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)

Geographies of Wikipedia in the UK

After a lot of data cleaning and number crunching, we are able to present the following three maps of the geographies of Wikipedia in the UK using brand new November 2010 data. Looking at the first map (total number of articles in each district), we see some interesting patterns. With a few exceptions, it is rural districts in Scotland, Wales and the North of England that are characterised by the highest density of articles.

What we're likely picking up on is the fact that large districts simply have more potential stuff to write about. If we normalise the map by area we see an entirely different pattern. The map below displays the number of articles per square KM.

We see that most of the large urban conurbations in the UK are covered by a dense layer of articles. Most sparsely populated areas in contrast have a much thinner layer of virtual representation in Wikipedia. There are, however, some notable exceptions. Parts of Cornwall, Somerset and the Isle of Wight all have a denser layer of content than might be expected for such relatively rural parts of the country. On the other hand, one might expect a higher density in the districts surrounding Belfast (in fact almost all of Northern Ireland is characterised by very low levels of content per square KM).

Finally, we can look a the number of articles per person in each district:

Here some more surprising results are visible. All major urban areas have relatively low counts of article per person (with the exception of central London). In contrast, many rural areas (particularly areas containing national parks) have high counts per person.

There are obviously a range of ways to measure the geographies of Wikipedia in the UK. We see that some areas are blanketed by a highly dense layer of virtual content (e.g. central London and many of the UK's other major conurbations). These maps also highlight the fact that some parts of the UK are characterised by a paucity of content irrespective of the ways in which the data are normalised. Northern Ireland in particular stands out in this respect.

We'll attempt to upload similar analyses of other countries in the next few months. In the meantime, however, we would welcome any thoughts on the uneven amount of virtual representation that blankets the UK.



p.s. many thanks to Adham Tamer for his help with the data extraction.

Jedi Knights as a Religious Phenomenon

As a non-religious person, I'm not sure how I feel about the fictional protagonists of my all-time favorite movies being turned into a quasi-religious sect. But that's exactly what's happened with the Jedi Knights of Star Wars. From the worldwide movement in 2001 to list Jedi as one's religious preference to the current debate over the axing of Canada's long-form census, the Jedi have long permeated the boundary between reality and fantasy. With their locations visible in Google Maps, we now have evidence that have they also permeated the boundary between materiality and virtuality [1].
This map comes from a paper we're currently working on about the cyberscapes of religion in Google Maps. Using the absolute number of references to "Jedi" in the Google Maps database as our measurement, we thought that mapping these references in the UK would present an excellent opportunity to blur the boundaries between the sacred and the farcical.

Although the concentration of most geotagged content can be explained by population density, references to Jedi conform to a more unique spatial distribution. The greatest number of references to Jedi exist in and around the cities of Manchester, Nottingham and Birmingham - all fairly large in their own right - rather than in the capital and largest city of London. It's also interesting to note that most of England has some level of references to Jedi, perhaps indicating a general level of interest across the country.

And yet, based on the actual counts from the 2001 census, the three cities with the most virtual references do not represent either the greatest absolute number of Jedi (that would be Leeds with 7,543) or the greatest number of Jedi as a percentage of the total population (Brighton and Hove with 2.6%). While both Nottingham (#12) and Manchester (#19) rank highly with Jedi as a percentage of the total population, Birmingham is ranked just #227 with just 0.6% of the population declaring themselves Jedi.

So what makes them so prevalent by our measure? Are these the locations of new, secret Jedi academies? Is there an important force nexus present somewhere in one of these cities? Any clues or suggestions welcome...

[1] We currently have no evidence that this virtual representation of Jedi within Google Maps has anything to do with the mystical powers of the Force.

UK election cyberscapes

In anticipation of the upcoming election in the UK, we have decided to explore the geographies of election-related references in the British Isles. The map below visualises which of five political parties contain the most references at any particular location in the Google Maps database.

References to UK Political Parties

First, a brief note on method. We searched for the three major political parties (Labour, Conservatives and Liberal Democrats) at each location, as well as two of the parties on the far-right of the political spectrum (UKIP and the BNP) that have made gains in recent years. We also searched for the terms "tories + election" and "lib dems + election" and assigned a dot to either the Conservatives or Liberal Democrats if either one of those terms had the most hits at any location.

The map reveals some interesting online political geographies. The Tories score better than any other party. In fact, 61% of locations possess more references to the Conservatives than any other political party, whereas 33.8% of places have more references to Labour and only 3.4% for the Lib Dems.

The UKIP has a particuarly strong showing in the South West, with multiple points that contain more references to "UKIP" than any other party. The BNP do best in South Wales, West Gloucestershire, West Yorkshire and South Tyneside.

One of the most interesting aspects of the map is the degree to which it diverges from maps of likely voting patterns of constituencies. Some of the differences can likely be explained by the relatively recent boost in the polls to the Liberal Democrats (which hasn't yet had a chance to be reflected in material indexed by Google Maps). The strong showing by the Tories could also perhaps be attributed to a greater degree of online engagement by that party.

Another way of gauging online popularity of political parties before the election is to search for the names of each party leader throughout the country. Here we again chose the leaders of the three main parties, as well as Nick Griffin (BNP) in order to explore whether this method can tell us anything about the popularity of the far-right in different parts of the country. The map below shows these results.

References to UK Political Party Leaders

Here we see that Labour's Gordon Brown outperforms his rivals in almost every part of the country, a fact that likely owes much to his current position as Prime Minister. The only significant anomaly seems to be a large number of references to David Cameron in Oxfordshire. Nick Clegg and the Lib Dems again show poorly in this map, although it will be interesting to see how the online visibility of these figures changes after the election.

References to Nick Griffin unsurprisingly appear in many of the same places in which there was also a great deal of visibility for the BNP. We explore the visibility of far-right parties in some more detail through the following maps, which display total number of references to the BNP and the UKIP (this time not compared to any of the other political parties).

References to the British National Party

References to the UK Independence Party

These maps seem to indicate that there is not always a greater total number of references to the BNP or UKIP in places in which they scored highly in the first two maps. In some places, such as West Gloucestershire, it could simply be that there are fewer online references to any of the mainstream political parties.

Are these maps predictors of election results and likely voting patters? We doubt it, but it is nonetheless interesting to observe the very unique geographies occupied on the Internet by different segments of the political spectrum. We will, however, claim any credit for correctly predicting an election result of 61% Tories, 33% Labour and 3% Lib Dems.