Understanding voters’ information seeking behaviour

Jonathan and I recently published a paper titledWikipedia traffic data and electoral prediction: towards theoretically informed models in EPJ Data Science.

In this article we examine the possibility of predicting election results by analysing Wikipedia traffic going to different articles related to the parties involved in the election.

Unlike similar work in which socially generated online data is used in an automated learning system to predict the electoral results, without much understanding of mechanisms, here we try to provide a theoretical understanding of voters’ information seeking behaviour around election time and use that understanding to make predictions.


Left panel shows the normalized daily views of the article on the European Parliament Election, 2009 in different langue editions of Wikipedia. The right panel shows the relative change between 2009 and 2014 election turnout in each country vs the relative change in the page view counts of the election article in the corresponding Wikipedia language edition. Germany and Czech Republic are marked as outliers from the general trend.

We test our model on a variety of countries in the 2009 and 2014 European Parliament elections. We show that Wikipedia offers good information about changes in overall turnout at elections and also about changes in vote share for parties. It gives a particularly strong signal for new parties which are emerging to prominence.

We use these results to enhance existing theories about the drivers of aggregate patterns in online information seeking, by suggesting that:

voters are cognitive misers who seek information only when considering changing their vote.

This shows the importance of informal online information in forming the opinions of swing voters, and emphasizes the need for serious consideration of the potentials of systems like Wikipedia by parties, campaign organizers, and institutions which regulate elections.

Read more here.

How much Wikipedia could tell us about elections

IMPORTANT NOTE: this post does not aim at predicting the results of any election. This is just a report on some publicly available data and does not draw any conclusion on it. 

In few hours, vote casting for Iranian presidential election, 2013 starts. And within few days (may be one or two) the next president of Iran for the forthcoming four years will be officially announced. This is not only an important event for all Iranians but it also could significantly impact the short or even long term history of the region and even the world, given the complicated internal and international political situation of Iran. Clearly this discussion is out of my expertise and interests and is not the goal of this post.


One of the main differences between Iranian elections and many other countries’ is that most of the time, the candidates are not known until very close to the election date. The process of self-nomination (registration), and then approval and pre-selection of candidates by the Guardian Council, and official announcement of campaigning candidates is rather complicated and unpredictable. In short, almost no one knows the candidates until about a month before election dates.

The rather short period of election campaigns makes it very important how to inform the voters about the programmes and plans of the candidates as well as their previous political biography. Of course online material and social networking could play an important role in bridging between candidates and voters. Among others, Wikipedia is one of the sources that citizens refer to in order to gather at least some basic information about the candidates.

This time, there have been 8 candidates officially announced by the Ministry of Interior, from which 2 have withdrawn later. I did a simple count on the number of edits, number of unique editors, and number of page views of the Persian Wikipedia pages of those 8 candidates from May 7th (start of registration) up to now.  The results are presented in the following chart. To my surprise, there hasn’t been massive editorial work on the pages within this period (180 edits at most). However, page view numbers are relatively large, with a maximum of 180,000 hits during the same period and for the same candidate with the maximum number of edits by maximum number of unique editors. If I were a candidate, I’d have put more effort in order to complete and groom my Wikipedia page! As it’s quite visible!

More interestingly, those candidates with higher page view statistics are commonly known to have higher chances of success according to official and unofficial polls during the last few weeks (I don’t believe in any kind of  survey-based opinion mining, by the way!).

Another interesting aspect of page view statistics, is of course its temporal evolution. In the next diagram I show the number of daily views for the top-4 candidates (according to the total number of page views and excluding Aref, who has withdrawn).


On May 21st, the final list of 8 candidates was announced and it’s the reason for the second peak in all 4 lines and it’s even higher for Jalili because his acceptance as a candidate was kind of a surprise and people apparently has started to know him more. The following bumps in the page view numbers of candidates are mainly due to their presence in either live TV debates or their campaign meetings. Finally, the most interesting and relevant jump is the one of Rouhani, just 2-3 days ago.Among those 4 candidate, Jalili was the least expected and known candidate who registered on the last day of registration and it produced the first peak in his page views.

