Classification of fake news using machine learning and deep learning

Öz

The rapid spread of fake news through digital channels is a major problem. In this study, after processing the texts with natural language processing techniques, machine learning methods and deep learning methods, the style-based detection of fake news was investigated with text analysis. After the necessary text processing on the open-source dataset ISOT, different models were built using word representations (TF-IDF, word2Vec) and different machine learning (K nearest neighbor Naïve Bayes, logistic regression) and deep learning Long Short-Term Memory (LSTM) methods. Acc, P, R and F were used to evaluate the performance of these models. On the fake news dataset, the LSTM model performed best with 99.2% Acc. Improving state-of-the-art methods on word representations and classification steps, including preprocessing in text classification processes, and making them usable in a practical environment can significantly reduce the amount of fake news.

Anahtar Kelimeler

• Deep learning
• Fake news detection
• Machine learning
• Style based detection.

Kaynakça

1. C. K. Hiramath and G. C. Deshpande, “Fake News Detection Using Deep Learning Techniques,” in 2019 1st International Conference on Advances in Information Technology (ICAIT), IEEE, Jul. 2019, pp. 411–415. doi: 10.1109/ICAIT47043.2019.8987258.
2. H. Ahmed, I. Traore, and S. Saad, “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques,” 2017, pp. 127–138. doi: 10.1007/978-3-319-69155-8_9.
3. F. A. Ozbay and B. Alatas, “Fake news detection within online social media using supervised artificial intelligence algorithms,” Physica A: Statistical Mechanics and its Applications, vol. 540, p. 123174, Feb. 2020, doi: 10.1016/j.physa.2019.123174.
4. S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep Learning--based Text Classification,” ACM Comput Surv, vol. 54, no. 3, pp. 1–40, Apr. 2022, doi: 10.1145/3439726.
5. D. Muduli, S. K. Sharma, D. Kumar, A. Singh, and S. K. Srivastav, “Maithi-Net: A Customized Convolution Approach for Fake News Detection using Maithili Language,” in 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/IC2E357697.2023.10262664.
6. M. Granik and V. Mesyura, “Fake news detection using naive Bayes classifier,” in 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), IEEE, May 2017, pp. 900–903. doi: 10.1109/UKRCON.2017.8100379.
7. A. Priyadarshi and S. K. Saha, “Towards the first Maithili part of speech tagger: Resource creation and system development,” Comput Speech Lang, vol. 62, p. 101054, Jul. 2020, doi: 10.1016/j.csl.2019.101054.
8. I. Ahmad, M. Yousaf, S. Yousaf, and M. O. Ahmad, “Fake News Detection Using Machine Learning Ensemble Methods,” Complexity, vol. 2020, pp. 1–11, Oct. 2020, doi: 10.1155/2020/8885861.

9. A. K. Shalini, S. Saxena, and B. S. Kumar, “Automatic detection of fake news using recurrent neural network—Long short-term memory,” Journal of Autonomous Intelligence, vol. 7, no. 3, Dec. 2023, doi: 10.32629/jai.v7i3.798.
10. M. Akhter et al., “COVID-19 Fake News Detection using Deep Learning Model,” Annals of Data Science, Jan. 2024, doi: 10.1007/s40745-023-00507-y.
11. J. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in Neural Information Processing Systems, 2013. [Online]. Available: https://proceedings.neurips.cc/paper
12. R. Ahmed, M. Bibi, and S. Syed, “Improving Heart Disease Prediction Accuracy Using a Hybrid Machine Learning Approach: A Comparative study of SVM and KNN Algorithms,” International Journal of Computations, Information and Manufacturing (IJCIM), vol. 3, no. 1, pp. 49–54, Jun. 2023, doi: 10.54489/ijcim.v3i1.223.
13. T. Öztürk, Z. Turgut, G. Akgün, and C. Köse, “Machine learning-based intrusion detection for SCADA systems in healthcare,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 11, no. 1, p. 47, Dec. 2022, doi: 10.1007/s13721-022-00390-2.
14. H. Canlı and S. Toklu, “Design and Implementation of a Prediction Approach Using Big Data and Deep Learning Techniques for Parking Occupancy,” Arab J Sci Eng, vol. 47, no. 2, pp. 1955–1970, Feb. 2022, doi: 10.1007/s13369-021-06125-1.
15. R. Vankdothu, M. A. Hameed, and H. Fatima, “A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method,” Computers and Electrical Engineering, vol. 101, p. 107960, Jul. 2022, doi: 10.1016/j.compeleceng.2022.107960.
16. H. Canli and S. Toklu, “Deep Learning-Based Mobile Application Design for Smart Parking,” IEEE Access, vol. 9, pp. 61171–61183, 2021, doi: 10.1109/ACCESS.2021.3074887.
17. M. Z. Khaliki and M. S. Başarslan, “Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN,” Sci Rep, vol. 14, no. 1, p. 2664, Feb. 2024, doi: 10.1038/s41598-024-52823-9.
18. S. N. Başa and M. S. Basarslan, “Sentiment Analysis Using Machine Learning Techniques on IMDB Dataset,” in 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), IEEE, Oct. 2023, pp. 1–5. doi: 10.1109/ISMSIT58785.2023.10304923.
19. F. Kayaalp, M. S. Basarslan, and K. Polat, “TSCBAS: A Novel Correlation Based Attribute Selection Method and Application on Telecommunications Churn Analysis,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), IEEE, Sep. 2018, pp. 1–5. doi: 10.1109/IDAP.2018.8620935.
20. Öztürk, T., Turgut, Z., Akgün, G. et al. Machine learning-based intrusion detection for SCADA systems in healthcare. Netw Model Anal Health Inform Bioinforma 11, 47 (2022). https://doi.org/10.1007/s13721-022-00390-2
21. Ardaç, H.A., Erdoğmuş, P. Question answering system with text mining and deep networks. Evolving Systems (2024). https://doi.org/10.1007/s12530-024-09592-7
22. Google LLC, “Colab.” https://colab.research.google.com/. Accessed 1 Feb 2023
23. Python, “Python.” https://www.python.org/downloads/. Accessed 1 Feb 2023