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10.1007/s13278-021-00779-3 http://scihub22266oqcxt.onion/10.1007/s13278-021-00779-3 34457082!8379609!34457082     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       Soc+Netw+Anal+Min 2021 ; 11 (1): 78 Nephropedia Template TP {{tp|p=34457082|t=2021. Echo chamber detection and analysis: A topology- and content-based approach in the COVID-19 scenario |pdf=|usr=}}{{34457082}} gab.com Text Echo chamber detection and analysis: A topology- and content-based approach in the COVID-19 scenario JO: Soc Netw Anal Min http://www.kidney.de/pget.php?&tw=1&pmid=34457082 #FOAvip Social media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice," and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020. Twit Text FOAVip Echo chamber detection and analysis: A topology- and content-based approach in the COVID-19 scenario ????Soc Netw Anal Min http://www.kidney.de/pget.php?&tw=1&pmid=34457082 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34457082 #FOAvip English Wikipedia <ref>{{Cite pmid|34457082}}</ref> Echo chamber detection and analysis: A topology- and content-based approach in the COVID-19 scenario #MMPMID34457082 Villa G; Pasi G; Viviani M Soc Netw Anal Min 2021[]; 11 (1): 78 PMID34457082show ga Social media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice," and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020. äEcho chamber detection and analysis: A topology- and content-based app(epmc).pdf DeepDyve Pubget Overpricing