Premier League teams on Twitter (or why Liverpool wins the league and the Queen might support West Ham)

Have you ever wondered where Premier League football teams draw most of their support from? Or what the geography of fandom is? We have too, and set about to better understand how Premiership teams are reflected in Twitter usage across the UK.

The Floatingsheep team, with the help of two researchers from the Oxford Internet Institute - Joshua Melville and Scott A. Hale (both of whom did most of the work) - have created a neat interactive map for you to both explore the geography of Twitter mentions of specific teams, and let you explore the patterns of five key rivalries. Click on the screenshot below to be brought to the full interactive map. 

The data used include all geotagged tweets mentioning any of the Premiership football teams and their associated hashtags (e.g., #MUFC or #YNWA) that were sent between August 18 and December 19, 2012. We have only included one tweet per user to prevent 'loud' fans from skewing the results. The users were then aggregated to postcode districts in order to see a fairly fine-grained geography of results. The number of tweeters per district is normalized by the total 'population' of Twitter users based on a 0.25% random sample of all tweets within the UK. 

What do the data show us, you ask? In Manchester, for instance, there is the oft-repeated stereotype that Manchester City are the 'real' local team, while Manchester United attract support from further afield. Our map doesn't really support that idea though. There are only a few parts of Greater Manchester in which we see significant more tweets mentioning Manchester City than their local rivals. We also, strangely, see more mentions of Manchester City in Scotland and Merseyside, and more support for Manchester United in Northern Ireland.

The Merseyside rivalry (Liverpool vs. Everton) is another interesting one to map. There we see that Liverpool have the slight edge in the postcode that is home to both team's stadiums. However, there is no clear winner in the rest of the region: with most postcodes having a fairly close split between the two teams. Interestingly, many postcodes in Scotland seem to have more mentions of Everton; while many in Northern Ireland have more mentions of Liverpool.

We can also zoom into particular postcodes and see which teams are most mentioned there. The  academics in Oxford (for some strange reason) mention Manchester City more than any other team. Central Edinburgh (when not focusing on Hearts or Hibs) has more mentions of Everton than any other Club. And the Queen's home of SW1A goes for West Ham.

What about maybe the most important question of all. Who wins the league based on total number of Tweets sent from anywhere in the UK? The answer is Liverpool (a team that hasn't won the actual league since 1990).  Manchester United are a somewhat distant second, joined by Everton and Tottenham in the Champions League spots. We also find out that Fulham, Swansea, and Wigan are the three teams that get relegated due to their quite abysmal scores. Apparently just not that many people want to tweet about Wigan.

There is no doubt that using Tweets as a proxy for fandom is messy and not always reliable. In other words, we are mapping mentions and not measuring sentiment. But, the data do give us a rough sense of who is interested in (or at least talking about what), and where they are doing it from. It allows to begin to counter myths (e.g. that Mancunians don't support Manchester United), develop new insights about places that we don't necessarily have good data about, and most importantly, have some guesses as to which team the Queen might support.

See also:
A broader take on how information augments place (a second paper on the topic can be accessed here)
Other examples of our Twitter mapping (racism, flooding, earthquakes)
The code behind this visualisation (made freely [CC-BY-NC-SA] available on Github)

Multi-lingual cyberscapes: the case of Bangkok, Thailand

For the most part, our maps have been based on the results of keywords in English which has complicated our analysis at the global level (see here and here). Although many English words are used in other languages, it remains an issue. As a result, we have recently begun mapping and comparing the results of searches based in a variety of other languages including those written using characters other than the Latin alphabet.

This post outlines four terms we searched for in Bangkok in both English and Thai.

The first map is similar to other city-scale maps we've published in the past. However, this time we specifically decided to search for references to beer. This map shows results of searches for placemarks containing the word "beer" overlaid on satellite imagery of Bangkok (courtesy of Google Earth). The color and size of each circle indicates the number of results at every given point. Big red circles have the most references and small purple circles have few. Locations without any circles have no references.

A few parts of the city stand out with an abundance of references to beer: the backpacker ghetto of Khao San Rd at the northwestern corner of the map, Patpong in the middle of the map, and the infamous Nana Plaza and Soi Cowboy at the far eastern edge of the map. A cluster of references to beer can also be seen slightly to the north of the main Hua Lamphong train station (the area of white blobs on the map), although we're not really sure why.

"Beer" in English

Interestingly when the same search is conducted using Thai charcters instead of English (เบียร์), a relatively similar pattern is evident. The same areas stand out on the map, although this time Patpong is far more visible. Also evident is the fact that references to beer have a far more dispersed geography in Thai than they do in English. This is likely due to the fact that English speakers are far more likely to reference the few tourist hot-spots in the city, while Thai speakers are more likely to be familiar with a much broader range of places . In any case, we are gratified to see that beer seems to international.

"Beer" in Thai (เบียร์)

This difference in the parts of the city understood and mapped by tourists versus locals can also be observed when a search for "silk" is conducted. Silk is one of famous exports of Thailand and this is reflected in Bangkok's cyberscape. When looking at references to silk in English, we see a map not too dissimilar from the map of beer. Khao San Rd. and Patpong stand out again (likely because silk is often sold in the night markets in both places). The map also picks up references to silk stretching from Patpong down Silom Rd. to the Chao Phraya river and stretching from Nana down Sukhumvit road in both directions (both roads are lined with silk shops that are oriented towards tourists).

"Silk" in English

When the Thai word for silk is used (แพร), a very different pattern can be seen. References to silk are scattered throughout the city without the clear clustering seen in the English (and presumably tourist oriented) cyberscapes of silk. Very different geographies and understandings of place are therefore being constructed between English and Thai representations of the city.

"Silk" in Thai (แพร)

These differences between English and Thai cyberspaces are observable in a whole range of terms. Mapping references to "temple" in English, unsurprisingly highlights the Grand Palace Complex and the many temples in the Phra Nakhon District of the city.

"Temple" in English

A search for the Thai word for temple (วัด) highlights entirely different parts of the city. Instead of the Grand Palace area, we see a focus on the Temple of Dawn and the many other temples on the eastern bank of the river. There are also clusters of placemarks around the Golden Mount, Chinatown and the temples in Sathon (e.g. Wat Yan Nawa). Again these are locations in the city that are more likely to be known and frequented by Thais than foreigners.

"Temple" in Thai (วัด)

Finally, we wanted to look at the geographies of an industry that is far more visible in Bangkok than in most parts of the world: the sex industry. We started with a search for the term "brothel" in English. We see a few dominant clusters showing up on the map: most notably the famous red-light districts of Nana and Patpong.

"Brothel" in English

But when searching for brothel in Thai (ซ่อง), a radically different geography can be seen. The Chong Nonsi area especially stands out (a part of the city not particuarly famous as a red-light district).

"Brothel" in Thai (ซ่อง)

We recognize that ensuring that our terms are equivalent in both languages is problematic and would welcome thoughts on ways to improve our searches. For example, would English speakers use the term brothel or sex club? Or something else?

Nevertheless what this shows, is that very different representations of a city are being produced and reproduced for different people. Our understandings of the cities we move through are heavily influenced by representations of place and we therefore need to find useful ways to map and understand the fluid and sometimes fleeting representations that exist on the internet.