Counts of public Facebook posts mentioning any of the party leaders’ surnames. Data generated by social media can be used to understand political behaviour and institutions on an ongoing basis.[/caption]‘Congratulations to my friend @Messina2012 on his role in the resounding Conservative victory in Britain’ tweeted David Axelrod, campaign advisor to Miliband, to his former colleague Jim Messina, Cameron’s strategy adviser, on May 8th. The former was Obama’s communications director and the latter campaign manager of Obama’s 2012 campaign. Along with other consultants and advisors and large-scale data management platforms from Obama’s hugely successful digital campaigns, Conservative and Labour used an arsenal of social media and digital tools to interact with voters throughout, as did all the parties competing for seats in the 2015 election.

The parties ran very different kinds of digital campaigns. The Conservatives used advanced data science techniques borrowed from the US campaigns to understand how their policy announcements were being received and to target groups of individuals. They spent ten times as much as Labour on Facebook, using ads targeted at Facebook users according to their activities on the platform, geo-location and demographics. This was a top down strategy that involved working out was happening on social media and responding with targeted advertising, particularly for marginal seats. It was supplemented by the mainstream media, such as the Telegraph for example, which contacted its database of readers and subscribers to services such as Telegraph Money, urging them to vote Conservative. As Andrew Cooper tweeted after the election, ‘Big data, micro-targeting and social media campaigns just thrashed “5 million conversations” and “community organizing”’.

He has a point. Labour took a different approach to social media. Widely acknowledged to have the most boots on the real ground, knocking on doors, they took a similar ‘ground war’ approach to social media in local campaigns. Our own analysis at the Oxford Internet Institute shows that of the 450K tweets sent by candidates of the six largest parties in the month leading up to the general election, Labour party candidates sent over 120,000 while the Conservatives sent only 80,000, no more than the Greens and not much more than UKIP. But the greater number of Labour tweets were no more productive in terms of impact (measured in terms of mentions generated: and indeed the final result).

Both parties’ campaigns were tightly controlled. Ostensibly, Labour generated far more bottom-up activity from supporters using social media, through memes like #votecameron out, #milibrand (responding to Miliband’s interview with Russell Brand), and what Miliband himself termed the most unlikely cult of the 21st century in his resignation speech, #milifandom, none of which came directly from Central Office. These produced peaks of activity on Twitter that at some points exceeded even discussion of the election itself on the semi-official #GE2015 used by the parties, as the figure below shows. But the party remained aloof from these conversations, fearful of mainstream media mockery.

The Brand interview was agreed to out of desperation and can have made little difference to the vote (partly because Brand endorsed Miliband only after the deadline for voter registration: young voters suddenly overcome by an enthusiasm for participatory democracy after Brand’s public volte face on the utility of voting will have remained disenfranchised). But engaging with the swathes of young people who spend increasing amounts of their time on social media is a strategy for engagement that all parties ought to consider. YouTubers like PewDiePie have tens of millions of subscribers and billions of video views – their videos may seem unbelievably silly to many, but it is here that a good chunk the next generation of voters are to be found.

Only one of the leaders had a presence on social media that managed anything like the personal touch and universal reach that Obama achieved in 2008 and 2012 based on sustained engagement with social media – Nicola Sturgeon. The SNP’s use of social media, developed in last September’s referendum on Scottish independence had spawned a whole army of digital activists. All SNP candidates started the campaign with a Twitter account. When we look at the 650 local campaigns waged across the country, by far the most productive in the sense of generating mentions was the SNP; 100 tweets from SNP local candidates generating 10 times more mentions (1,000) than 100 tweets from (for example) the Liberal Democrats.

Scottish Labour’s failure to engage with Scottish peoples in this kind of way illustrates how difficult it is to suddenly develop relationships on social media – followers on all platforms are built up over years, not in the short space of a campaign. In strong contrast, advertising on these platforms as the Conservatives did is instantaneous, and based on the data science understanding (through advertising algorithms) of the platform itself. It doesn’t require huge databases of supporters – it doesn’t build up relationships between the party and supporters – indeed, they may remain anonymous to the party. It’s quick, dirty and effective.

