• Annu
• Rahul

The extensive dissemination of misinformation on social media has emerged as a significant concern, affecting public opinion and decision-making. The capacity to autonomously detect false information is essential in alleviating its detrimental impacts. This study introduces a machine learning methodology for identifying false news through the analysis of textual data utilizing many classification models, such as Logistic Regression, Naïve Bayes, and K-Nearest Neighbors (KNN). This study's dataset includes both authentic and fabricated news stories, which are subjected to preprocessing methods like tokenization, stopword elimination, and TF-IDF vectorization to transform textual content into structured numerical data. The efficacy of these models is assessed using critical metrics including accuracy, precision, recall, and F1-score, facilitating a comparison analysis to identify the most successful classification algorithm. The findings indicate that machine learning can markedly improve the precision of fake news detection, providing a dependable and automated method to address the proliferation of disinformation. This research advances the field of digital content verification and underscores the necessity of incorporating artificial intelligence-driven methodologies to enhance the reliability of online information.”

Fake News Detection, Social Media, Machine Learning, Text Classification, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Natural Language Processing, Misinformation, TF-IDF Vectorization.”

"Automated Fake News Detection on Social Media Platforms Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.b289-b298, July-2025, Available :http://www.jetir.org/papers/JETIR2507135.pdf

"Automated Fake News Detection on Social Media Platforms Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppb289-b298, July-2025, Available at : http://www.jetir.org/papers/JETIR2507135.pdf