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10.2196/26780 http://scihub22266oqcxt.onion/10.2196/26780 33720841!8023378!33720841     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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       JMIR+Public+Health+Surveill 2021 ; 7 (4): e26780 Nephropedia Template TP {{tp|p=33720841|t=2021. Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data |pdf=|usr=}}{{33720841}} gab.com Text Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data JO: JMIR Public Health Surveill http://www.kidney.de/pget.php?&tw=1&pmid=33720841 #FOAvip BACKGROUND: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. OBJECTIVE: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. METHODS: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. RESULTS: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. CONCLUSIONS: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics. Twit Text FOAVip Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data ????JMIR Public Health Surveill http://www.kidney.de/pget.php?&tw=1&pmid=33720841 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33720841 #FOAvip English Wikipedia <ref>{{Cite pmid|33720841}}</ref> Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data #MMPMID33720841 Al-Ramahi M; Elnoshokaty A; El-Gayar O; Nasralah T; Wahbeh A JMIR Public Health Surveill 2021[Apr]; 7 (4): e26780 PMID33720841show ga BACKGROUND: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. OBJECTIVE: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. METHODS: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. RESULTS: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. CONCLUSIONS: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics. |*Masks[MESH] |*Public Opinion[MESH] |COVID-19/*epidemiology/*prevention & control[MESH] |Data Mining[MESH] |Humans[MESH] |Machine Learning[MESH] |Social Media/*statistics & numerical data[MESH]Public Discourse Against Masks in the COVID-19 Era: Infodemiology Stud(epmc).pdf DeepDyve Pubget Overpricing