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10.1177/15404153211020453 http://scihub22266oqcxt.onion/10.1177/15404153211020453 34323101!ä!34323101 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       Hisp+Health+Care+Int 2021 ; 19 (4): 239-245 Nephropedia Template TP {{tp|p=34323101|t=2021. Impact of COVID-19: A Text Mining Analysis of Twitter Data in Spanish Language |pdf=|usr=}}{{34323101}} gab.com Text Impact of COVID-19: A Text Mining Analysis of Twitter Data in Spanish Language JO: Hisp Health Care Int http://www.kidney.de/pget.php?&tw=1&pmid=34323101 #FOAvip BACKGROUND: Latino communities in the United States and Latin America are disproportionately affected by the COVID-19 pandemic. We analyzed information shared on Twitter in Spanish language for insights into the public's communication and information needs about the COVID-19 pandemic. METHODS: We performed a mixed-methods analysis using a text mining approach. We used SAS Text Miner, an algorithmic-driven statistical program to capture 10,000 tweets posted between June 3, 2020, and June 10, 2020. We used the following search terms to capture relevant Twitter messages in Spanish language: "coronavirus," "covid-19," "corona," and the hash tags "#COVID19" and "#Coronavirus." Key text topics were identified and categorized into themes using an emergent content analysis. RESULTS: We identified 12 text topics and six themes: (1) prevention measures, (2) epidemiology/surveillance, (3) economic impact, (4) optimizing nursing workforce, (5) access to reliable information, and (6) call for a response from the local government. Top trending hashtags from our search included #COVID19 (n = 7,098), #Coronavirus (n = 6,394), and #SNTESALUD (n = 2,598). CONCLUSIONS: Spanish-language Tweets related to the COVID-19 pandemic contained information from health departments and labor unions on the surveillance, prevention, and impact of COVID-19. Public health officials should consider increasing their use of Twitter to ensure a wide dissemination of messages about COVID-19 in Spanish outlets. Twit Text FOAVip Impact of COVID-19: A Text Mining Analysis of Twitter Data in Spanish Language ????Hisp Health Care Int http://www.kidney.de/pget.php?&tw=1&pmid=34323101 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34323101 #FOAvip English Wikipedia <ref>{{Cite pmid|34323101}}</ref> Impact of COVID-19: A Text Mining Analysis of Twitter Data in Spanish Language #MMPMID34323101 Osakwe ZT; Cortes YI Hisp Health Care Int 2021[Dec]; 19 (4): 239-245 PMID34323101show ga BACKGROUND: Latino communities in the United States and Latin America are disproportionately affected by the COVID-19 pandemic. We analyzed information shared on Twitter in Spanish language for insights into the public's communication and information needs about the COVID-19 pandemic. METHODS: We performed a mixed-methods analysis using a text mining approach. We used SAS Text Miner, an algorithmic-driven statistical program to capture 10,000 tweets posted between June 3, 2020, and June 10, 2020. We used the following search terms to capture relevant Twitter messages in Spanish language: "coronavirus," "covid-19," "corona," and the hash tags "#COVID19" and "#Coronavirus." Key text topics were identified and categorized into themes using an emergent content analysis. RESULTS: We identified 12 text topics and six themes: (1) prevention measures, (2) epidemiology/surveillance, (3) economic impact, (4) optimizing nursing workforce, (5) access to reliable information, and (6) call for a response from the local government. Top trending hashtags from our search included #COVID19 (n = 7,098), #Coronavirus (n = 6,394), and #SNTESALUD (n = 2,598). CONCLUSIONS: Spanish-language Tweets related to the COVID-19 pandemic contained information from health departments and labor unions on the surveillance, prevention, and impact of COVID-19. Public health officials should consider increasing their use of Twitter to ensure a wide dissemination of messages about COVID-19 in Spanish outlets. |*COVID-19[MESH] |*Social Media[MESH] |Data Mining[MESH] |Humans[MESH] |Language[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH]Impact of COVID-19: A Text Mining Analysis of Twitter Data in Spanish (epmc).pdf DeepDyve Pubget Overpricing