A large-scale study of fashion influencers on Twitter

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Chen, Qinglin

Contributors dc:contributor

• Kumar, Ranjitha

• Fashion
• Influencers
• Social Networks
• Twitter
• Machine Learning
• Data Mining
• HCI

Statement dc:rights

• © 2019 Qinglin Chen

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105102