The pollsters’ terrible night

So neither of the two largest parties really did anything with social media, or the huge databases of interactions that their platforms will have generated, to generate long-running engagement with the electorate. The campaigns were disconnected from their supporters, from their grass roots.

But the differing use of social media by the parties could lend a clue to why the opinion polls throughout the campaign got it so wrong, underestimating the Conservative lead by an average of five per cent. The social media data that may be gathered from this or any campaign is a valuable source of information about what the parties are doing, how they are being received, and what people are thinking or talking about in this important space – where so many people spend so much of their time. Of course, it is difficult to read from the outside; Andrew Cooper labeled the Conservatives’ campaign of big data to identify undecided voters, and micro-targeting on social media, as ‘silent and invisible’ and it seems to have been so to the polls.

Many voters were undecided until the last minute, or decided not to vote, which is impossible to predict with polls (bar the exit poll) – but possibly observable on social media, such as the spikes in attention to UKIP on Wikipedia towards the end of the campaign, which may have signaled their impressive share of the vote. As Jim Messina put it to msnbc news following up on his May 8^th tweet that UK (and US) polling was ‘completely broken’ – ‘people communicate in different ways now’, arguing that the Miliband campaign had tried to go back to the 1970s.

Surveys – such as polls — give a (hopefully) representative picture of what people think they might do. Social media data provide an (unrepresentative) picture of what people really said or did. Long-running opinion surveys (such as the Ipsos MORI Issues Index) can monitor the hopes and fears of the electorate in between elections, but attention tends to focus on the huge barrage of opinion polls at election time – which are geared entirely at predicting the election result, and which do not contribute to more general understanding of voters. In contrast, social media are a good way to track rapid bursts in mobilization or support, which reflect immediately on social media platforms – and could also be developed to illustrate more long running trends, such as unpopular policies or failing services.

As opinion surveys face more and more challenges, there is surely good reason to supplement them with social media data, which reflect what people are really thinking on an ongoing basis – like, a video in rather than the irregular snapshots taken by polls. As a leading pollster João Francisco Meira, director of Vox Populi in Brazil (which is doing innovative work in using social media data to understand public opinion) put it in conversation with one of the authors in April – ‘we have spent so long trying to hear what people are saying – now they are crying out to be heard, every day’. It is a question of pollsters working out how to listen.

Political big data

Analysts of political behaviour – academics as well as pollsters — need to pay attention to this data. At the OII we gathered large quantities of data from Facebook, Twitter, Wikipedia and YouTube in the lead-up to the election campaign, including mentions of all candidates (as did Demos’s Centre for the Analysis of Social Media). Using this data we will be able, for example, to work out the relationship between local social media campaigns and the parties’ share of the vote, as well as modeling the relationship between social media presence and turnout.

We can already see that the story of the local campaigns varied enormously – while at the start of the campaign some candidates were probably requesting new passwords for their rusty Twitter accounts, some already had an ongoing relationship with their constituents (or potential constituents), which they could build on during the campaign. One of the candidates to take over the Labour party leadership, Chuka Umunna, joined Twitter in April 2009 and now has 100K followers, which will be useful in the forthcoming leadership contest.

Election results inject data into a research field that lacks ‘big data’. Data hungry political scientists will analyse these data in every way imaginable for the next five years. But data in between elections, for example relating to democratic or civic engagement or political mobilization, has traditionally been woefully short in our discipline. Analysis of the social media campaigns in #GE2015 will start to provide a foundation to understand patterns and trends in voting behaviour, particularly when linked to other sources of data, such as the actual constituency-level voting results and even discredited polls — which may yet yield insight, even having failed to achieve their predictive aims. As the OII’s Jonathan Bright and Taha Yasseri have argued, we need ‘a theory-informed model to drive social media predictions, that is based on an understanding of how the data is generated and hence enables us to correct for certain biases’

A political data science

Parties, pollsters and political analysts should all be thinking about these digital disconnects in #GE2015, rather than burying them with their hopes for this election. As I argued in a previous post, let’s use data generated by social media to understand political behaviour and institutions on an ongoing basis. Let’s find a way of incorporating social media analysis into polling models, for example by linking survey datasets to big data of this kind. The more such activity moves beyond the election campaign itself, the more useful social media data will be in tracking the underlying trends and patterns in political behavior.

