rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: How to situate big data in social scientific thought
  name:
    name part: Törnberg, Petter
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: enk
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: As our social life is mediated by digital interfaces such as Twitter, TikTok, and Google Maps, these services are at the same time producing large quantities of social data that are transforming the social sciences. Such "Big Data" are giving researchers access to new ways of studying society, bringing the emergence of a rapidly growing discipline: Computational Social Science (CSS). Big Data are however not only bringing social scientists promises of new insights, but also important new challenges. Big Data has proven challenging to situate in existing social scientific theory, leading some to criticize CSS for seeking merely surface-level description. In this guide, we look closer at the nature of Big Data, to examine how we can better situate its study in social scientific thinking. What are we actually studying when we are studying Big Data? How do we link our findings to existing social scientific theory? We will propose a way of answering these questions, to guide aspiring social scientists who are interested in doing digital research. The guide proposes a form of CSS that is critical and pluralist, combining computational methods and Big Data with theory to critically examine how platforms are reshaping social life.
  target audience: specialized
  note: Petter Törnberg.
  subject:
    topic: Information theory in the social sciences
  subject:
    topic: Big data
  subject:
    topic: Théorie de l'information en sciences sociales
  subject:
    topic: Données volumineuses
  subject:
    topic: Big data
  subject:
    topic: Information theory in the social sciences
  classification: H61.95 .T67 2022
  classification: 025.063
  related item:
    title info:
      title: SAGE research methods: doing research online
  identifier: 9781529611281
  identifier: 1529611288
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: SGPBL
    record creation date: 220704
    record change date: 20240923170456.9
    record identifier: in00005496346
    language of cataloging:
      language term: eng