Abstract
As communication advances and social media is growing day by day, the dessimation of fake news is rapidly increasing. This is a rising area of research that has received a considerable amount of attention, with certain constraints making it difficult to progress. This paper examines a machine learning-based technique for identifying deceptive news stories. For feature extraction, Term Frequency-Inverse Document Frequency (TF-IDF) was applied, and Logistic regression, Naïve bias, and Passive aggressive were employed as the classifier. Results obtained demonstrate that Logistic regression (because it can take a set of features that allows for the easy threshold to make a binary decision (real vs fake) and the ability to handle non-linearly separable data) gives the best accuracy as compared to Naïve Bayes and Passive aggressive classifier. With the help of Logistic Regression, the model has achieved an accuracy of 97.9% also this paper proposed the development of an android-based application that will work as a front-end application for fake news detection.
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Zafar, M.F., Rawat, N., Mishra, R., Shekhar Pandey, P., Kshetri, N. (2023). Uncovering Deception: A Study on Machine Learning Techniques for Fake News Detection. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_56
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DOI: https://doi.org/10.1007/978-981-99-5166-6_56
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