Social and Political Effects of the Covid-19 Pandemic in Russia: Results of a Study Within the Framework of the "World Internet Project - 2021"

Abstract

The results of the All-Russian survey on the social well-being of citizens in the con-text of the spread of the COVID-19 pandemic are presented. The baseline results were con-clusions about changes in Russians 'offline and online consumer behavior, social expecta-tions, fears and citizens' perception of universal vaccination as a measure to combat the COVID-19 pandemic. The study showed that three types of fears prevail among Russians: coronavirus infection of relatives and friends, their own illness when they do not receive the necessary medical care, and a drop in income (worsening living conditions) amid an uncon-trollably spreading pandemic. The analysis of the survey database showed the activation of digital behavior of citizens in the context of the pandemic, which became a favorable factor in the development of online retail. Against the background of the spread of the COVID-19 vi-rus, the age structure of the Russian national audience is changing, and the digital gap be-tween generations is gradually being bridged. The pandemic triggered the involvement of older people in the digital space. Cluster analysis of the research data made it possible to segment Russians into three groups in relation to the pandemic and measures to overcome it: covid-pessimists, who suffered the most from the restrictions of the corona virus and did not adapt to life under conditions of covid-restrictions; covid-optimists who have successfully adapted to new circumstances in connection with the spread of the COVID-19 virus; covid-realists focused on constructively overcoming the negative consequences of restrictive pandemic measures. Their socio-demographic and behavioral characteristics have been ana-lyzed, and the specificity of the civic position of representatives of various clusters of Rus-sians has been established.

Axiology of Russia’s Image in Mass Media Discourse in Coronavirus Pandemic

“…The results of the All-Russian survey on the social well-being of citizens in the context of the spread of the COVID-19 pandemic, conducted by Tula State University and Financial University under the Government of the Russian Federation, revealed three basic types of fears that prevail among Russians when it comes to this disease: relatives and friends' exposure to coronavirus infection; their own infection along with the lack of necessary medical care; a significant drop in income leading to the worsening of living conditions amid an uncontrollably spreading pandemic. The mentioned fears, in turn, have divided the Russian society into three socio-demographic groups in relation to the pandemic and ways to overcome it: covid-pessimists, who suffered the most from the restrictions of the COVID-19 virus and were not able to adapt to life under the pandemic restrictions; covid-optimists, who showed an impressive level of adaptivity to a set of new circumstances in connection with the spread of the Coronavirus; covid-realists, mainly focused on constructively overcoming the negative consequences of restrictive pandemic measures (Batanina, Brodovskaya, Dombrovskaya and Parma 2021).…”

GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts

“…Only a few studies focus on some green practices, such as waste sorting, and describe them by eliciting data from questionnaires and interviews [11,12]. Meanwhile, the content of online environmental community posts on social media is a valuable source of information about the characteristics and activities of online communities [13,14]. Social networks have a significant impact on changing social practices [15,16].…”

“…Since the manual identification of information in posts published in online communities is a time-consuming and long-term process, it requires the use of the automated analysis of such posts. However, to date, there are very few studies on environmental practices in the context of their risks and opportunities based on big data analytics, for example, deep learning or content analysis methods [13,26].…”