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10.1016/j.eswa.2020.114090 http://scihub22266oqcxt.onion/10.1016/j.eswa.2020.114090 33041529!7532792!33041529     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33041529&cmd=llinks): Failed to open stream: HTTP request failed! 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SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection |pdf=|usr=}}{{33041529}} gab.com Text SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection JO: Expert Syst Appl http://www.kidney.de/pget.php?&tw=1&pmid=33041529 #FOAvip The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods. Twit Text FOAVip SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection ????Expert Syst Appl http://www.kidney.de/pget.php?&tw=1&pmid=33041529 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33041529 #FOAvip English Wikipedia <ref>{{Cite pmid|33041529}}</ref> SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection #MMPMID33041529 Wang Y; Wang L; Yang Y; Lian T Expert Syst Appl 2021[Mar]; 166 (ä): 114090 PMID33041529show ga The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods. äSemSeq4FD: Integrating global semantic relationship and local sequenti(epmc).pdf DeepDyve Pubget Overpricing