Abstract
The wide spread of rumors inflicts damages on social media platforms. Detecting rumors has become an emerging problem concerning the public and government. A crucial problem for rumors detection on social media is the lack of reliably pre-annotated dataset to train classification models. To solve this problem, we propose an unsupervised model that detects rumors by measuring how well the tweets follow the normal patterns. However, the problem is challenging in how to automatically discover the normal patterns of tweets. To tackle the challenge, we first propose a novel tree variational autoencoder model that reconstructs the sentiment labels along the propagation tree of a factual tweet. Then we propose a cross-alignment method to align the multiple modalities, i.e. tree structure and propagation features, and output the final prediction results. We conduct extensive experiments on a real-world dataset collected from Weibo. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised methods and adapts better to the concept drift than state-of-the-art supervised methods.
Published Version
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