• Sunil Matkar
• Mayur Gavade
• Ganesh Karale
• Jatin Tonge
• Prof S.V Shinde

Abstract The rise of fake news has posed a significant challenge within these networks, impacting our society. Detecting and combating fake news is crucial to ensure the reliability of information spread on social media platforms. This research proposes the utilization of machine learning techniques, specifically Natural Language Processing (NLP) algorithms, to detect fake news. The approach involves data normalization as a pre-processing step to clean the data before applying machine learning methods for classification. The model also considers the credibility of content and user reputation as factors in assessing the authenticity of news. The goal is to automate the detection process by training a model on a credibility-focused dataset, enabling accurate assessments of fake news on social media.

fake news, machine learning, NLP, information exchange, credibility, user reputation, Machine Learning

" Implementation of Fake News Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.k306-k309, May-2023, Available :http://www.jetir.org/papers/JETIR2305A41.pdf

" Implementation of Fake News Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppk306-k309, May-2023, Available at : http://www.jetir.org/papers/JETIR2305A41.pdf