And for the parties, these kind of ways of understanding and interacting with voters needs to be institutionalized in party structures, from top to bottom. On 8th May, the VP of a policy think-tank tweeted to both Axelrod and Messina ‘Gentlemen, welcome back to America. Let’s win the next one on this side of the pond’. The UK parties are on their own now. We must hope they use the time to build an ongoing dialogue with citizens and voters, learning from the success of the new online interest group barons, such as 38 degrees and Avaaz, by treating all internet contacts as ‘members’ and interacting with them on a regular basis. Don’t wait until 2020!

Helen Margetts is the Director of the OII, and Professor of Society and the Internet. She is a political scientist specialising in digital era governance and politics, investigating political behaviour, digital government and government-citizen interactions in the age of the internet, social media and big data. She has published over a hundred books, articles and major research reports in this area, including Political Turbulence: How Social Media Shape Collective Action (with Peter John, Scott Hale and Taha Yasseri, 2015).

Scott A. Hale is a Data Scientist at the OII. He develops and applies techniques from computer science to research questions in the social sciences. He is particularly interested in the area of human-computer interaction and the spread of information between speakers of different languages online and the roles of bilingual Internet users. He is also interested in collective action and politics more generally.

Recently, there has been a lot of interest in the potential of social media as a means to understand public opinion. Driven by an interest in the potential of so-called “big data”, this development has been fuelled by a number of trends. Governments have been keen to create techniques for what they term “horizon scanning”, which broadly means searching for the indications of emerging crises (such as runs on banks or emerging natural disasters) online, and reacting before the problem really develops. Governments around the world are already committing massive resources to developing these techniques. In the private sector, big companies’ interest in brand management has fitted neatly with the potential of social media monitoring. A number of specialised consultancies now claim to be able to monitor and quantify reactions to products, interactions or bad publicity in real time.

It should therefore come as little surprise that, like other research methods before, these new techniques are now crossing over into the competitive political space. Social media monitoring, which in theory can extract information from tweets and Facebook posts and quantify positive and negative public reactions to people, policies and events has an obvious utility for politicians seeking office. Broadly, the process works like this: vast datasets relating to an election, often running into millions of items, are gathered from social media sites such as Twitter. These data are then analysed using natural language processing software, which automatically identifies qualities relating to candidates or policies and attributes a positive or negative sentiment to each item. Finally, these sentiments and other properties mined from the text are totalised, to produce an overall figure for public reaction on social media.

These techniques have already been employed by the mainstream media to report on the 2010 British general election (when the country had its first leaders debate, an event ripe for this kind of research) and also in the 2012 US presidential election. This growing prominence led my co-author Mike Jensen of the University of Canberra and myself to question: exactly how useful are these techniques for predicting election results? In order to answer this question, we carried out a study on the Republican nomination contest in 2012, focused on the Iowa Caucus and Super Tuesday. Our findings are published in the current issue of Policy and Internet.

There are definite merits to this endeavour. US candidate selection contests are notoriously hard to predict with traditional public opinion measurement methods. This is because of the unusual and unpredictable make-up of the electorate. Voters are likely (to greater or lesser degrees depending on circumstances in a particular contest and election laws in the state concerned) to share a broadly similar outlook, so the electorate is harder for pollsters to model. Turnout can also vary greatly from one cycle to the next, adding an additional layer of unpredictability to the proceedings.

However, as any professional opinion pollster will quickly tell you, there is a big problem with trying to predict elections using social media. The people who use it are simply not like the rest of the population. In the case of the US, research from Pew suggests that only 16 per cent of internet users use Twitter, and while that figure goes up to 27 per cent of those aged 18-29, only 2 per cent of over 65s use the site. The proportion of the electorate voting for within those categories, however, is the inverse: over 65s vote at a relatively high rate compared to the 18-29 cohort. furthermore, given that we know (from research such as Matthew Hindman’s The Myth of Digital Democracy) that the only a very small proportion of people online actually create content on politics, those who are commenting on elections become an even more unusual subset of the population.

