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10.4258/hir.2020.26.4.335 http://scihub22266oqcxt.onion/10.4258/hir.2020.26.4.335 33190468!7674818!33190468     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 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       Healthc+Inform+Res 2020 ; 26 (4): 335-343 Nephropedia Template TP {{tp|p=33190468|t=2020. Determining Public Opinion of the COVID-19 Pandemic in South Korea and Japan: Social Network Mining on Twitter |pdf=|usr=}}{{33190468}} gab.com Text Determining Public Opinion of the COVID-19 Pandemic in South Korea and Japan: Social Network Mining on Twitter JO: Healthc Inform Res http://www.kidney.de/pget.php?&tw=1&pmid=33190468 #FOAvip OBJECTIVES: This study analyzed the perceptions and emotions of Korean and Japanese citizens regarding coronavirus disease 2019 (COVID-19). It examined the frequency of words used in Korean and Japanese tweets regarding COVID-19 and the corresponding changes in their interests. METHODS: This cross-sectional study analyzed Twitter posts (Tweets) from February 1, 2020 to April 30, 2020 to determine public opinion of the COVID-19 pandemic in Korea and Japan. We collected data from Twitter (https://twitter.com/), a major social media platform in Korea and Japan. Python 3.7 Library was used for data collection. Data analysis included KR-WordRank and frequency analyses in Korea and Japan, respectively. Heat diagrams, word clouds, and rank flowcharts were also used. RESULTS: Overall, 1,470,673 and 4,195,457 tweets were collected from Korea and Japan, respectively. The word trend in Korea and Japan was analyzed every 5 days. The word cloud analysis revealed "COVID-19", "Shinchonji", "Mask", "Daegu", and "Travel" as frequently used words in Korea. While in Japan, "COVID-19", "Mask", "Test", "Impact", and "China" were identified as high-frequency words. They were divided into four categories: social distancing, prevention, issue, and emotion for the rank flowcharts. Concerning emotion, "Overcome" and "Support" increased from February in Korea, while "Worry" and "Anxiety" decreased in Japan from April 1. CONCLUSIONS: As a result of the trend, people's interests in the economy were high in both countries, indicating their reservations on the economic downturn. Therefore, focusing policies toward economic stability is essential. Although the interest in prevention increased since April in both countries, the general public's relaxation regarding COVID-19 was also observed. Twit Text FOAVip Determining Public Opinion of the COVID-19 Pandemic in South Korea and Japan: Social Network Mining on Twitter ????Healthc Inform Res http://www.kidney.de/pget.php?&tw=1&pmid=33190468 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33190468 #FOAvip English Wikipedia <ref>{{Cite pmid|33190468}}</ref> Determining Public Opinion of the COVID-19 Pandemic in South Korea and Japan: Social Network Mining on Twitter #MMPMID33190468 Lee H; Noh EB; Choi SH; Zhao B; Nam EW Healthc Inform Res 2020[Oct]; 26 (4): 335-343 PMID33190468show ga OBJECTIVES: This study analyzed the perceptions and emotions of Korean and Japanese citizens regarding coronavirus disease 2019 (COVID-19). It examined the frequency of words used in Korean and Japanese tweets regarding COVID-19 and the corresponding changes in their interests. METHODS: This cross-sectional study analyzed Twitter posts (Tweets) from February 1, 2020 to April 30, 2020 to determine public opinion of the COVID-19 pandemic in Korea and Japan. We collected data from Twitter (https://twitter.com/), a major social media platform in Korea and Japan. Python 3.7 Library was used for data collection. Data analysis included KR-WordRank and frequency analyses in Korea and Japan, respectively. Heat diagrams, word clouds, and rank flowcharts were also used. RESULTS: Overall, 1,470,673 and 4,195,457 tweets were collected from Korea and Japan, respectively. The word trend in Korea and Japan was analyzed every 5 days. The word cloud analysis revealed "COVID-19", "Shinchonji", "Mask", "Daegu", and "Travel" as frequently used words in Korea. While in Japan, "COVID-19", "Mask", "Test", "Impact", and "China" were identified as high-frequency words. They were divided into four categories: social distancing, prevention, issue, and emotion for the rank flowcharts. Concerning emotion, "Overcome" and "Support" increased from February in Korea, while "Worry" and "Anxiety" decreased in Japan from April 1. CONCLUSIONS: As a result of the trend, people's interests in the economy were high in both countries, indicating their reservations on the economic downturn. Therefore, focusing policies toward economic stability is essential. Although the interest in prevention increased since April in both countries, the general public's relaxation regarding COVID-19 was also observed. äDetermining Public Opinion of the COVID-19 Pandemic in South Korea and(epmc).pdf DeepDyve Pubget Overpricing