BINARY LOGISTIC REGRESSION BASED CLASSIFIER FOR FAKE NEWS
Keywords:
News, Fake, Classifier, Machine Learning, Binary Linear Regression, Big DataAbstract
Addressing modern society's mutual need for reliable News from new media has been a challenge since the development of modern mobile and telecommunication technologies. This study attempts to contribute to current solutions in mitigating the spread of fake news by creating a classifier based on a logistic binary regression commonly prevalent in the field of machine learning and artificial intelligence. A dataset of 10,000 news data has been used in this study and was processed to measure the sentiment neutrality, page rank and content length to content structure error ratio for each data to create a model for the fake news classifier. The model was then subject to prediction tests and has shown to have an 80% accuracy rate, therefore implying that the model derived from this study is capable of classifying legitimate news apart from fake news.
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