Fake News Detection on Social Media: A Temporal-Based Approach

Abstract

Following the development of communication techniques and smart devices, the era of Artificial Intelligence (AI) and big data has arrived. The increased connectivity, referred to as hyper-connectivity, has led to the development of smart cities. People in these smart cities can access numerous online contents and are always connected. These developments, however, also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels. Information cannot often be verified, which can confuse the users. The increasing influence of social media has thus led to the emergence and increasing prevalence of fake news. In this study, we propose a methodology to classify and identify fake news emanating from social channels. We collected content from Twitter to detect fake news and statistically verified that the temporal propagation pattern of quote retweets is effective for the classification of fake news. To verify this, we trained the temporal propagation pattern to a two-phases deep learning model based on convolutional neural networks and long short-term memory. The fake news classifier demonstrates the ability for its early detection. Moreover, it was verified that the temporal propagation pattern was the most influential feature compared to other feature groups discussed in this paper.