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Assessing Topical Homophily on Twitter

  • Kuntal Dey
  • Ritvik Shrivastava
  • Saroj Kaushik
  • Kritika Garg
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

We perform a first-of-its-kind characterization of topical homophily - familiarity co-occurring with topic-participation similarity of user pairs - by correlating topic participation similarity and degree of familiarity of users on Twitter. We quantify similarity between a user pair by measuring their distribution of participation in topics, wherein topics are defined as clusters of hashtags formed using semantically related user-generated content. We examine the topic participation similarity of users against different degrees of familiarity: edges, shared neighbors, and structural communities. We provide varying relaxation in identifying topics, and characterize the correlation of topical similarity with the degree of familiarity over the range of relaxation. We empirically substantiate the characteristics of topical homophily, over the varying relaxation of identified topics. We empirically show that homophily grows linearly with increase of familiarity, reaches a peak, and subsequently falls, indicating that, familiarity correlates with similarity up to a point, beyond which, similarity occurs for other reasons.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kuntal Dey
    • 1
  • Ritvik Shrivastava
    • 2
  • Saroj Kaushik
    • 3
  • Kritika Garg
    • 4
  1. 1.IBM ResearchNew DelhiIndia
  2. 2.Columbia UniversityNew YorkUSA
  3. 3.Indian Institute of TechnologyDelhiIndia
  4. 4.Ch. Brahm Prakash GECDelhiIndia

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