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Social Media + Elections: A Recap

OII - GE2015 - Candidate Activity on Twitter - Bright, Hale - web

From Jonathan Bright and Scott Hale’s blog post on Twitter Use.

In the run-up to the general election we conducted a number of investigations into relative candidate and party use of social media and other online platforms. The site elections.oii.ox.ac.uk has served as our hub for elections-related data analysis. There is much to look over, but this blog post can guide you through.

Twitter

What if mentions were votes?” by Jonathan Bright and Scott Hale

Which parties are having the most impact on Twitter?” by Jonathan Bright and Scott Hale

The (Local) General Election on Twitter” by Jonathan Bright

Where do people mention candidates on Twitter?” by Jonathan Bright

Twitter + Wikipedia 

Online presence of the General Election Candidates: Labour Wins Twitter while Tories take Wikipedia” by Taha Yasseri

Wikipedia 

Which parties were most read on Wikipedia?” by Jonathan Bright

Does anyone read Wikipedia around election time?” by Taha Yasseri

Google Trends

What does it mean to win a debate anyway?: Media Coverage of the Leaders’ Debates vs. Google Search Trends” by Eve Ahearn

Social Media Overall

Could social media be used to forecast political movements?” by Jonathan Bright

Social Media are not just for elections” by Helen Margetts

The (Local) General Election on Twitter

The UK’s national election is decided on a constituency basis: 650 odd separate small elections, each returning one MP. Despite the obvious importance of national parties and their leaders for shaping the election campaign as a whole, it is commonly accepted that the ability of local campaigns is also a significant factor. For example, the Liberal Democrats are well known for having highly organised local activities in their home constituencies; something which might help them hold onto seats despite their overall poor polling in national polls. Given this, for those interested in the influence of social media on the election, it’s worth looking not just at nationally relevant hashtags and Twitter accounts, but how local candidates have been using social media.

In a previous post Taha Yasseri and Stefano de Sabbata looked at the distribution of candidate accounts on Twitter, based on data from YourNextMP. In this post, using the same YNMP data as well as tweets collected by Scott Hale from the Twitter Streaming API over the last month, we look at the actual tweeting activity of MPs. The map below shows the level of activity of each MP in each British constituency for six UK parties in the month leading up to the election. The scale shows light, medium and heavy users of Twitter

OII - GE2015 - Candidate Activity on Twitter - Bright, Hale - web

Level of candidate activity on Twitter

Almost 450,000 tweets were sent by candidates of these six parties in the month leading up to the general election (the Labour party sent over 120,000, the Conservatives and the Green Party sent around 80,000 each, the Liberal Democrats just over 70,000, UKIP just over 60,000 and the SNP just over 15,000).

Compared to the map which Taha and Stefano produced on account distribution, in this new one regional patterns are clearly more apparent: whereas the major parties have candidates with Twitter accounts almost uniformly across the UK, their level of usage varies a lot. Only the SNP are uniform heavy users of Twitter: only two of their candidates sent less than 10 tweets in this period, and the majority sent more than 100. This also chimes with the fact that they are the party who has, relative to the overall number of candidates, created the most Twitter accounts – clearly they have a very active and organised social media presence.

OII - GE2015 - Tweet Histograms - Bright, Hale

Candidate activity on Twitter by party

The histogram above show more detail about the level of twitter activity and how it breaks down between different parties. Conservative, Green and Labour have broadly similar patterns, with the average candidate having sent around 100 tweets in the last month, whilst a few have sent several thousand. UKIP and the Liberal Democrats show a flatter distribution.

Of course, it’s one thing to tweet, but is anyone else actually listening? More on that soon…

Which parties are having the most impact on Twitter?

The previous two posts have shown that the amount of effort parties are putting in on Twitter at the local level is pretty variable. But what about the response they are getting? In this post we’ll look at the amount of mentions candidates receive on Twitter. A mention could be a retweet or it could be a message @ someone – any time the candidate’s name is in there. Data was harvested from Datasift, using the same YourNextMP data for the list of candidate Twitter handles.

In the week before the election candidates were mentioned over one million times. Lots of that activity, it goes without saying, goes to the party leaders: Ed Miliband accounts for almost 120,000 of those mentions alone, with, Cameron, Farage, Clegg and Bennett in places 2 – 5. Yet there was also a lot of activity for less nationally famous figures: over the 2,312 candidates in the YourNextMP dataset, only 12 weren’t mentioned even once during that week (and none of them tweeted either).

