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10.1016/j.ijdrr.2021.102142 http://scihub22266oqcxt.onion/10.1016/j.ijdrr.2021.102142 33643835!7902209!33643835     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Disaster+Risk+Reduct 2021 ; 56 (ä): 102142 Nephropedia Template TP {{tp|p=33643835|t=2021. Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?|pdf=|usr=}}{{33643835}} gab.com Text Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic JO: Int J Disaster Risk Reduct http://www.kidney.de/pget.php?&tw=1&pmid=33643835 #FOAvip The U.S. has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the risk of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown. Twit Text FOAVip Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic ????Int J Disaster Risk Reduct http://www.kidney.de/pget.php?&tw=1&pmid=33643835 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33643835 #FOAvip English Wikipedia <ref>{{Cite pmid|33643835}}</ref> Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic? #MMPMID33643835 Li L; Ma Z; Lee H; Lee S Int J Disaster Risk Reduct 2021[Apr]; 56 (ä): 102142 PMID33643835show ga The U.S. has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the risk of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown. äCan social media data be used to evaluate the risk of human interactio(epmc).pdf DeepDyve Pubget Overpricing