Fake News Detection Using TF-IDF Weighted with Word2Vec: An Ensemble Approach
Keywords:
Convolutional Neural Networks, Machine learning, TF-IDF Weighted Vector, Word2VecAbstract
Social media platforms' utilization for news consumption is steadily growing due to their accessibility, affordability, appeal, and ability to propagate misinformation. False information, whether intentionally or unintentionally created, is being disseminated across the internet. Certain individuals spread inaccurate information on social media to gain attention, financial benefits, or political advantage. This has a detrimental impact on a substantial portion of society that is heavily influenced by technology. It is imperative for us to develop better discernment in distinguishing between fake and genuine news. In this research paper, we present an ensemble approach for detecting fake news by using TF-IDF Weighted Vector with Word2Vec. The extracted features capture specific textual characteristics, which are converted into numerical representations for training the models and balanced dataset with the Random over Sampling technique. The implementation of our proposed framework utilized the ensemble approach with majority voting which combines 2 machine learning models like Random Forest and Decision Tree. The proposed strategy was adopted empirically evaluated against contemporary techniques and basic classifiers, including Gaussian Naïve Bayes, Logistic Regression, Multilayer Perceptron, and XGBoost Classifier. The effectiveness of our approach is validated through the evaluation of the accuracy, F1-Score, Precision, Recall, and Auc curve, yielding an impressive accuracy score of 94.24% on the FakeNewsNet dataset.
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S. K. Mondal, J. P. Sahoo, J. Wang, K. Mondal, and Md. M. Rahman, “Fake News Detection Exploiting TF-IDF Vectorization with Ensemble Learning Models,” in Springer eBooks, 2022, pp. 261–270. doi: 10.1007/978-981-16-4807-6_25.
A. Abdulrahman and M. Baykara, Fake News Detection Using Machine Learning and Deep Learning Algorithms. 2020. doi: 10.1109/icoase51841.2020.9436605.
A. Mallik and S. Kumar, “Word2Vec and LSTM based deep learning technique for context-free fake news detection,” Multimedia Tools and Applications, May 2023, Published, doi: 10.1007/s11042-023-15364-3.
L. Waikhom and R. S. Goswami, “Fake News Detection Using Machine Learning,” SSRN Electronic Journal, 2019, Published, doi: 10.2139/ssrn.3462938..
Kaliyar, R.K., Goswami, A. & Narang, P. FakeBERT: Fake news detection in social media with a BERT-based deep learning approach. Multimed Tools Appl 80, 11765–11788 (2021). https://doi.org/10.1007/s11042-020-10183-2
Mallick, C., Mishra, S. & Senapati, M.R. A cooperative deep learning model for fake news detection in online social networks. J Ambient Intell Human Comput 14, 4451–4460 (2023). https://doi.org/10.1007/s12652-023-04562-4
Mohawesh, R., Maqsood, S. & Althebyan, Q. Multilingual deep learning framework for fake news detection using capsule neural network. J Intell Inf Syst (2023). https://doi.org/10.1007/s10844-023-00788-y
Ishfaq Manzoor, Syed & Singla, Jimmy & Nikita,. (2019). Fake News Detection Using Machine Learning approaches: A systematic Review. 230-234. 10.1109/ICOEI.2019.8862770.
Abdulaziz Albahr and Marwan Albahar, “An Empirical Comparison of Fake News Detection using different Machine Learning Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110917
Rodríguez, Álvaro Ibrain and Lara Lloret Iglesias. “Fake news detection using Deep Learning.” ArXiv abs/1910.03496 (2019)
aslam, Nida & Khan, Irfan & Salem, Farah & Aldaej, Lama & Aldubaikil, Asma. (2021). Fake Detect: A Deep Learning Ensemble Model for Fake News Detection. Complexity. 2021. 1-8. 10.1155/2021/5557784.
Mosallanezhad, Ahmadreza & Karami, Mansooreh & Shu, Kai & Mancenido, Michelle & Liu, Huan. (2022). Domain Adaptive Fake News Detection via Reinforcement Learning.
Sahoo, Somya Ranjan & Gupta, Brij B. (2020). Multiple features based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing. 100. 10.1016/j.asoc.2020.106983.
Wang, Yaqing & Yang, Weifeng & Ma, Fenglong & Xu, Jin & Zhong, Bin & Deng, Qiang & Gao, Jing. (2020). Weak Supervision for Fake News Detection via Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 34. 516-523. 10.1609/aaai.v34i01.5389.
Özbay, Feyza & Alatas, Bilal. (2019). Fake news detection within online social media using supervised artificial intelligence algorithms. Physica A: Statistical Mechanics and its Applications. 540. 123174. 10.1016/j.physa.2019.123174.
Shu, Kai & Zhou, Xinyi & Wang, Suhang & Zafarani, Reza & Liu, Huan. (2019). The role of user profiles for fake news detection. 436-439. 10.1145/3341161.3342927.
Q. Zhang, Z. Guo, Y. Zhu, P. Vijayakumar, A. Castiglione, and B. B. Gupta, “A Deep Learning-based Fast Fake News Detection Model for Cyber-Physical Social Services,” Pattern Recognition Letters, vol. 168, pp. 31–38, Apr. 2023, doi: 10.1016/j.patrec.2023.02.026.
Qaiser, Shahzad & Ali, Ramsha. (2018). Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents. International Journal of Computer Applications. 181. 10.5120/ijca2018917395.
Ma, Long & Zhang, Yanqing. (2015). Using Word2Vec to process big text data. 10.1109/BigData.2015.7364114.
S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 40–46, Jun. 2021, doi: 10.1016/j.ijcce.2021.01.001.
Bhatt, S., Goenka, N., Kalra, S., Sharma, Y. (2022). Fake News Detection: Experiments and Approaches Beyond Linguistic Features. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-16-2937-2_9
Ryoya Furukawa, Daiki Ito, Yuta Takata, Hiroshi Kumagai, Masaki Kamizono, Yoshiaki Shiraishi, and Masakatu Morii. 2022. Fake News Detection via Biased User Profiles in Social Networking Sites. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '21). Association for Computing Machinery, New York, NY, USA, 136–145. https://doi.org/10.1145/3486622.3493939.
J. Lin, G. Tremblay-Taylor, G. Mou, D. You and K. Lee, "Detecting Fake News Articles," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 3021-3025, doi: 10.1109/BigData47090.2019.9005980.
Rai, Nishant & Kumar, Deepika & Kaushik, Naman & Ali, Ahad. (2022). Fake News Classification using transformer based enhanced LSTM and BERT. International Journal of Cognitive Computing in Engineering. 3. 10.1016/j.ijcce.2022.03.003.
Vaqur, M. ., Kumar, R. ., Singh, R. ., Umang, U., Gehlot, A. ., Vaseem Akram, S. ., & Joshi, K. . (2023). Role of Digitalization in Election Voting Through Industry 4.0 Enabling Technologies. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 123–130. https://doi.org/10.17762/ijritcc.v11i2.6136
Prof. Madhuri Zambre. (2016). Automatic Vehicle Over speed Controlling System using Microcontroller Unit and ARCAD. International Journal of New Practices in Management and Engineering, 5(04), 01 - 05. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/47
Dhabliya, D., & Sharma, R. (2019). Cloud computing based mobile devices for distributed computing. International Journal of Control andAutomation, 12(6 Special Issue), 1-4. doi:10.33832/ijca.2019.12.6.01
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