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Quantifying collective identity online from self-defining hashtags.

Alexander T J Barron1, Johan Bollen2,3

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Social media hashtags help form collective identities online. This study quantifies identity strength using a graph model and information theory on Twitter profiles, revealing a complex hierarchy of online self-labeling.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Sociology

Background:

  • Social media platforms facilitate mass communication and influence collective identity.
  • Hashtags serve as crucial social coordinators for movements like #metoo and #blacklivesmatter.

Purpose of the Study:

  • To quantify collective identity formation using hashtags as self-labels on Twitter.
  • To analyze the hierarchical structure and strength of online collective identities.

Main Methods:

  • Utilized a graph model of over 85,000 Twitter user profiles (2017-2019) with overlapping hashtag self-labels.
  • Defined and applied information-theoretic measures: collective identity coherence and conspicuousness.

Main Results:

  • Identified a hierarchy of graph clusters representing collective identities, semantically characterized by key hashtags.
  • Quantified the integration (coherence) and distinctiveness (conspicuousness) of these online identities.

Conclusions:

  • Online collective identities emerge from the hierarchical alignment of uncoordinated individual self-labeling actions.
  • The study reveals a rich landscape of digital identity formation driven by social media usage.