A lightweight propagation path aggregating network with neural topic model for rumor detection

Abstract

The structure information associated with message propagation has been proved to be effective to distinguish false and true rumors. However, existing methods lack an efficient way to learn the representation of the whole rumors which captures the intrinsic mechanism of rumor propagation structures and semantics. In this study, we propose a lightweight propagation path aggregating (PPA) neural network for rumor embedding and classification. In the network, we first model the propagation structure of each rumor as an independent set of propagation paths in which each path represents the source post in a different talking context. We then aggregate all paths to obtain the representation of the whole propagation structure. Besides, we utilize a neural topic model in the Wasserstein autoencoder (WAE) framework to capture event insensitive stance patterns in response propagation trees where no source post is included. Empirical studies demonstrate that 1) PPA achieves the state-of-the-art performance with much less parameters and training time, 2) PPA can further benefit from the pre-trained neural topic model which enables to fully use unlabeled data, thus improves the performance of PPA especially when labeled samples are limited or rumors are spreading at early stage. Meanwhile, this topic model offers an explicit interpretation of stance patterns in the form of topics, consequently improves interpretability of the PPA network. The source code can be available at https://github.com/zperfet/PathFakeGit.