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The steep increase of fake news influenced by social media trends has remained a bottleneck not just to the Nigerian government alone but to the global world. Since this unverified news is spread on online social media, blogs, and other mediums for financial or other motives, this paper developed an improved ensemble-based online fake news detection system to curb the menace. The system adopted the weighted majority voting techniques to evaluate the combined decisions of the artificial neural network, decision tree classifier, k-Nearest Neighbors, passive-aggressive classifiers, and support vector machine in predicting any news article. The developed system has a lower computational time and achieved a 1.18% improvement over the state-of-art with F1-score, recall, precision, and accuracy of 99.36%, 99.27%, 99.45%, and 99.68% respectively. Furthermore, a novel online fake news crawler agent known as NTA (news tracker agent) with the capability of monitoring trusted websites continually while updating the database information instantaneously was developed to mitigate the fake news dynamic nature. Hence, the system allows easy verification of news before sharing thereby ameliorating the wide spread of fake information
Idakwo et al. (Tue,) studied this question.
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