Abstract
Advancement of technology and rapidly growing telecom giants like Jio platforms and Bharti Airtel, have made information accessible to just about everyone. The Internet has proven itself to be a boon with people relying on it for education, business, news, etc., but the trustworthiness of information is not guaranteed. Vast amount of information is churned out via print and online media, but it is not easy to tell whether the information generated is true or false. At present times, false info about terrorism, natural disasters, pandemic, science, COVID-19 virus, or financial information has become a menace. Individuals are devouring day-by-day news they need, and fake news has been found to spread significantly faster than genuine news. People are consuming daily news they need, and fake news has been found to notoriously spread even quicker than real news. It has an extraordinary negative effect on people just as on society in general. For fake news classification, we are applying methods and models from ML literature in this paper. The goal of this project is to make a classifier that can recognize if the given news snippet is fake based only its text. Our goal was to develop deep learning models for identifying fake news and labeling them as false or real. Our experiments on the dataset using ensemble technique show a promising accuracy of 91.87%.
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Jindal, R., Dahiya, D., Sinha, D., Garg, A. (2022). A Study of Machine Learning Techniques for Fake News Detection and Suggestion of an Ensemble Model. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_51
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DOI: https://doi.org/10.1007/978-981-16-3071-2_51
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