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10.1017/ice.2020.406 http://scihub22266oqcxt.onion/10.1017/ice.2020.406 32758315!7450231!32758315     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32758315.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Infect+Control+Hosp+Epidemiol 2021 ; 42 (2): 131-138 Nephropedia Template TP {{tp|p=32758315|t=2021. Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter |pdf=|usr=}}{{32758315}} gab.com Text Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter JO: Infect Control Hosp Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32758315 #FOAvip OBJECTIVE: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. DESIGN: Retrospective cross-sectional study. METHODS: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. RESULTS: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0-0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). CONCLUSIONS: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation. Twit Text FOAVip Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter ????Infect Control Hosp Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=32758315 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32758315 #FOAvip English Wikipedia <ref>{{Cite pmid|32758315}}</ref> Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter #MMPMID32758315 Saleh SN; Lehmann CU; McDonald SA; Basit MA; Medford RJ Infect Control Hosp Epidemiol 2021[Feb]; 42 (2): 131-138 PMID32758315show ga OBJECTIVE: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. DESIGN: Retrospective cross-sectional study. METHODS: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. RESULTS: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0-0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). CONCLUSIONS: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation. |*Physical Distancing[MESH] |*Public Opinion[MESH] |Adaptation, Psychological[MESH] |COVID-19/epidemiology/*prevention & control/*psychology[MESH] |Cross-Sectional Studies[MESH] |Data Collection/methods[MESH] |Emotions[MESH] |Humans[MESH] |Machine Learning[MESH] |Retrospective Studies[MESH]Understanding public perception of coronavirus disease 2019 (COVID-19)(epmc).pdf DeepDyve Pubget Overpricing