Prediction of Fake Twitters Using AdaBoost-Based Neuro-Evolution of Augmenting Topologies Algorithm

Knowledge dissemination had never before been hampered in the history of humanity until the World Wide Web's development and the rapid adoption of social media outlets. As a result of the growing usage of social media platforms, fake news is increasingly common in all kinds of circumstances. After the internet evolved, most of the people are utilizing Internet for their personal purpose only at the same time they are uncontrolled to read many of fake news, also. Automated classification of a text article as real or fake is a challenging task. In this situation, to detect such types of fake news and to provide well verified news to our society, the machine learning (ML) techniques such as support vector machine, linear regression, K-nearest neighbor, neuro-evolution of augmenting topologies (NEAT) and boosting NEAT are applied in this research. After preprocesses over the actual dataset methods effectively identify the fake news with collected dataset and evaluated by the metrics such as accuracy, precision, recall and F1-score.