A lightweight propagation path aggregating network with neural topic model for rumor detection
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 a...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 458; S. 468 - 477 |
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11.10.2021
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| 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. |
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| AbstractList | 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. |
| Author | Jia, Caiyan Li, Xuanya Zhang, Pengfei Ran, Hongyan Han, Xueming |
| Author_xml | – sequence: 1 givenname: Pengfei surname: Zhang fullname: Zhang, Pengfei organization: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China – sequence: 2 givenname: Hongyan surname: Ran fullname: Ran, Hongyan organization: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China – sequence: 3 givenname: Caiyan surname: Jia fullname: Jia, Caiyan email: cyjia@bjtu.edu.cn organization: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China – sequence: 4 givenname: Xuanya surname: Li fullname: Li, Xuanya email: lixuanya@baidu.com organization: Baidu Inc., , Beijing 100085, China – sequence: 5 givenname: Xueming surname: Han fullname: Han, Xueming organization: School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China |
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| Cites_doi | 10.1609/aaai.v34i01.5393 10.1145/2350190.2350203 10.1109/ICDM.2019.00090 10.1609/aaai.v34i05.6405 |
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| Keywords | Wasserstein autoencoder Rumor detection Propagation structure Lightweight Neural topic model |
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| Snippet | The structure information associated with message propagation has been proved to be effective to distinguish false and true rumors. However, existing methods... |
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| SubjectTerms | Lightweight Neural topic model Propagation structure Rumor detection Wasserstein autoencoder |
| Title | A lightweight propagation path aggregating network with neural topic model for rumor detection |
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