Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1002/pra2.233 http://scihub22266oqcxt.onion/10.1002/pra2.233 33173810!7645926!33173810     free     free     free       Proc+Assoc+Inf+Sci+Technol 2020 ; 57 (1): e233 Nephropedia Template TP {{tp|p=33173810|t=2020. Uncovering temporal differences in COVID-19 tweets |pdf=|usr=}}{{33173810}} gab.com Text Uncovering temporal differences in COVID-19 tweets JO: Proc Assoc Inf Sci Technol http://www.kidney.de/pget.php?&tw=1&pmid=33173810 #FOAvip In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people's discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light. Twit Text FOAVip Uncovering temporal differences in COVID-19 tweets ????Proc Assoc Inf Sci Technol http://www.kidney.de/pget.php?&tw=1&pmid=33173810 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33173810 #FOAvip English Wikipedia <ref>{{Cite pmid|33173810}}</ref> Uncovering temporal differences in COVID-19 tweets #MMPMID33173810 Zheng H; Goh DH; Lee CS; Lee EWJ; Theng YL Proc Assoc Inf Sci Technol 2020[]; 57 (1): e233 PMID33173810show ga In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people's discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light. äUncovering temporal differences in COVID-19 tweets (epmc).pdf DeepDyve Pubget Overpricing