},
pages = {8--14},
issn = {2456-2351},
url = {https://journalspub.com/publication/uncategorized/article=26504}
}
