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10.3390/ijerph17186847 http://scihub22266oqcxt.onion/10.3390/ijerph17186847 32961702!7557581!32961702     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (18): ä Nephropedia Template TP {{tp|p=32961702|t=2020. Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak |pdf=|usr=}}{{32961702}} gab.com Text Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32961702 #FOAvip This study focuses on stigma communication about COVID-19 on Twitter in the early stage of the outbreak, given the lack of information and rapid global expansion of new cases during this period. Guided by the model of stigma communication, we examine four types of message content, namely mark, group labeling, responsibility, and peril, that are instrumental in forming stigma beliefs and sharing stigma messages. We also explore whether the presence of misinformation and conspiracy theories in COVID-19-related tweets is associated with the presence of COVID-19 stigma content. A total of 155,353 unique COVID-19-related tweets posted between December 31, 2019, and March 13, 2020, were identified, from which 7000 tweets were randomly selected for manual coding. Results showed that the peril of COVID-19 was mentioned the most often, followed by mark, responsibility, and group labeling content. Tweets with conspiracy theories were more likely to include group labeling and responsibility information, but less likely to mention COVID-19 peril. Public health agencies should be aware of the unintentional stigmatization of COVID-19 in public health messages and the urgency to engage and educate the public about the facts of COVID-19. Twit Text FOAVip Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32961702 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32961702 #FOAvip English Wikipedia <ref>{{Cite pmid|32961702}}</ref> Constructing and Communicating COVID-19 Stigma on Twitter: A Content Analysis of Tweets during the Early Stage of the COVID-19 Outbreak #MMPMID32961702 Li Y; Twersky S; Ignace K; Zhao M; Purandare R; Bennett-Jones B; Weaver SR Int J Environ Res Public Health 2020[Sep]; 17 (18): ä PMID32961702show ga This study focuses on stigma communication about COVID-19 on Twitter in the early stage of the outbreak, given the lack of information and rapid global expansion of new cases during this period. Guided by the model of stigma communication, we examine four types of message content, namely mark, group labeling, responsibility, and peril, that are instrumental in forming stigma beliefs and sharing stigma messages. We also explore whether the presence of misinformation and conspiracy theories in COVID-19-related tweets is associated with the presence of COVID-19 stigma content. A total of 155,353 unique COVID-19-related tweets posted between December 31, 2019, and March 13, 2020, were identified, from which 7000 tweets were randomly selected for manual coding. Results showed that the peril of COVID-19 was mentioned the most often, followed by mark, responsibility, and group labeling content. Tweets with conspiracy theories were more likely to include group labeling and responsibility information, but less likely to mention COVID-19 peril. Public health agencies should be aware of the unintentional stigmatization of COVID-19 in public health messages and the urgency to engage and educate the public about the facts of COVID-19. |*Betacoronavirus[MESH] |*Social Media[MESH] |*Social Stigma[MESH] |COVID-19[MESH] |Communication[MESH] |Coronavirus Infections/epidemiology/*psychology[MESH] |Disease Outbreaks[MESH] |Health Communication[MESH] |Humans[MESH] |Information Dissemination/*methods[MESH] |Pandemics[MESH] |Pneumonia, Viral/epidemiology/*psychology[MESH] |Public Health/*standards[MESH]Constructing and Communicating COVID-19 Stigma on Twitter: A Content A(epmc).pdf DeepDyve Pubget Overpricing