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10.3390/ijerph17197032 http://scihub22266oqcxt.onion/10.3390/ijerph17197032 32993005!7579565!32993005     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2020 ; 17 (19): ä Nephropedia Template TP {{tp|p=32993005|t=2020. Exploring U S Shifts in Anti-Asian Sentiment with the Emergence of COVID-19 |pdf=|usr=}}{{32993005}} gab.com Text Exploring U S Shifts in Anti-Asian Sentiment with the Emergence of COVID-19 JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32993005 #FOAvip Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment. Twit Text FOAVip Exploring U S Shifts in Anti-Asian Sentiment with the Emergence of COVID-19 ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32993005 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32993005 #FOAvip English Wikipedia <ref>{{Cite pmid|32993005}}</ref> Exploring U S Shifts in Anti-Asian Sentiment with the Emergence of COVID-19 #MMPMID32993005 Nguyen TT; Criss S; Dwivedi P; Huang D; Keralis J; Hsu E; Phan L; Nguyen LH; Yardi I; Glymour MM; Allen AM; Chae DH; Gee GC; Nguyen QC Int J Environ Res Public Health 2020[Sep]; 17 (19): ä PMID32993005show ga Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment. |*Health Knowledge, Attitudes, Practice[MESH] |*Social Media[MESH] |Asian People[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*psychology[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*psychology[MESH] |Racism/*statistics & numerical data[MESH] |SARS-CoV-2[MESH] |Supervised Machine Learning[MESH] |Support Vector Machine[MESH]Exploring U S Shifts in Anti-Asian Sentiment with the Emergence of CO(epmc).pdf DeepDyve Pubget Overpricing