Model for Fake News Detection Using AI Technique

Abstract

The spread of fake news on social media platforms can have serious consequences for society, especially in urgent situations such as crises. Despite efforts to combat it, fake news is still able to proliferate rapidly through social media, where users share and exchange a vast amount of information on a daily basis. This information, however, is not always accurate, making it difficult to distinguish real news from fake news. To address this problem, this research proposes additional characteristics based on social interactions and content to identify fake news on social media platforms. These characteristics are designed to work in conjunction with each other and are found to be more effective in identifying fake news compared to the current baseline criteria. In addition, a CNN-LSTM model is used to analyze the text and predict the veracity of news. Unlike early research that focuses on fake news that has been circulating for a long time, this study tests the identification of fake news on a real-world dataset. The proposed features and machine learning models outperformed the baseline in terms of accuracy, recall, and F1 metrics, which are standard measures of classification model performance.