Detecting Emerging Rumors by Embedding Propagation Graphs

In this paper, we propose a propagation-driven approach to discover newly emerging rumors which are spreading on social media. Firstly, posts and their responsive ones (i.e., comments, sharing) are modeled as graphs. These graphs will be embedded using their structure and node’s attributes. We then train a classifier to predict from these graph embedding vectors rumor labels. In addition, we also propose an incremental training method to learn embedding vectors of out-of-vocabulary (OOV) words because newly emerging rumor regularly contains new terminologies. To demonstrate the actual performance, we conduct an experiment by using a real-world dataset which is collected from Twitter. The result shows that our approach outperforms the state-of-the-art method with a large margin.

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