by Ate Poorthuis , Dominic Power and Matthew Zook
For more detailed information see: Poorthuis, A., Power, D. and M. Zook (2019). Attentional Social Media: Mapping the Spaces and Networks of the Fashion Industry. Annals of the American Association of Geographers .
The key inspiration for this project is that one of fashion’s most fundamental inputs is attention. Attention to trends. Attention to fads. Attention to brands. Of course, producing, shipping and selling clothing, accessories and cosmetics, is also key, but if a designer or firm doesn’t capture consumers’ attention, they are toast.
Put a slightly more academic way, fashion fundamentally is an economy of attention.
And since we’re Geographers, we want to know what the geography of fashion attention looks like. Attention can be a hard thing to measure and so we use a novel indicator – mentions in social media, in this case Twitter – to map the spatial manifestation of attention to fashion (see the FAQ below for more details). Hence, our paper and the full set of maps here provide a detailed overview of the global, but uneven, geography of attention to fashion across designers, companies and national origin.
There are over 400 different maps (based on different fashion keywords) to explore. To make this a little easier, we created an interactive version where you can choose and compare different keywords. For instance, you can compare where Italian fashion garners attention versus where French fashion gets noticed. Or see differences between where people discuss the creatives behind the business is different to where the individual firms like Louis Vuitton or Gucci attract attention.
Autocomplete is on so try typing a company’s or designer’s name to see your choices. Countries are another good choice. It will be most interesting to compare counts (overall size) and odds ratio (relative intensivity) for the same keyword.
EDUCATIONAL ALERT: If you want to understand what these maps are actually showing please read the sections below. If not, have fun exploring.
For example, you could start out with looking at the aggregated total of all attention to fashion before narrowing down on attention to fashion from a particular designer or country.
Map 2 represents where tweets about Italian fashion are coming from. Italian fashion attracts attention in the entire eastern half and west coast of the United States and Canada, western Europe, Turkey and the eastern Mediterranean, the urban areas of the Gulf states, and many locations within East and Southeast Asia as well as Australia. In short, attention to the Italian fashion industry is both widespread and large. Perhaps unsurprisingly given the premium price points for much of the fashion exported by Italy, attention is concentrated in higher income urban areas.
Of course, because this is social media, tweets are coming from where people are located and so population density is driving a lot of the patterns seen in these maps. Note: not every place uses the same social media platforms and so lack of attention on Twitter does not necessarily mean lack of fashion awareness (See section on China below).
To augment the population density driven maps of raw counts we also include a measure of the relative intensity in places (an Odds Ratio). The odds ratio helps control for population and locations with higher scores contain much more attention to fashion than would be expected given their overall volume of tweets.
Using odds ratios (Map 3) rather than counts (Map 2) for Italian fashion, provides a different understanding of these networks of attention. The high volumes in much of western Europe drop off when normalized by total amount of Twitter activity, although a localized pattern of relatively intense attention remains centered around Milan, the center of the Italian fashion industry. In the United States, the high volumes likewise do not stand up to normalization with much of this region exhibiting less attention relative to other things. In contrast, some places – west and southern Africa, northern India, etc. – are characterized by relatively more intense interest. Thus, these locations represent a particularly specialized knowledge within its Twitter user base, namely an interest in Italian fashion.
This project provides evidence for the key geographic patterns within the fashion industry that are often talked about but seldom evidentially proven. In particular, the data strongly supports the widely held idea of four global fashion capitals (London, Milan, New York, Paris). In terms of volume of social media output, the diversity of attention paid, and the mix of industry specific as well as consumer oriented and consumer side traffic these four cities stand out from the rest of the world as especially important locales.
Moreover, when analyzed with more nuance for industry sector and function we see that geographies of attention vary greatly: the attentional geography for the business sides of fashion are very different to the marketing and consumer oriented sides. Equally geographies of attention vary widely across nationality and firm. Such global analysis points to the networked dimensions of fashion knowledge/attention and how physically distant places and people are differentially connected.