Why do some candidates get more attention than others? The most obvious explanation is that some candidates tweet more than others: and being active on social media ought to be a way of getting noticed. The image below plots all of the candidates in the dataset as a point, comparing the number of times they tweeted with the number of times they are mentioned, on a logarithmic scale. The positive relationship is clear.

TwitterMentions-scatter

Twitter mentions of local party candidates

However within all the points, there also seem to be some differences between the parties.  The figure below makes the different clearer by grouping all the candidates into a per party average. What it shows is that, while for every party writing more tweets tends to get more mentions, some parties have a much better “Tweet to mention” ratio than others. In other words, their tweets have on average more impact, and their presence is on average greater. Like the previous one, this graph is on a log scale, meaning that the differences between parties are in orders of magnitude. So, for example, 100 tweets from a Lib Dem candidate would give around 100 mentions; but the same amount from an SNP candidate would give over 1,000 mentions.

TwitterMentions-line

Twitter mentions of local party candidates – averaged by party

Broadly speaking, we can see the parties form three groups on social media in terms of outreach: the SNP are clearly in front, Labour and Conservatives are in the middle, and the Greens and Liberal Democrats at the back. UKIP are somewhere in between the middle and back groups. Interestingly, these relationships hold more or less regardless of the amount of tweets sent by the candidate (and the most famous candidates were by no means the most avid tweeters – Miliband for example only authored 20 tweets in this period, whilst others authored several hundred).

Summary? Some parties have a lot more impact on social media than others.

NB: Post was updated slightly @ 19.45 to correct a data collection issue – overall conclusions weren’t change.

What if mentions were votes?

The last post looked at mention activity for each British constituency. What would happen if we took these mentions to be votes? Does this reaction from social media offer any potential insight into what might happen in the election? In the image below (top),  using the same week of Twitter data from Datasift and YourNextMP, we identify which party “won” the Twitter mention battle in each constituency. The blank constituency on the map is Buckingham (the speaker’s constituency), and we have of course excluded Northern Ireland and Plaid Cymru entirely, which was done purely to limit the number of parties and hence make the job a bit more feasible in real time.

Of course, as we highlight in the previous post, there is a strong relationship between the amount of times a candidate tweeted and the amount of mentions they got: and we don’t want to just measure how much effort candidates have been putting in online, but the relative level of attention they generate. Hence in the map we divide the overall number of mentions of a candidate by the amount of tweets they published themselves, giving us a kind of relative measure of a candidate’s impact on Twitter.

The map below ours is a constituency level forecast based on polling data for the purposes of comparison, lifted straight from our colleagues at electionforecast.co.uk.

Twitter-election

Constituency level Twitter winners

electionforecast.co.uk

Constituency level prediction from http://www.electionforecast.co.uk/

As you can see the number of seats “won” in the Twitter vote diverges significantly from the electionforecast.co.uk model (which is, of course, much closer to what is actually going to happen), but is nevertheless not entirely unrealistic. Labour are understated to a large degree, whilst the reverse is true for UKIP and the Green Party. Labour, Liberal Democrats and SNP are somewhere within the ball park (+/- 30).

Of course, we didn’t really expect this type of method to offer a perfect “prediction” of the election: it would be a major surprise (and probably a coincidence) if it did. My guess is it indicates more something about the loyal / activist base present in a constituency than voter levels. Hence it will be interesting to see if the seats given to some of the more minor parties using this method are areas where these parties do surprisingly well or beat the national trend. For example, are the 35 Green Party constituencies we highlight places where the Greens manage to make a major improvement on their vote share?

Which parties were most read on Wikipedia?

Taha and Stefano previously looked at the distribution of Wikipedia pages by candidate. These pages are much more patchy than Twitter handles: only in the Conservative and Labour cases do more than 40% of candidates have an account, whilst most other parties have far less (though we should note that we are relying on the data crowdsourced by YourNextMP, which is brilliant but not guaranteed to be perfectly accurate). This could be a mistake: the 520 candidates who did have a Wikipedia page together garnered 1.6 million views in the week before the election. Could the candidates who didn’t make have missed a trick? Again, the party leaders account for a lot of the traffic: David Cameron and Ed Miliband contribute around 400,000 of those views alone. But many other pages attracted several thousand views, which in the context of a closely contested election in constituencies of around 70,000 in size, could be quite significant. The distributions of page views by party are shown below.

