Authors: Vishlesha Anil Habib, Vidya Gorakh Jagtap, Shrawani Ravindra Gaikwad, Nagraj Yashwant Kherud, Vaijayanti Pradip Kolhe
Abstract: The exponential growth of online platforms has enabled rapid dissemination of information, but it has also facilitated the widespread propagation of fake news. Fake news has negatively impacted political stability, public health, social harmony, and digital trust. This paper presents a comprehensive study and implementation of machine learning (ML) and Natural Language Processing (NLP)-based techniques for detecting fake news. The proposed system uses advanced text preprocessing, TF-IDF feature extraction, and multiple ML algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Naïve Bayes. Experimental results show that SVM achieves the highest accuracy of 94.8%, outperforming other models. This work demonstrates that combining linguistic features and machine learning provides a scalable and reliable approach to combat misinformation. Future enhancements include using transformer-based deep learning models and multilingual datasets
DOI: https://doi.org/10.5281/zenodo.18596939
