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10.1016/j.jbi.2021.103862 http://scihub22266oqcxt.onion/10.1016/j.jbi.2021.103862 34229062!9044732!34229062     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34229062&cmd=llinks): Failed to open stream: HTTP request failed! 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Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran |pdf=|usr=}}{{34229062}} gab.com Text Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=34229062 #FOAvip It has not been long since a new disease called COVID-19 has hit the international community. Unknown nature of the virus, evidence of its adaptability and survival in new conditions, its widespread prevalence and also lengthy recovery period, along with daily notifications of new infection and fatality statistics, have created a wave of fear and anxiety among the public community and authorities. These factors have led to extreme changes in the social discourse in a rather short period of time. The analysis of this discourse is important to reconcile the society and restore ordinary conditions of mental peace and health. Although much research has been done on the disease since its international pandemic, the sociological analysis of the recent public phenomenon, especially in developing countries, still needs attention. We propose a framework for analyzing social media data and news stories oriented around COVID-19 disease. Our research is based on an extensive Persian data set gathered from different social media networks and news agencies in the period of January 21-April 29, 2020. We use the Latent Dirichlet Allocation (LDA) model and dynamic topic modeling to understand and capture the change of discourse in terms of temporal subjects. We scrutinize the reasons of subject alternations by exploring the related events and adopted practices and policies. The social discourse can highly affect the community morale and polarization. Therefore, we further analyze the polarization in online social media posts, and detect points of concept drift in the stream. Based on the analyzed content, effective guidelines are extracted to shift polarization towards positive. The results show that the proposed framework is able to provide an effective practical approach for cause and effect analysis of the social discourse. Twit Text FOAVip Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=34229062 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34229062 #FOAvip English Wikipedia <ref>{{Cite pmid|34229062}}</ref> Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran #MMPMID34229062 Jafarinejad F; Rahimi M; Mashayekhi H J Biomed Inform 2021[Sep]; 121 (ä): 103862 PMID34229062show ga It has not been long since a new disease called COVID-19 has hit the international community. Unknown nature of the virus, evidence of its adaptability and survival in new conditions, its widespread prevalence and also lengthy recovery period, along with daily notifications of new infection and fatality statistics, have created a wave of fear and anxiety among the public community and authorities. These factors have led to extreme changes in the social discourse in a rather short period of time. The analysis of this discourse is important to reconcile the society and restore ordinary conditions of mental peace and health. Although much research has been done on the disease since its international pandemic, the sociological analysis of the recent public phenomenon, especially in developing countries, still needs attention. We propose a framework for analyzing social media data and news stories oriented around COVID-19 disease. Our research is based on an extensive Persian data set gathered from different social media networks and news agencies in the period of January 21-April 29, 2020. We use the Latent Dirichlet Allocation (LDA) model and dynamic topic modeling to understand and capture the change of discourse in terms of temporal subjects. We scrutinize the reasons of subject alternations by exploring the related events and adopted practices and policies. The social discourse can highly affect the community morale and polarization. Therefore, we further analyze the polarization in online social media posts, and detect points of concept drift in the stream. Based on the analyzed content, effective guidelines are extracted to shift polarization towards positive. The results show that the proposed framework is able to provide an effective practical approach for cause and effect analysis of the social discourse. |*COVID-19[MESH] |*Social Media[MESH] |Humans[MESH] |Iran/epidemiology[MESH] |Pandemics[MESH]Tracking and analysis of discourse dynamics and polarity during the ea(epmc).pdf DeepDyve Pubget Overpricing