A large-scale study of fashion influencers on Twitter
Chen, Qinglin
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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
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.
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