Abstract
Nowadays, social media has become a dominant platform for disseminating news and information. However, it also accelerated the spread of rumors, which causes great impacts on the real world. Therefore, the detection of rumors plays a crucial role in controlling the diffusion of misinformation. Cantonese is widely spoken across the world, yet limited research has been conducted on Cantonese rumor detection. Moreover, current detection methods primarily focus on extracting textual and propagation structure characteristics without utilizing external knowledge to enhance the performance of identifying rumors. In this paper, we propose a novel model using Bidirectional Graph Convolutional Networks Embedded with External Knowledge, namely BGEK, for Cantonese rumor detection. Specifically, we first construct a directed heterogeneous knowledge graph using official statements and related entity descriptions from Wikipedia to obtain external knowledge embeddings. Secondly, we use BERT (Bidirectional Encoder Representations from Transformers) model to extract text features from source tweets and obtain the correlation vectors of external knowledge and text features through the Comparison Network. Thirdly, we utilize Bidirectional Graph Convolutional Networks to extract rumor propagation structural features. Finally, we fuse text features, structural features and comparison features to construct a Cantonese rumor detection model. To the best of our knowledge, we are the first to apply external knowledge to Cantonese rumor detection. Experimental results demonstrate that the BGEK model outperforms existing state-of-the-art detection models.
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Acknowledgments
This work is supported by the National Key Research and Development Program of China under grant No. 2022YFC3303101 and Key Research and Development Program of Science and Technology Department of Sichuan Province under grant No. 2023YFG0145.
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Wang, X., Luo, C., Guo, T., Liu, Z., Zhang, J., Wang, H. (2023). BGEK: External Knowledge-Enhanced Graph Convolutional Networks for Rumor Detection in Online Social Networks. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_24
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