Abstract
In this article we use big data methods to analyze the attention paid to the fashion industry on social media. The article argues that for the fashion industry, like many industries, the core product is a form of knowledge that is dependent on gaining and holding people’s attention. To understand this attentional economy, social media offers a unique window because it is increasingly a central space within which fashion knowledge is created and shared. Using long-term, geotagged big data from Twitter, we analyze the hitherto difficult-to-explore spaces and places of the global fashion industry. The article suggests that the data confirm the ideas that there are a series of global fashion capitals that are especially important to the industry and that attention paid to fashion is highly uneven and varied across industry functions, national origins, and companies. Evidence is presented that attention to fashion is a global phenomenon that does not always directly link to where fashion products are sold. Attention to fashion is both a market-making mechanism for the industry as well as an indicator of wider social and cultural processes of tastemaking and identity formation within which fashion is entwined. The article concludes by suggesting that such data offer geographers new ways of looking at and linking economic, social, and cultural spaces and geographies and that social media analysis can help bridge boundaries that divide geographers.
Document Type
Article
Publication Date
2020
Digital Object Identifier (DOI)
https://doi.org/10.1080/24694452.2019.1664887
Repository Citation
Poorthuis, Ate; Powers, Dominic; and Zook, Matthew, "Attentional Social Media: Mapping the Spaces and Networks of the Fashion Industry" (2020). Geography Faculty Publications. 22.
https://uknowledge.uky.edu/geography_facpub/22
Notes/Citation Information
Published in Annals of the American Association of Geographers, v. 110, issue 4.
© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.