It is particularly noteworthy to show how regions – such as sub-Saharan Africa, Indonesia or India - that are generally both unrecognized by the fashion industry and only very small markets are nevertheless connected to the industry in terms of attention. These types of attention may not correlate to market size, consumption of products or industry locations but to the ability of fashion to insert itself into and be appropriated into global as well as highly local aspirational and taste-making discourses and world-building. Why not use the tool below to explore in greater detail our data? The tool below allows you to use our search terms and data to make your own visualizations and see what grabs your attention.
Attention can be a hard thing to measure and so we use a novel indicator – mentions in social media, in this case Twitter – to map the spatial manifestation of attention to fashion. Just as one might measure electrical consumption to gauge the size of the aluminum industry or count shipping containers to measure trade, mentions of fashion on Twitter provide a useful indicator of the attentional dimension of the fashion industry. And metrics for fashion attention have hitherto been extremely difficult, if not impossible, to do.
We drew from a corpus of all geotagged tweets sent from July 2012 to August 2016 as archived by the DOLLY database in the Department of Geography at the University of Kentucky . In order to built a set of search terms that is representative of the global fashion industry we relied upon data at the Business of Fashion , a prominent and respected independent fashion website specializing in the industrial and business side of the industry. Since 2013 BoF have produced a Global 500 list of individuals that represent the industry. Drawing from three years plus the “Hall of Fame” of this list, we generated keyword search terms for each individual including their names but also the principal firm and brand names they work with or have worked with. An important step (discussed in detail in the paper) was filtering out search terms that generated results that were primarily unrelated to fashion: e.g. Celine, Chloé, Coach, Colette, Elle, or Zara.
The end result of this process was a set of 882 fashion keywords representing individuals, firms and brands also coded by their country of origin and industry sub-sector. For these keywords we pulled all geotagged tweets in the DOLLY database to create the analysis and maps in our paper. And because we could only include a fraction of these in our journal article, we created this website to allow you to explore the full extent of our data the geography of fashion attention.
The large number of data observations makes analysis and visualization difficult and this paper uses hexagonal binning – aggregating individual tweets to a grid of scale dependent polygons – allowing for efficient data aggregation and analysis. The hexagonal bins do not use national or other official boundaries reflecting that social media use (and the fashion industry itself) does not entirely respect borders, but flows across through different cultures, topics and places.
A related issue of particular importance for global studies is that social media practices differ between countries. Perhaps most visible in the case of China where the state blocks most Western social media services, this also extends to national preferences rooted in culture, custom or history. For example, Twitter is quite popular (and sees high use rates) in Brazil and Indonesia, especially relative to countries such as Germany. Thus, while Twitter is used globally and is the site for active discussions of and attention to fashion, other social media platforms could also be used to similar effect. These alternative sources, such as Instagram or Facebook, come with their own strengths, weaknesses and biases and therefore it is essential that we are cautious in our research questions, analysis and interpretations of the results.
Since our primary purpose has been to address questions about the fashion industry’s global geography, we made a choice to create a more static and easily-read picture of the industry by combining data over the entire period and amalgamating all the search terms and data points over the period. The fashion industry does indeed change rapidly and there are many drop-in and drop-out names in our search terms. Nonetheless the majority of search terms appear year on year and the highest volume search terms appear in all years. This is perhaps unsurprising given that fashion is a large global industry with high barriers to entry and large investment cycles, meaning that entry to the global top takes time and once there people and firms tend to stay at the top (at least for a number of years). This is reflected in the number of household names and large capital firms and brand names that occur each year and dominate the overall twitter traffic studied.
A particularly innovative part of this approach is the ability to highlight networks of attention between places and how these vary by brand and origin. As such, we offer not simply a case study but propose a new strategy for future research on the geography of the global economy using big data sources and methods. Focusing on the geographies of attention help counter the long-standing productionist and supply side bias in economic approaches, it also allows researchers to understand how these processes differentially extends across space, connecting certain cities and sub-locations of cities, while bypassing others. Beyond fashion, this approach is also relevant for analyzing other sectors of the economy where consumer sentiment and interest are crucial factors in product uptake and the global value networks that serve consumption.
We’d suggest that you start with the paper behind this project and refer to the bibliography. It is full of good work. To check out previous work that the three of use have done, see below.