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10.3390/ijerph17145081 http://scihub22266oqcxt.onion/10.3390/ijerph17145081 32674510!7399940!32674510     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 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 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 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (14): ä Nephropedia Template TP {{tp|p=32674510|t=2020. Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis |pdf=|usr=}}{{32674510}} gab.com Text Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32674510 #FOAvip Background: COVID-19 has greatly attacked China, spreading in the whole world. Articles were posted on many official WeChat accounts to transmit health information about this pandemic. The public also sought related information via social media more frequently. However, little is known about what kinds of information satisfy them better. This study aimed to explore the characteristics of health information dissemination that affected users' information behavior on WeChat. Methods: Two-wave data were collected from the top 200 WeChat official accounts on the Xigua website. The data included the change in the number of followers and the total number of likes on each account in a 7-day period, as well as the number of each type of article and headlines about coronavirus. It was used to developed regression models and conduct content analysis to figure out information characteristics in quantity and content. Results: For nonmedical institution accounts in the model, report and story types of articles had positive effects on users' following behaviors. The number of headlines on coronavirus positively impacts liking behaviors. For medical institution accounts, report and science types had a positive effect, too. In the content analysis, several common characteristics were identified. Conclusions: Characteristics in terms of the quantity and content in health information dissemination contribute to users' information behavior. In terms of the content in the headlines, via coding and word frequency analysis, organizational structure, multimedia applications, and instructions-the common dimension in different articles-composed the common features in information that impacted users' liking behaviors. Twit Text FOAVip Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32674510 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32674510 #FOAvip English Wikipedia <ref>{{Cite pmid|32674510}}</ref> Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis #MMPMID32674510 Ma R; Deng Z; Wu M Int J Environ Res Public Health 2020[Jul]; 17 (14): ä PMID32674510show ga Background: COVID-19 has greatly attacked China, spreading in the whole world. Articles were posted on many official WeChat accounts to transmit health information about this pandemic. The public also sought related information via social media more frequently. However, little is known about what kinds of information satisfy them better. This study aimed to explore the characteristics of health information dissemination that affected users' information behavior on WeChat. Methods: Two-wave data were collected from the top 200 WeChat official accounts on the Xigua website. The data included the change in the number of followers and the total number of likes on each account in a 7-day period, as well as the number of each type of article and headlines about coronavirus. It was used to developed regression models and conduct content analysis to figure out information characteristics in quantity and content. Results: For nonmedical institution accounts in the model, report and story types of articles had positive effects on users' following behaviors. The number of headlines on coronavirus positively impacts liking behaviors. For medical institution accounts, report and science types had a positive effect, too. In the content analysis, several common characteristics were identified. Conclusions: Characteristics in terms of the quantity and content in health information dissemination contribute to users' information behavior. In terms of the content in the headlines, via coding and word frequency analysis, organizational structure, multimedia applications, and instructions-the common dimension in different articles-composed the common features in information that impacted users' liking behaviors. |*Disease Outbreaks[MESH] |Betacoronavirus/*isolation & purification[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*epidemiology/virology[MESH] |Emotions[MESH] |Humans[MESH] |Information Dissemination/*methods[MESH] |Language[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/virology[MESH] |SARS-CoV-2[MESH]Effects of Health Information Dissemination on User Follows and Likes (epmc).pdf DeepDyve Pubget Overpricing