FITNet: Identifying Fashion Influencers on Twitter

被引:8
作者
Han J. [1 ]
Chen Q. [1 ]
Jin X. [1 ]
Xu W. [1 ]
Yang W. [1 ]
Kumar S. [1 ]
Zhao L. [1 ]
Sundaram H. [2 ]
Kumar R. [1 ]
机构
[1] University of Illinois at Urbana Champaign, Urbana, IL
[2] University of Missouri, Columbia, MO
关键词
fashion; influencers; twitter;
D O I
10.1145/3449227
中图分类号
学科分类号
摘要
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 have distributed power across a broader set of tastemakers. To understand this new landscape of influence, we created FITNet - - a network of the top 10k influencers of the larger Twitter fashion graph. To construct FITNet, we trained a content-based classifier to identify fashion-relevant Twitter accounts. Leveraging this classifier, we estimated the size of Twitter's fashion subgraph, snowball sampled more than 300k fashion-related accounts based on following relationships, and identified the top 10k influencers in the resulting subgraph. We use FITNet to perform a large-scale analysis of fashion influencers, and demonstrate how the network facilitates discovery, surfacing influencers relevant to specific fashion topics that may be of interest to brands, retailers, and media companies. © 2021 ACM.
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