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10.1016/j.ijmedinf.2020.104340 http://scihub22266oqcxt.onion/10.1016/j.ijmedinf.2020.104340 33242762!7654388!33242762     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Med+Inform 2021 ; 145 (ä): 104340 Nephropedia Template TP {{tp|p=33242762|t=2021. Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users perceptions |pdf=|usr=}}{{33242762}} gab.com Text Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users perceptions JO: Int J Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33242762 #FOAvip OBJECTIVE: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals' perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users' perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. MATERIALS AND METHODS: We collected 166,005 English tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. RESULTS: We identified 28,555 relevant tweets and estimate that 40.4 % indicate uncertainty about weather's impact, 33.5 % indicate no effect, and 26.1 % indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. DISCUSSION: There is no consensus among the public for weather's potential impact. Earlier months were characterized by tweets that were uncertain of weather's effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza's seasonality, President Trump's comments on weather's effect, and social distancing. CONCLUSION: We exhibit a research approach that is effective in measuring population perceptions and identifying misconceptions, which can inform public health communications. Twit Text FOAVip Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users perceptions ????Int J Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33242762 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33242762 #FOAvip English Wikipedia <ref>{{Cite pmid|33242762}}</ref> Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users perceptions #MMPMID33242762 Gupta M; Bansal A; Jain B; Rochelle J; Oak A; Jalali MS Int J Med Inform 2021[Jan]; 145 (ä): 104340 PMID33242762show ga OBJECTIVE: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals' perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users' perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. MATERIALS AND METHODS: We collected 166,005 English tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. RESULTS: We identified 28,555 relevant tweets and estimate that 40.4 % indicate uncertainty about weather's impact, 33.5 % indicate no effect, and 26.1 % indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. DISCUSSION: There is no consensus among the public for weather's potential impact. Earlier months were characterized by tweets that were uncertain of weather's effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza's seasonality, President Trump's comments on weather's effect, and social distancing. CONCLUSION: We exhibit a research approach that is effective in measuring population perceptions and identifying misconceptions, which can inform public health communications. |*COVID-19[MESH] |*Machine Learning[MESH] |*Social Media[MESH] |*Weather[MESH] |Humans[MESH] |Pandemics[MESH]Whether the weather will help us weather the COVID-19 pandemic: Using (epmc).pdf DeepDyve Pubget Overpricing