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Hashtags and Haggis: Mapping the Scottish Referendum

Reposted from our work over at Floatingsheep

The past weeks have been quite eventful in Scotland as a monumental election unfolds. Everyone wants to know, which way will the Scots vote? While we here at Floatingsheep certainly don’t have the answer or power to predict the referendum, we thought it might be interesting to see the geographic dimension of how Scots (and the rest of the world) are tweeting about a fundamentally geographic decision [1].

We pulled data from DOLLY from the last month and a half for a number of hashtags and terms that we thought might be helpful in taking the pulse of Twitter discussion around the independence referendum. Most obviously, we collected the hashtags #VoteYes and #YesBecause due to their association with the pro-independence movement, and the hashtag #NoThanks because of its association with anti-pro-independence sentiment [2].


We started by comparing the prevalence of ‘no’ (i.e., pro-union) hashtags versus 'yes’ (i.e., pro-independence) hashtags the global level. In the map below, orange indicates a greater prevalence of 'yes’ tweets and purple indicates that there are more 'no’ tweets. Perhaps the most interesting thing here is that we can see the United Kingdom swing towards a 'yes’ vote, which has, for the most part, appeared to be the underdog in more conventional polling leading up to the referendum. Then again, most of Western Europe, along with Thailand and Australia, also have a general preference for 'yes’ tweets. Oddly enough, the United States is the staunchest defender of the union, based solely on it’s massive preference for 'no’ tweets. Strange for a country that yearly celebrates its breaking away from Mother England

Comparing 'Yes’ vs. 'No’ Tweets at the Global Scale

Looking closer at the UK, we can see that much of Scotland has a roughly equal number of tweets in support of both the 'yes’ and 'no’ positions – reflecting the contentious and hotly-contested nature of this referendum. But the Central Belt in particular – where a lot of actual votes will be coming from, as it is the most densely populated part of the nation – swings heavily towards 'yes’. The English, on the other hand, seem very much inclined towards pro-union or anti-separation tweeting.

Comparing 'Yes’ vs. 'No’ Tweets in the United Kingdom

To take an alternative look at support for the different positions, we mapped the percentage of each of the three hashtags that originates in each of the administrative sub-regions of both Scotland and the UK as a whole. The Highlands and parts of the Central Belt again show up as strong bastions of 'yes’ votes.

Percentage of Referendum-Related Tweets from Different Regions

But seeing as we’re interested in doing more than just mapping distributions, the next question is how are we to put all of this into context? The only proper place to start is, of course, with the Queen. The map below illustrates those places which also tend to have higher-than-normal levels of tweeting about the Queen (in orange) and those places that are tweeting less about the Queen than might usually be expected (in purple), based on a baseline measure of tweeting activity. Sadly, the whole country seems to be ignoring her. Apart from Glasgow, that is. In the interests of not upsetting an 88 year-old lady, we have chosen not to explore these tweets in any more detail.

Tweets referencing “Queen”

Building on this, we also explored the geography of references (using the same method described above) to something inherent in most people’s definitions of Britishness: tea and crumpets

We see an all-around tea-depression; hardly anywhere is particularly pro-tea at the moment, truly a shocking state of affairs. The British are clearly not being their usual selves, and for their sake we’re glad the referendum will be over soon, regardless of the outcome. Scotland, in particular, has average tea counts that are low by historical standards.

Tweets referencing “tea and crumpets”

This analysis would, of course, all be meaningless unless we mapped the geographies of a range of uniquely Scottish phenomena: haggis [3], kilts and Nessie. Still using the same method as above, the map below shows without a shadow of a doubt that Scotland is destined to become it’s own nation.

Tweets referencing “haggis”, “kilts” or “Nessie” 

The Scots are tweeting about these topics at a greater-than-usual rate, while their southern neighbors remain distinctly uninterested. If ever there were an indication that these nations are divided by more than just a line on a map, we see that manifested in the topic of people’s Twitter conversations. In short, the Scottish referendum is not just simply about “yes” or “no” but seemingly touches on much more fundamental questions of ovis-based cuisine, men’s wear and mythological creatures.

So even if the 'no’ votes win out in and the Kingdom remains united, the geographies of haggis related tweeting (along with a few other things) has revealed that these are two very different nations, indeed.

