A predictive model for fake news detection using ensemble learning techniques
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Abstract
Nowadays, social media is one of the important activities for the general public for the purpose of sharing news and various stories with people. As a result, there are ill-intentioned individuals who seek to benefit themselves and cause chaos in society by creating and spreading fake news to the public. Recently, efforts have been made to develop systems for automatically detecting fake news on social media, such as using machine learning to analyze news data or using systems that learn the behavior of people on social media. From these methods, the concept of integrating multi-factor data analysis has emerged to enhance the accuracy and efficiency of fake news detection systems. This research aims to present a predictive model for detecting fake news using ensemble learning techniques with voting methods. It includes a dataset of 20,000 data points, considering factors such as media sources and user interactions. The test results, in conjunction with the fake news database recorded from social media like Fakeddit.com, show that the proposed model has a prediction accuracy of 96.97%, which is higher than other learning techniques used for comparison.
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References
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