title: Social networks as a new environment for interdisciplinary studies of human behavior
metadata.name: dc.contributor.author
metadata.value: Ledovaya, Yanina A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Tikhonov, Roman V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bogolyubova, Olga N.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2017-12-14T16:44:58Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2017-12-14T16:44:58Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2017-09
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Ledovaya Y. A., Tikhonov R. V., Bogolyubova O. N. Social networks as a new environment for interdisciplinary studies of human behavior. Vestnik SPbSU. Psychology and Education, 2017, vol. 7, issue 3, pp. 193–210.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu16.2017.301
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/8732
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The paper describes a new approach to collecting individual psychological, behavioral and language
  data from online social networks. Within this approach, personal data (“digital footprints”) are collected
  by means of special programs and web-applications that are embedded in social networks interfaces
  or otherwise connected with them. Usually, users provide additional information by answering
  questions of online surveys embedded in such applications. Psychological variables can be then associated
  with online behavioral data and other available information. The data of thousands of users
  can be not only analyzed with traditional statistical methods, but can also be used to build predictive
  models with machine learning algorithms. Thus, psychological characteristics (personality traits, wellbeing,
  etc.) and demographical data can be predicted based on public user information — wall posts,
  page likes, etc., which is a completely new approach to data collection. Such research projects usually
  involve multidisciplinary teams of psychologists, web developers, computational linguists and data
  scientists. Advantages and limitations of this methodology are discussed, as well as the methods of
  data collection and processing and predictive models building. Key findings of the pioneers of this
  research direction are presented. These are the findings of the British project “Mypersonality.org” and
  the USA-based project “World Well-Being Project”. Both are employing the described methodology
  quite massively. Refs 47. Tables 2.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: Работа выполнена при поддержке гранта Санкт-Петербургского государственного университета, проект 8.38.351.2015 «Стресс, здоровье и психологическое благополучие в социальных сетях:
  кросскультурное исследование».
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. Psychology and Education;Volume 7; Issue 3
metadata.lang: -
metadata.name: dc.subject
metadata.value: social networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Facebook
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: data collection
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: digital footprints
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: psychological traits
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: predictive models
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: computer linguistics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: interdisciplinary approach
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Social networks as a new environment for interdisciplinary studies of human behavior
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 02_Ledovaya.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/8732/1/02_Ledovaya.pdf
resources.format: Adobe PDF
resources.size: 679,03 kB