• Sehar Nazeer
• Kamal Kumar
• Gautam Kumar

The development of the World Wide Web and the quick adoption of social media platforms (such as Facebook and Twitter) prepared the door for the most rapid transmission of knowledge in recorded human history. Consumers are producing and spreading more information than ever before, some of it erroneous and unconnected to reality, thanks to social media platforms. Using an algorithm to identify text that contains erroneous or misleading information is difficult. Even a subject area expert must consider a variety of factors when deciding whether something is genuine or false. For the automated categorization of news stories, we suggest an ensemble machine learning approach in this study. Our research looks at a variety of linguistic traits that can be utilized to tell false information from true. We train a range of distinctive machine learning algorithms utilizing those characteristics in a variety of ensemble approaches, and then we evaluate their performance on four real-world datasets. Experimental testing shows that our suggested ensemble learner technique works better than individual learner approaches.

Fake News, social platforms, deep learning

"FAKE NEWS DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.b513-b519, September-2022, Available :http://www.jetir.org/papers/JETIR2209162.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.9, Issue 9, page no. ppb513-b519, September-2022, Available at : http://www.jetir.org/papers/JETIR2209162.pdf