A Machine Learning Approach to Fake News Detection using Python

Author(s)	Mr. Ramakrishna Reddy BIjjam, Rajesh Babu M
Country	India
Abstract	The proliferation of fake news on digital and social platforms is recognized as a significant threat to social stability and democratic discourse, thereby necessitating the development of scalable automated detection solutions. In this article, a holistic approach to fake news detection is presented, leveraging Python and modern machine learning methodologies. Theoretical underpinnings and the evolving landscape of misinformation are analyzed, followed by a detailed explanation of data acquisition, annotation, and pre-processing processes. Foundational and advanced feature engineering methods—such as bag-of-words, TF-IDF, and word embeddings—are explored to effectively capture textual nuances for classification tasks.
Keywords	TF-IDF , Word Embeddings, Social Platforms, ML Methodologies, Datasets, Python, Real Time Detections, AI,SVM,LSM,CNN,RNN,BiRNN,F1Score, Pre- Processing, Python, Deep Learning, Natural Language Processing (NLP), BERT, Transformer Models, Automated Detection Systems.
Field	Computer Applications
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-21
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58534

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