Wikipedia-Bar

How do Wikipedia views compare to activity on Twitter? They are uncannily similar: they are highly correlated, and at around the same levels: on average, candidates which got 1,000 Twitter mentions got 1,000 Wikipedia views. Perhaps a surprise – considering the very different mechanisms which generate the data.

TwitterMentions-vsWikipedia

The question is of course: do these Wikipedia views make any difference to the local battles? Once we have the full results we can find out…

Where do people mention candidates on Twitter?

In previous posts we’ve looked at people mentioning local party candidates on Twitter. In that post we basically assumed that people mentioning local candidates were based in the same constituency as the candidate themselves. But is that the case? It could be that the majority of tweets are coming from large cities, especially London, where the majority of the party machines are typically based.

Candidate Mention Locations

Candidate mention locations on Twitter in the month leading up to the UK General Election 2015

To provide a rough check of this, we looked at all mentions of candidates on Twitter during the last month which had geolocation enabled (usually because they are tweeted through a smartphone). Geolocated tweets are a fraction of the overall tweets produced (less than 5%); nevertheless, they provide a rough and ready way of checking that all of our candidate tweets are not from one place.

In short, candidate mentions are pretty evenly spread through the country (albeit based on a relatively small amount of data): there is no sense they are concentrated in one part of the country.

Could social media forecast political movements?

GE2015 turned out to be a bad night for some. Beyond the obvious political parties, the reputation of polling firms took a big hit: while the exit poll got more or less in the ball park, none of the pre-election polls were anywhere near. This, combined with the advance of the SNP, UKIP and Greens, lent the whole election a real “earthquake” feel, with people like David Dimbleby questioning whether politicians would ever take polling seriously again.

Considering the weaknesses of conventional polling, could social media have filled a gap in terms of forecasting the earthquake that was to come? Were people on Twitter in advance of the opinion polls?

The data we produced last night produces a mixed picture. We were able to show that the Liberal Democrats were much weaker than the Tories and Labour on Twitter, whilst the SNP were much stronger; we also showed more Wikipedia interest for the Tories than Labour, both things which chime with the overall results. But a simple summing of mention counts per constituency produces a highly inaccurate picture, to say the least (reproduced below): generally understating large parties and overstating small ones. And it’s certainly striking that the clearly greater levels of effort Labour were putting into Twitter did not translate into electoral success: a warning for campaigns which focus solely on the “online” element.

Twitter-election

In terms of prediction the problem here, of course, is that there are many potential statistics which could be produced by social media, and many potential metrics to predict (from vote shares, to swings, to turnouts etc.). Some of them are bound to be “right” after the fact. In response to this, Taha Yasseri and I have recently written a draft paper trying to produce social election predictions more systematically using Wikipedia data. The main premise is that we need a theory informed model to drive social media predictions, which is based on an understanding of how the data is generated and hence enables us to correct for certain biases.

How could we apply this reasoning to our Twitter data? Well one of the suggestions we made last night was that, even though we were sure the Green Party wasn’t going to win the 46 constituencies shown on our Twitter map, perhaps these areas were nevertheless places where the Green vote was going to spike upwards disproportionately (they might, for instance, indicate a highly organised local party machine which would be capable of delivering extra votes). In order to check this, I took results data for the Green Party and UKIP from 50 constituencies in England and Wales (good data tables for the election results still haven’t been released – so I’m limited to the amount I could quickly collect by hand). The graph below plots the amount of percentage points each party’s results increased by against the amount of Twitter mentions candidates received in the run up to the election in each constituency.

Percentage point vote increase vs Twitter Mentions

Overall on the graph there is little apparent correlation for UKIP candidates; Green Party candidates show by contrast a rough though by no means perfect positive correlation. In other words, for the Green Party the Twitter mentions have a little predictive power, whereas for UKIP they have none at all. What is more striking is that the points on the graph group clearly into two sections: UKIP increasing more than their mentions would suggest, whilst the reverse is true for the Greens. This highlights one of the major difficulties in making predictions from social media: that voters of different parties make different uses of social media, and a predictive model would need to take these differences into account.

Once the results are announced in full, over the next few weeks we will be looking into this in more detail, for all parties, and across a wider range of metrics.