University of Illinois Urbana-Champaign

A large-scale study of fashion influencers on Twitter

Chen, Qinglin

Content Files
CHEN-THESIS-2019.pdf

Permalink

https://hdl.handle.net/2142/105102

Description

Title
A large-scale study of fashion influencers on Twitter
Author(s)
Chen, Qinglin
Issue Date
2019-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Kumar, Ranjitha
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2019-08-23T20:36:12Z
Keyword(s)
  • Fashion
  • Influencers
  • Social Networks
  • Twitter
  • Machine Learning
  • Data Mining
  • HCI
Abstract
The rise of social media has changed the nature of the fashion industry. Influence is no longer concentrated in the hands of an elite few: social networks distribute power across a broad set of tastemakers; trends are driven bottom-up and top-down; and designers, retailers, and consumers are regularly inundated with new styles and looks. This thesis presents a large-scale study of fashion influencers on Twitter and proposes a fashion graph visualization dashboard to explore the social interactions between these Twitter accounts. Leveraging a dataset of 11.5k Twitter fashion accounts, a content-based classifier was trained to predict which accounts are fashion-centric. With the classifier, I identified more than 300k fashion-related accounts through a snowball crawling and then defined a stable group of 1000 influencers as the fashion core. I further human-labeled these influencers’ Twitter accounts and mine their recent tweets. Finally, I built a fashion graph visualization dashboard that allows users to visualize the interactions and relationships between individuals, brands, and media influencers.
Graduation Semester
2019-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/105102
Copyright and License Information
© 2019 Qinglin Chen

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