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10.1371/journal.pone.0247086 http://scihub22266oqcxt.onion/10.1371/journal.pone.0247086 33600477!7891716!33600477     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       PLoS+One 2021 ; 16 (2): e0247086 Nephropedia Template TP {{tp|p=33600477|t=2021. Classification aware neural topic model for COVID-19 disinformation categorisation |pdf=|usr=}}{{33600477}} gab.com Text Classification aware neural topic model for COVID-19 disinformation categorisation JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33600477 #FOAvip The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source. Twit Text FOAVip Classification aware neural topic model for COVID-19 disinformation categorisation ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33600477 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33600477 #FOAvip English Wikipedia <ref>{{Cite pmid|33600477}}</ref> Classification aware neural topic model for COVID-19 disinformation categorisation #MMPMID33600477 Song X; Petrak J; Jiang Y; Singh I; Maynard D; Bontcheva K PLoS One 2021[]; 16 (2): e0247086 PMID33600477show ga The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source. |*COVID-19[MESH] |*Communication[MESH] |*Neural Networks, Computer[MESH] |Classification/*methods[MESH]Classification aware neural topic model for COVID-19 disinformation c(epmc).pdf DeepDyve Pubget Overpricing