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[1] In case you don’t know what Twitter, is we refer you to the Scots Wikipedia page on the subject, which states: “Twitter is an online social networkin service an microbloggin service that enables its uisers tae send an read text-based messages o up tae 140 characters, kent as 'tweets’”.
[2] Perhaps we could have simplified this phrasing, but then we would have lost the chance to type “anti-pro-independence”, which is a lot of fun. Anti-pro-independence. Anti-pro-independence.
[3] Normally the Floatingsheep collective avoids conversation about sheep heart, liver, and lungs that are boiled in a sheep stomach. But we made an exception this time.
Mapping tweets about the Kenyan presidential debate
Yesterday saw the first ever televised presidential debate in Kenya. The debate was not only carried across a range of tv and radio stations, but was also live-streamed on YouTube and was actively discussed on Twitter under the #KeDebate13 and #KeDebate hashtags (and perhaps others).

Because Kenya is one of Africa’s most active sites of Twitter usage, I thought it would be useful to map out the geographies of geocoded tweets about the debate. With a bounding box around the continent, we collected 2321 geocoded tweets mentioning #KeDebate13 and #KeDebate (between the afternoon of the 12th and the morning of the 13th). 

Most of these tweets were unsurprisingly in Kenya. Of those tweets, only Nairobi saw a significant cluster of activity. In the maps below, we map tweets mentioning #KeDebate13 and #KeDebate in Nairobi: revealing some of the urban geographies of participation in the Twitter debate.  

We see that some parts of the city (downtown, Madaraka, Kileleshwa, Kimathi, Royasmbu etc) are clearly central in the online discussion, while others barely show up (e.g. take a look at Kibera). I’ll dive into the data a bit more to see whether we are seeing clusters of friends tweeting in these areas (and look at what they are saying), but in the meantime wanted to share the maps in case anyone has initial insights into these patterns.

See also:
Mapping Twitter in African cities
Obama wins the election! (on Twitter)

Can Twitter predict the outcome of the US election tomorrow? If our results are anything to go by then Barack Obama will be reelected. The data presented below are the result of some research that Adham Tamer, Ning Wang, Scott Hale and I (Mark Graham) carried out in order to see how visible both major presidential candidates are on Twitter.

We collected about 30 million geocoded tweets between Oct 1 and Nov 1 and pulled out all references to Obama and Romney. You can see the initial results in the map below.


We see that if the election were decided purely based on Twitter mentions, then Obama would be re-elected. In fact, the only states that Romney would win are Maine, Massachusetts, New Mexico, Oregon, Pennsylvania, Utah, and Vermont. Romney also wins in the District of Colombia (we unfortunately didn’t collect data on Alaska or Hawaii).

However, this drubbing that Romney receives in the Twitter electoral college belies the close nature of the final popular (Twitter) vote. There are a total of 132,771 tweets mentioning Obama and 120,637 mentioning Romney, giving Obama only 52.4% of the total (and Romney 47.6%). A breakdown that is remarkably similar to current opinion polls.

If you want to explore the data in more detail, please play around with the interactive map below:
We can also map the data using a sliding scale in order to better see how close the margin of victory is in each state.


Romney’s largest margins of victory are in Pennsylvania and Massachusetts. Obama’s largest victories are in California and, strangely, Texas.

It is also worth noting that we compared Twitter mentions of both Vice-Presidential candidates: Biden and Ryan. Ryan, interestingly, wins the head-to-head competition in every single state. This makes for a rather boring map, so I decided to instead compare references to Ryan and Romney in the map below (Romney shaded in grey for his ebullient personality, and Ryan in pink as a result of his staunch support for gay rights).


As might be expected, there are more references to Romney in most states (Kansas, Michigan, North Dakota, Rhode Island, South Dakota, and Vermont being the exceptions here). However, when looking at total references, we again don’t see a large gap between the two men. Ryan has 94,707 tweets compared to Romney's 120,637.

What do these data really tell us? I doubt that they will accurately predict that Obama will win in Texas or that Romney will win in Massachusetts. But they do certainly reveal that many internet users in California, Texas, and much of the country prefer talking about Obama than Romney. We would need to employ sentiment analysis or manually read a large number of the election-related tweets in order to figure out whether we are seeing messages of support or more critical posts.

Some of the results seem to be interesting reflections of social and political characteristics of particular places. It makes sense that Romney has captured more of the public imagination in Utah (perhaps due to the state’s large Mormon population) and Massachusetts (the state that he once governed).

Other results are harder to extract meaning from. Romney's (Twitter) win in Pennsylvania perhaps will also presage interesting results in that state on election day. But, who knows what Obama’s Texas win demonstrate.

Maybe the most revealing aspect of these data is the ‘popular vote’ split between the two candidates. While the social and political data shadows that we are picking up may not accurately tell us much about the electoral college results, when aggregated across the country they may be a rough indicator of outcomes tomorrow.

While this work may seem like a contemporary attempt at soothsaying, the data will also serve as a useful benchmark in order to allow us to see what social media data shadows actually might reflect.