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10.1093/ofid/ofaa258 http://scihub22266oqcxt.onion/10.1093/ofid/ofaa258 C7337776!7337776!33117854     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 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       Open+Forum+Infect+Dis 2020 ; 7 (7): ä Nephropedia Template TP {{tp|p=33117854|t=2020. An ?Infodemic?: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak |pdf=|usr=}}{{33117854}} gab.com Text An Infodemic : Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak JO: Open Forum Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33117854 #FOAvip Background: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described. Methods: We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter?s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets. Results: We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention. Conclusions: Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion. Twit Text FOAVip An Infodemic : Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak ????Open Forum Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33117854 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33117854 #FOAvip English Wikipedia <ref>{{Cite pmid|33117854}}</ref> An ?Infodemic?: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak #MMPMID33117854 Medford RJ; Saleh SN; Sumarsono A; Perl TM; Lehmann CU Open Forum Infect Dis 2020[Jul]; 7 (7): ä PMID33117854show ga Background: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described. Methods: We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter?s application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets. Results: We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention. Conclusions: Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion. äAn ?Infodemic?: Leveraging High-Volume Twitter Data to Understand Earl(epmc).pdf DeepDyve Pubget Overpricing