title: Saint-Petersburg’s political online communities clusters in “VKontakte”
metadata.name: dc.contributor.author
metadata.value: Suslov, Sergey I.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2017-05-17T19:18:27Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2017-05-17T19:18:27Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2016-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Suslov S. I. Saint-Petersburg’s political online communities clusters in “VKontakte”. Vestnik SPbSU. Series 12. Sociology, 2016, issue 4, pp. 69–87.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu12.2016.405
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/6326
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Social media creates the space for the development of online communities that are different in social
  setting and activities. Our ambition is to study the sociopolitical online communities of St. Petersburg.
  We try to explore how online communities structure the practices of political consumption, especially
  in the field of the online social network “VKontakte”. In this part of research, that can be described as
  the first step of the project, we focus on the mapping network of sociopolitical online communities of St.
  Petersburg. Our aim is to make a general picture of the sociopolitical online communities and to analyse
  its structure in terms of clustering. We examine 440 online communities collected via a sample extension
  algorithm. The methodological approach is based on mathematical graphing theory. The model is a set
  of communities and a set of audience adjacency. These intersections are regarded as the edge’s weights. In
  the second model ties are interpreted as the communication flow of reposts between communities. The
  edge’s weights are regarded as the edges merge with the same source and target node. It is interesting that
  the model of audience adjacency has the strong links. When reducing the weak edges we use the polarization
  simulation in echo chambers. In the model, we use the different types of affinity propagation in
  the Chinese Secret concept to divide the graph in more clusters: from 2 up to 15. Refs 38. Figs 4. Table 7.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Series 12. Sociology;Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: big data
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: data mining
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: clustering
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: online communities
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: political communication
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: network analysis
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: audience adjacency
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: social media
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: social networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: echo chambers
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Saint-Petersburg’s political online communities clusters in “VKontakte”
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 05-Suslov.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/6326/1/05-Suslov.pdf
resources.format: Adobe PDF
resources.size: 3,16 MB