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Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization.

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第一作者： Thirunavukarasu,Balasubramaniam

第一单位： School of Computer Science and Centre for Data Science, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000 Australia.

作者： Thirunavukarasu,Balasubramaniam ^[1] ; Richi,Nayak ^[1] ; Khanh,Luong ^[1] ; Md Abul,Bashar ^[1]

作者单位： School of Computer Science and Centre for Data Science, Queensland University of Technology, 2 George St, Brisbane City, QLD 4000 Australia. ^[1]

关键词 Covid-19 Misinformation detection Nonnegative tensor factorization Ranking Saturating Coordinate Descent Spatio-temporal patterns Topic modelling

DOI 10.1007/s13278-021-00767-7

PMID 34149960

发布时间 2024-04-04

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Social network analysis and mining

2021年11卷1期

57页

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Social network analysis and mining

2021年11卷1期

57页

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Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization.

Social network analysis and mining

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Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization.

Social network analysis and mining

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同项目论文

Social network analysis and mining

相关期刊

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