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10.1007/s11069-020-04497-5 http://scihub22266oqcxt.onion/10.1007/s11069-020-04497-5 33469243!7809642!33469243     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       Nat+Hazards+(Dordr) 2021 ; 107 (3): 2319-2336 Nephropedia Template TP {{tp|p=33469243|t=2021. An empirical study on Twitter s use and crisis retweeting dynamics amid Covid-19 |pdf=|usr=}}{{33469243}} gab.com Text An empirical study on Twitter s use and crisis retweeting dynamics amid Covid-19 JO: Nat Hazards (Dordr) http://www.kidney.de/pget.php?&tw=1&pmid=33469243 #FOAvip This study conducts an analysis on topics of the most diffused tweets and retweeting dynamics of crisis information amid Covid-19 to provide insights into how Twitter is used by the public and how crisis information is diffused on Twitter amid this pandemic. Results show that Twitter is first and foremost used as a news seeking and sharing platform with more than 70% of the most diffused tweets being related to news and comments on crisis updates. As for the retweeting dynamics, our results show an almost immediate response from Twitter users, with some first retweets occurring as quickly as within 2 s and the vast majority (90%) of them done within 10 min. Nearly 86% of the retweeting processes could have 75% of their retweets finished within 24 h, indicating a 1-day information value of tweets. Distribution of retweeting behaviors could be modeled by Power law, Weibull, and Log normal in this study, but still there are 20% original tweets whose retweeting distributions left unexplained. Results of retweeting community analysis show that following retweeters contribute to nearly 50% of the retweets. In addition, the retweeting contribution of verified Twitter users is significantly (P < 0.05) different from that of unverified users. A similar significant (P < 0.05) difference is also found in their rates of verified retweeters, and it has been shown that verified Twitter users enjoy seven times as high value as that of unverified users. In other words, users with the same verification status are more likely to get together to diffuse crisis information. Twit Text FOAVip An empirical study on Twitter s use and crisis retweeting dynamics amid Covid-19 ????Nat Hazards (Dordr) http://www.kidney.de/pget.php?&tw=1&pmid=33469243 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33469243 #FOAvip English Wikipedia <ref>{{Cite pmid|33469243}}</ref> An empirical study on Twitter s use and crisis retweeting dynamics amid Covid-19 #MMPMID33469243 Wang B; Liu B; Zhang Q Nat Hazards (Dordr) 2021[]; 107 (3): 2319-2336 PMID33469243show ga This study conducts an analysis on topics of the most diffused tweets and retweeting dynamics of crisis information amid Covid-19 to provide insights into how Twitter is used by the public and how crisis information is diffused on Twitter amid this pandemic. Results show that Twitter is first and foremost used as a news seeking and sharing platform with more than 70% of the most diffused tweets being related to news and comments on crisis updates. As for the retweeting dynamics, our results show an almost immediate response from Twitter users, with some first retweets occurring as quickly as within 2 s and the vast majority (90%) of them done within 10 min. Nearly 86% of the retweeting processes could have 75% of their retweets finished within 24 h, indicating a 1-day information value of tweets. Distribution of retweeting behaviors could be modeled by Power law, Weibull, and Log normal in this study, but still there are 20% original tweets whose retweeting distributions left unexplained. Results of retweeting community analysis show that following retweeters contribute to nearly 50% of the retweets. In addition, the retweeting contribution of verified Twitter users is significantly (P < 0.05) different from that of unverified users. A similar significant (P < 0.05) difference is also found in their rates of verified retweeters, and it has been shown that verified Twitter users enjoy seven times as high value as that of unverified users. In other words, users with the same verification status are more likely to get together to diffuse crisis information. äAn empirical study on Twitter s use and crisis retweeting dynamics ami(epmc).pdf DeepDyve Pubget Overpricing