Rumor Detection on Time-Series of Tweets via Deep Learning

False information has become a weapon in cyber-warfare. How to detect false information effectively and efficiently on social media is a challenging problem. In this study, a novel method of rumor detection on Twitter tweets is proposed as a proof-of-concept for fast detection of false information on social media. Specifically, the proposed method will use the propagation pattern of the tweets to detect false information rather than the contents. As a result, the proposed method is very effective in reducing the dimensionality of the input feature set, and it requires much less computational time compared to content-based methods. Extensive experiments on PHEME dataset, a collection of Twitter rumors and non-rumors posted during five breaking news, have been performed to demonstrate the effectiveness of the proposed method. We also observe that deep learning models such as recurrent neural networks outperform classical machine learning models in terms of micro-F score.