Thus (and I can say this as someone who does use social media to talk about politics!) we are looking at an unrepresentative sub-set (those interested in politics) of an unrepresentative sub-set (those using social media) of the population. This is hardly a good omen for election prediction, which relies on modelling the voting population as closely as possible. As such, it seems foolish to suggest that a simply culmination of individual preferences can simply be equated to voting intentions.

However, in our article we suggest a different way of thinking about social media data, more akin to James Surowiecki’s idea of The Wisdom of Crowds. The idea here is that citizens commenting on social media should not be treated like voters, but rather as commentators, seeking to understand and predict emerging political dynamics. As such, the method we operationalized was more akin to an electoral prediction market, such as the Iowa Electronic Markets, than a traditional opinion poll.

We looked for two things in our dataset: sudden changes in the number of mentions of a particular candidate and also words that indicated momentum for a particular candidate, such as “surge”. Our ultimate finding was that this turned out to be a strong predictor. We found that the former measure had a good relationship with Rick Santorum’s sudden surge in the Iowa caucus, although it did also tend to disproportionately-emphasise a lot of the less successful candidates, such as Michelle Bachmann. The latter method, on the other hand, picked up the Santorum surge without generating false positives, a finding certainly worth further investigation.

Our aim in the paper was to present new ways of thinking about election prediction through social media, going beyond the paradigm established by the dominance of opinion polling. Our results indicate that there may be some value in this approach.

Read the full paper: Michael J. Jensen and Nick Anstead (2013) Psephological investigations: Tweets, votes, and unknown unknowns in the republican nomination process. Policy and Internet 5 (2) 161–182.

Dr Nick Anstead was appointed as a Lecturer in the LSE’s Department of Media and Communication in September 2010, with a focus on Political Communication. His research focuses on the relationship between existing political institutions and new media, covering such topics as the impact of the Internet on politics and government (especially e-campaigning), electoral competition and political campaigns, the history and future development of political parties, and political mobilisation and encouraging participation in civil society.

Dr Michael Jensen is a Research Fellow at the ANZSOG Institute for Governance (ANZSIG), University of Canberra. His research spans the subdisciplines of political communication, social movements, political participation, and political campaigning and elections. In the last few years, he has worked particularly with the analysis of social media data and other digital artefacts, contributing to the emerging field of computational social science.

Links to the articles are included below. Happy reading!

Helen Margetts: Editorial

David Karpf: Online Political Mobilization from the Advocacy Group’s Perspective: Looking Beyond Clicktivism

Elisabeth A. Jones and Joseph W. Janes: Anonymity in a World of Digital Books: Google Books, Privacy, and the Freedom to Read

Stefan Larsson and Måns Svensson: Compliance or Obscurity? Online Anonymity as a Consequence of Fighting Unauthorised File-sharing

Irina Shklovski and David M. Struthers: Of States and Borders on the Internet: The Role of Domain Name Extensions in Expressions of Nationalism Online in Kazakhstan

Andreas Jungherr and Pascal Jürgens: The Political Click: Political Participation through E-Petitions in Germany

Jan Fivaz and Giorgio Nadig: Impact of Voting Advice Applications (VAAs) on Voter Turnout and Their Potential Use for Civic Education

Anne-Marie Oostveen: Outsourcing Democracy: Losing Control of e-Voting in the Netherlands

Helen Margetts: Editorial

Stephan G. Grimmelikhuijsen: Transparency of Public Decision-Making: Towards Trust in Local Government?

Jesper Schlæger: Digital Governance and Institutional Change: Examining the Role of E-Government in China’s Coal Sector

Fadi Salem and Yasar Jarrar: Government 2.0? Technology, Trust and Collaboration in the UAE Public Sector

Mike Just and David Aspinall: Challenging Challenge Questions: An Experimental Analysis of Authentication Technologies and User Behaviour

Ainė Ramonaite: Voting Advice Applications in Lithuania: Promoting Programmatic Competition or Breeding Populism?

Thomas M. Lenard and Paul H. Rubin: In Defense of Data: Information and the Costs of Privacy