A Machine Learning Technique for Detection of Social Media Fake News

Micheal Olaolu Arowolo, Sanjay Misra and Roseline Oluwaseun Ogundokun
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Micheal Olaolu Arowolo: Landmark University, Nigeria
Sanjay Misra: Institute for Energy Technology, Halden, Norway
Roseline Oluwaseun Ogundokun: Kaunas University of Technology, Lithuania

International Journal on Semantic Web and Information Systems (IJSWIS), 2023, vol. 19, issue 1, 1-25

Abstract: The emergence of the Internet and the growing development of online platforms (like Facebook and Instagram) opened the way for disseminating information that hasn't been experienced in the history of mankind earlier. Consumers generate and share more information and a massive amount of data than ever with the growing utilization of social media sites, many of which are deceptive with little relevance to reality. A daunting task is the automated classification of a text article as misleading or misinformation. To see the latest news alerts, individuals often utilize e-newspapers, Twitter, Instagram, Youtube, and many more. Fake news created on social media can lead to uncertainty amongst individuals and psychiatric illness. We may detect that news obtained based on machine learning techniques is either true or false. This study proposes a machine learning technique to detect fake news by carrying out filtration on social media data, classifying the preprocessed data using a machine learning algorithm, evaluating the developed system, and evaluating the results.

Date: 2023
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