A deep semantic-aware approach for Cantonese rumor detection in social networks with graph convolutional network

Xinyu Chen, Yifei Jian, Liang Ke, Yunxiang Qiu, Xingshu Chen, Yunya Song, Haizhou Wang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

The rapid development of social networks provides people with opportunities for communication, which also makes it easier for the spread of rumors. In addition to Mandarin Chinese and English rumors, Cantonese rumors have been a major concern on social networks. However, there is no available Cantonese rumor dataset that includes information of propagation structures. Moreover, existing approaches focused on Mandarin Chinese cannot be applied directly to Cantonese rumor detection because of the differences of words in glyphs and pronunciations between them. In this paper, we construct the first Cantonese rumor dataset with abundant propagation structure information. Moreover, a novel deep semantic-aware graph convolutional network is proposed for Cantonese rumor detection, which integrates the global structural information and the local semantic features of Cantonese posts. To be specific, a CantoneseBERT model is designed to encode deep semantic and syntactic representations of Cantonese text contents, which introduces Cantonese glyph and Jyutping embeddings into the inputs of the model. In addition, a Bi-GCN model is used to extract the propagation clues and dispersion information from two social network graphs with opposite directions. Experimental results demonstrate that the proposed model outperforms the state-of-the-art models with an F-score of 0.8686.
Original languageEnglish
Article number123007
Number of pages12
JournalExpert Systems with Applications
Volume245
Early online date4 Jan 2024
DOIs
Publication statusE-pub ahead of print - 4 Jan 2024

Scopus Subject Areas

  • Engineering(all)
  • Artificial Intelligence
  • Computer Science Applications

User-Defined Keywords

  • Cantonese rumor detection
  • CantoneseBERT model
  • Graph convolutional network
  • Social network graph

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