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10.1007/s11042-020-10034-0 http://scihub22266oqcxt.onion/10.1007/s11042-020-10034-0 33132740!7585996!33132740     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Multimed+Tools+Appl 2021 ; 80 (5): 7187-7204 Nephropedia Template TP {{tp|p=33132740|t=2021. A graph-based approach for population health analysis using Geo-tagged tweets |pdf=|usr=}}{{33132740}} gab.com Text A graph-based approach for population health analysis using Geo-tagged tweets JO: Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=33132740 #FOAvip We propose in this work a graph-based approach for automatic public health analysis using social media. In our approach, graphs are created to model the interactions between features and between tweets in social media. We investigated different graph properties and methods in constructing graph-based representations for population health analysis. The proposed approach is applied in two case studies: (1) estimating health indices, and (2) classifying health situation of counties in the US. We evaluate our approach on a dataset including more than one billion tweets collected in three years 2014, 2015, and 2016, and the health surveys from the Behavioral Risk Factor Surveillance System. We conducted realistic and large-scale experiments on various textual features and graph-based representations. Experimental results verified the robustness of the proposed approach and its superiority over existing ones in both case studies, confirming the potential of graph-based approach for modeling interactions in social networks for population health analysis. Twit Text FOAVip A graph-based approach for population health analysis using Geo-tagged tweets ????Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=33132740 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33132740 #FOAvip English Wikipedia <ref>{{Cite pmid|33132740}}</ref> A graph-based approach for population health analysis using Geo-tagged tweets #MMPMID33132740 Nguyen H; Nguyen T; Nguyen DT Multimed Tools Appl 2021[]; 80 (5): 7187-7204 PMID33132740show ga We propose in this work a graph-based approach for automatic public health analysis using social media. In our approach, graphs are created to model the interactions between features and between tweets in social media. We investigated different graph properties and methods in constructing graph-based representations for population health analysis. The proposed approach is applied in two case studies: (1) estimating health indices, and (2) classifying health situation of counties in the US. We evaluate our approach on a dataset including more than one billion tweets collected in three years 2014, 2015, and 2016, and the health surveys from the Behavioral Risk Factor Surveillance System. We conducted realistic and large-scale experiments on various textual features and graph-based representations. Experimental results verified the robustness of the proposed approach and its superiority over existing ones in both case studies, confirming the potential of graph-based approach for modeling interactions in social networks for population health analysis. äA graph-based approach for population health analysis using Geo-tagged(epmc).pdf DeepDyve Pubget Overpricing