• Shivam Sharma
• Manish Kumar
• Rohit Singh

Keeping up with the enormous amount of news that is produced and shared online on a daily basis is difficult. This makes it difficult to identify and flag every fake news item. Fake news producers are using cutting-edge techniques like deep fakes and text produced by artificial intelligence to give their articles more legitimacy. Social media makes it simple for people to share news articles, even if they haven't verified their accuracy. By examining a range of characteristics, including the article's writing style, source, and social media interaction, machine learning algorithms can be trained to recognize fake news. Public awareness of bogus news can aid in its detection and avoidance. Programs in schools , public awareness campaigns, and factchecking tools can all help achieve this. Deep learning, (SVM), Natural Language Processing is among the most effective machine learning approaches for identifying fake news by analyzing language patterns and contextual cues in text. Some specific examples of tools and technology used for fake news detection: • Misleading headlines • Bias • Lack of evidence • Poor grammar and spelling • Unreliable sources The following are some instances of outcomes and products for the finding of fake News: Fact Check Explorer on Google: When a user searches for a claim, it presents a list of verified claims. The claim, the fact-checking organization, the fact-check rating ,and a link to the fact-check article are all included in the list. Facebook’s Third-Party Fact-Checking Program: Fake news articles that are flagged by fact-checking organizations on Facebook are rated as "False" or "Misleading." Facebook then lessens the article's News Feed dissemination. Twitter's Birdwatch Pilot: A user's note appears beneath a potentially deceptive tweet when they add one. After reading the remark, other users can decide whether or not to find it useful. Future investigations to identify false news: 1. Creating machine learning models with real-time fake news detection capabilities. 2. Creating machine learning models that are capable of identifying bogus news across a variety of media platforms and languages. 3. Increasing the precision of machine learning models used to identify false news.

Artificial Intelligence, Fuzzy Logic, Fuzzy Inference, Machine Learning, Naïve Based Classifier, News, Forecst, Guidance, Support Vector Machine (SVM).

"FAKE NEWS DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.a484-a488, March-2025, Available :http://www.jetir.org/papers/JETIR2503064.pdf

"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.12, Issue 3, page no. ppa484-a488, March-2025, Available at : http://www.jetir.org/papers/JETIR2503064.pdf