The only significant event during this period was the withdrawal of Aref, which could be seen as a supportive action for Rouhani (although never mentioned explicitly).

I’d like to emphasise that I’m not trying to do any prediction based on this low-dimensional, sparse data, but if you are interested in predictions, see our soon-to-be-published paper on Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data or read about it in the Guardian.

What can Wikipedia tell us about the Cannes Festival just before the closing

Among all the interesting events taking place today, one is the Closing Ceremony of 2013 Cannes Film Festival.

If you already have seen our recent paper on Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data, you already know that I’m a big fan of movies.

In that paper we investigated the possibility of predicting the future success of movies based on the activity level of Wikipedia editors in combination with page view statistics. We applied a very simple linear model on a very rich set of Wikipedia transactional data and, well, at the end could make rather good “post-dictions” about a sample of USA movies released in 2010.

We all know that “Prediction is very difficult, especially about the future!”, so, the question is weather we could use the method we used in that paper to predict anything about movie success in future?

This is not what I want to talk about now! But in an adventures Saturday evening, I did some  data collection to see whether Wikipedia could give me a hint on the award winners of tonight Cannes closing ceremony.

There are 20 movies in the Competition section. All of them have an article in English Wikipedia, though some very short. First I collected some of the activity measures: Length of the article for each movie, how many times the page has been edited, and by how many distinct editors, how many times the page has been viewed from the beginning of the Festival (by editors and random readers), and finally how many different Wikipedia language editions have an article about the movie.

An interactive visualisation of the data is here (click on it!) Image

All pages together have been viewed more than 600,000 times. That’s a big number. However I was surprised looking at the small number of edits by even smaller number of editors: 15 articles are edited less than 50 times and by around only 5 editors! The average length of all 20 pages is 3700 bytes, just slightly more than a page. Most of the movies have an article in 3 or 4 different languages and no more (including English).

Well, most of the movies are not released yet, that might explain why they are so much under-represented in Wikipedia at the moment. Nevertheless, there are already interesting patterns.

The top-4 movies in respect of page views are also among the top-4 in number of edits, editors, language versions, and are also relatively longer. There is an exception though: The Past (the new drama of Oscar winner Asghar Farhadi) which is 8th in page view ranking, but has comparable activity parameters  to the top-4.

Play around with the visualization, you may see other patterns.

Now let’s focus on the top-3 of the most viewed articles, which are well separated from the rest of the movies: Only God Forgives a Thriller by Nicolas Winding RefnInside Llewyn Davis The Coen Brothers‘ Drama, and Behind the Candelabra by Steven Soderbergh.

The first movie of these 3 is released on 22 May in France and that might explain why is that so popular. See the diagram below (clickable), which shows the daily page views from a week before the Festival opening until yesterday (click to enlarge).


The first peak is clearly due to the nomination announcement on 18 April and the second peak of Only God Forgives  is due to its release. So, what I’m saying is that may be Coen’s have done a better job and we only need to wait until it reaches the market. We will see how the Juries think about it!

Now you may think I’m a Coen’s fan, but No! My favourite directors among these 20 (actually 21, counting Coen Brothers 2!) are Roman Polanski and Asghar Farhadi with Venus in Fur and The Past this year. Talking about directors, let’s have a look at the Wikipedia page view statistics of directors and compare them to their movies. The following figures show the daily views for those two directors and the movies they brought to Cannes this year. Yellow lines are the movies an red ones for the corresponding directors (click to enlarge).


That’s interesting. Isn’t it? The Wikipedia article of Asghar Farhadi and his movie (right panel) are not only at the same level of “popularity” but also their fluctuations are heavily correlated (the second peak comes from the movie release in France), whereas Roman Polanski (left panel) seems to be much more famous than his movie with weird up and downs in his data!

The last piece is on the main Wikipedia article about the event: 2013 Cannes Film Festival with more than 123,000 visitors within the last 2 months. If someone wants to have a baseline to do details fluctuation analysis on individual movies, I would recommend the following diagram, which clearly shows the main events and the overall public interest in them.


And Finally, don’t forget to take a look at our paper:

Mestyán, M., Yasseri, T., and Kertész, J. (2012) Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data. Forthcoming.