A Multi-features Framework for Fake News Detection
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Abstract
Nowadays, social media is one of the key activities for the general public to share news and various stories with others. Consequently, there are malicious groups who seek to benefit themselves and cause societal disruption by creating and spreading fake news to the public. Recently, there have been efforts to develop systems for automatically detecting fake news on social media, such as using machine learning to analyze news data or employing systems to learn user behavior on social media. These methods have led to the concept of integrating multi-factor data analysis to enhance the accuracy of fake news detection systems. This research presents a multi-factor model for detecting fake news using techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), concatenation, and neural networks, based on user posts. The dataset consists of 20,000 data entries, considering factors like headlines, sources, and user interactions. Testing the model with the Fakeddit.com fake news database revealed that the proposed model performs better in prediction accuracy compared to single-factor analysis and traditional machine learning methods, achieving an accuracy rate of 0.8504 (85.04%).
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