SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection
作者：
Highlights：
• An enhanced text representation model for content-based early fake news detection.
• Global semantic relations between sentences modeled with graph convolution network.
• Local sentence representation extracted by applying 1D convolution on local context.
• Document representation obtained by incorporating the sentence order through LSTM.
• Generalization ability verified on cross-source and cross-domain datasets.
摘要
•An enhanced text representation model for content-based early fake news detection.•Global semantic relations between sentences modeled with graph convolution network.•Local sentence representation extracted by applying 1D convolution on local context.•Document representation obtained by incorporating the sentence order through LSTM.•Generalization ability verified on cross-source and cross-domain datasets.
论文关键词：Fake news detection,Enhanced text representation,Global semantic relationship,Local sequential order,Graph neural network
论文评审过程：Received 12 July 2020, Revised 17 September 2020, Accepted 2 October 2020, Available online 3 October 2020, Version of Record 7 October 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114090
