Measuring the popularity of the contestants in the Eurovision Song Contest using Twitter

In this post, we confirm that Loreen is well placed to win the popular vote in the Eurovision Song Contest final 2012.

We have previously shown in this blog that Ethersource monitoring of on-line sentiment can predict the popular vote in certain high-profile media events, such as the national Eurovision Song Contest. In this post, we report on some observations on using Ethersource to measure the popularity of the contestants in the international Eurovision Song Contest, based on analysis of expressions of popularity on Twitter. The following image shows the relative popularity scores of the participating countries.

Popularity of each country

It should be obvious to anyone following the pre-contest speculations about who will win the ESC 2012 that the proportions of popularity in this image do not correlate with current betting odds for the ESC final (the current odds can be found at any betting site). The image shows Ireland and the UK as the most popular contributions in the ESC final (they are ranked 11th and 5th in the current betting odds). One reason for this discrepancy can be that popularity and betting odds do not refer to the same type of measurement; popularity refers to population-wide opinion, while betting odds are estimates of who will win the actual contest (which is determined both by popular and jury votes). Another reason for this discrepancy is the issues identified in commentaries of other recent attempts to predict election votes based on sentiment analysis of the Tweet stream:

  • Twitter users (and users of other social media) do not constitute a perfect sample of the population, which means that measurements based on Twitter may not be representative for the population as a whole.
  • Twitter is a perfect medium for marketers and campaigns, which makes the analysis sensitive to ad-bots and automated Twitter campaigns.

These concerns are of course valid also for the present scenario. However, even more important when comparing measurements based on Twitter analysis across different countries are the following issues:

  • There is a huge difference in population size between the European countries: Russia has a European population of more than 100 million, while Iceland has a population of a mere 300 000 inhabitants.
  • The Twitter penetration (i.e. proportion of the population that use Twitter) is very different for different countries. In the present scenario, where we measure expressions of popularity on Twitter, it means that some countries may get high popularity scores merely because a comparatively large proportion of the population in that country uses Twitter (people tend to promote their own country’s entry in the ESC).

It is somewhat difficult to find recent and reliable estimates of the Twitter penetration per country, but not so recent studies show that the Netherlands, Turkey, UK, and Ireland top the list for Twitter penetration in Europe. Perhaps this explains the results we see in the image above? Scaling the popularity scores for each country by the estimated number of Twitter users in that country produces the following image:

Popularity of each country

When scaling with Twitter penetration, Sweden gets the highest relative popularity score. This is in line with current betting odds, which unanimously rank Sweden as the most likely winner. However, the other countries that receive high normalized popularity scores do not correlate with odds rankings: Greece has the second highest popularity score (ranked 14th place in the odds rankings), followed by Denmark (ranked 8th place), Ireland (11th), and Iceland (7th). These discrepancies may be due to the issues with non-representativeness and Twitter penetration discussed above. We may also add the following issues:

  • The activity level of the Twitter population in some countries may not correspond with the Twitter penetration; Twitter users may be more active in some countries than others.
  • The interest for the ESC may be higher in certain countries than others, thus leading to more Tweets about the contestants from that country.

We conclude this post with the observation that Loreen seems to be the likely winner of the popular vote in the ESC final 2012. We also conclude that attempting to model population-wide opinions based on Twitter analysis is a non-trivial task that requires more than merely counting word frequencies.

A Minute-by-minute Popularity Contest – Loreen versus Danny

Despite the fact that the Swedish part of the Eurovision Song Contest final was broadcast live, as a TV viewer it was impossible to get a sense of just how popular the artists were at a given point in time. Having access to Ethersource made sifting out meaningful blog posts and Tweets in real-time a breeze! Below are two graphs outlining, minute-by-minute, the popularity of the two top contestants as expressed in Swedish on-line social media for the day of the final (click the image for a larger version). Note that the popularity score of Loreen’s reaches higher during her performance than does Danny’s. In fact, looking at the scale and the contents of the two graphs, it is clear that the expressions of popularity towards Loreen is consequently higher throughout the day.

The popularity of Loreen and Danny Saucedo, measured minute-by-minute during the day of the final of the Swedish Eurovision Song Contest.

The popularity of Loreen and Danny Saucedo, measured minute-by-minute during the day of the final of the Swedish part of the Eurovision Song Contest. The annotations in red denotes the appearance on stage by the two artists.

The timing information for the performances of the artists is available at the official web site of the contest.

Fabulous Fest Forecast by Gavagai

Sweden’s contribution to the Eurovision Song Contest this year has been decided in yesterday’s finale with ten contestants. The winner of 2012 year’s Swedish music fest Melodifestivalen is Loreen, with the song “Euphoria”, which landed almost 700000 call-in votes from the at-home TV audience.

Using the Ethersource technology, Gavagai followed the on-line sentiment towards all contestants throughout the lead-up to the event. We are pleased to note that Gavagai’s forecast of the results based on expressions of appreciation in blog posts and tweets which was published in the paper edition of Svenska Dagbladet (SvD) in the morning prior to the event – was close enough to the actual outcome of the viewers’ votes to not only correctly predict the three top spots from the start field of ten contenders but to get their vote percentages pretty much right!

Talk comes cheap – demonstration counts!

This was fun! We will make similar public opinion forecasts in coming analyses, and return to the observations we can make from this event and these and similar data.