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.

P-values: misunderstood and misused

Since I launched this blog, I always wanted to write something about the dangers of big data! Things that can go wrong easily when you study a large scale transactional data. Obviously, I haven’t done this!

But recently we (Bertie, my PhD Student and I) just finished a paper titled: P-values: misunderstood and misused.

Of course statistical “misunderstanding” is one of the dangers of big data. Calculating p-values has become the most-used method to prove the “significance” of your analysis. However, as we say in the abstract:

P-values are widely used in both the social and natural sciences to quantify the statistical significance of observed results. The recent surge of big data research has made p-value an even more popular tool to test the significance of a study. However, substantial literature has been produced critiquing how p-values are used and understood. In this paper we review this recent critical literature, much of which is routed in the life sciences, and consider its implications for social scientific research. We provide a coherent picture of what the main criticisms are, and draw together and disambiguate common themes. In particular, we explain how the False Discovery Rate is calculated, and how this differs from a p-value. We also make explicit the Bayesian nature of many recent criticisms, a dimension that is often underplayed or ignored. We also identify practical steps to help remediate some of the concerns identified, and argue that p-values need to be contextualised within (i) the specific study, and (ii) the broader field of inquiry.


“Significant”; taken from  http://xkcd.com/882/


When and Where is Citizen Science happening?

Citizen Science is research undertaken by professional scientists and members of the public collaboratively. The best example of it is Zooniverse.

Since it first launched as a single project called Galaxy Zoo in 2007, the Zooniverse has grown into the world’s largest citizen science platform, with more than 25 science projects and over 1 million registered volunteer citizen scientists. While initially focused on astronomy projects, such as those exploring the surfaces of the moon and the planet Mars, the platform now offers volunteers the opportunity to read and transcribe old ship logs and war diaries, identify animals in nature capture photos, track penguins, listen to whales communicating and map kelp from space.

We have been running a project to reveal the taxonomy and ecology of contributions to the Zooniverse. The project is now towards its end. And we have just released the first short report:
After dinner: the best time to create 1.5 million dollars of ground-breaking science

Count this! In celebration of the International Year of Astronomy 2009, NASA’s Great Observatories. Image: Nasa Marshall Space Flight Center.

Wikipedia; modern platform, ancient debates on Land and Gods

What are the most controversial topics in Wikipedia? What articles have been subject to edit wars more than others? We now have a tool to explore what topics are most controversial in different languages and different parts of the world.

Wikipedia is great! There is no doubt about it. You may argue that it’s not reliable, it’s incomplete, it’s biased, etc, and I might agree. However, despite all these issues, Wikipedia IS useful, fast, practical and phenomenal!

Do you have any other example of a mass collaboration at the scale of Wikipedia with more 40 million editors, having produced more than 37 million articles in more than 280 languages?

Coordinating a small group of friends becomes a big issue when it’s about collaboration and reaching agreement on some topic, how is that possible that this huge number of unprofessional individuals with different backgrounds, cultures, opinions, come together and produce the largest encyclopaedia of all times?

Well, the answer is: it’s not easy and it’s not always smooth. Many Wikipedia articles are about neutral topic, like watermelon and hamsters. But there are lots of editorial wars and opinion clashes happening behind the scenes of Wikipedia as well. What are the main characteristics of these wars? What are the most disputed articles? Does it give us a window to how humans of different parts of the world think about stuff? It’s not difficult to observe some of the editorial wars in English Wikipedia, for example see the list of controversial issues in Wikipedia. But first of all there is no guarantee that these lists are inclusive, and more importantly, such lists are only available for the biggest language editions like English Wikipedia.

There have been already nice studies on Wikipedia conflict, but unfortunately only limited to English Wikipedia. In a recent multidisciplinary project (see the paper), my colleagues Anselm Spoerri (communication and Information scientist), Mark Graham (geographer) , János Kertész (senior physicist), and I (physicist in transition to computational social scientist) studied Wikipedia editorial wars in 13 different language editions including: English, German, French, Spanish, Portuguese, … Persian, Arabic, Hebrew, … Czech, Hungarian, Romanian, …. Chinese and Japanese.

We have developed our tools to locate, quantify, and rank the most controversial articles in different language editions without being able to read the language! Our method to measure editorial wars has been reported in our previous papers on Dynamics of conflicts in Wikipedia and Edit wars in Wikipedia.

Now that we have measures of controversy for all the articles in the language editions under study, we could have lots of fun!

First take a look at the awesome post by Mark on mapping conflict and geographical locations of the controversial articles, and then I’ll tell you something about most debated topics in different language editions.

Here’s the top-10 list of most controversial articles in different languages:

English German French Spanish Portuguese Czech Hungarian  Romanian Arabic Persian Hebrew Japanese Chinese
1 George W. Bush Croatia Ségolène Royal Chile São Paulo Homosexuality Gypsy Crime FC Universitatea Craiova Ash’ari Báb Chabad Koreans in Japan Taiwan
2 Anarchism Scientology Unidentified flying object Club América Brazil Psychotronics Atheism Mircea Badea Ali bin Talal al Jahani Fatimah Chabad messianism Korea origin theory List of upcoming TVB series
3 Muhammad 9/11 conspiracy theories Jehovah’s Witnesses Opus Dei Rede Record Telepathy Hungarian radical right Disney Channel (Romania) Muhammad Mahmoud Ahmadinejad 2006 Lebanon War Men’s rights TVB
4 List of WWE personnel Fraternities Jesus Athletic Bilbao José Serra Communism Viktor Orbán Legionnaires’ rebellion & Bucharest pogrom Ali People’s Mujahedin of Iran B’Tselem internet right-wing China
5 Global warming Homeopathy Sigmund Freud Andrés Manuel López Obrador Grêmio Foot-Ball Porto Alegrense Homophobia Hungarian Guard Movement Lugoj Egypt Criticism of the Quran Benjamin Netanyahu AKB48 Chiang Kai-shek
6 Circumcision Adolf Hitler September 11 attacks Newell’s Old Boys Sport Club Corinthians Paulista Jesus Ferenc Gyurcsány’s speech in May 2006 Vladimir Tismăneanu Syria Tabriz Jewish settlement in Hebron Kamen Rider Series Ma Ying-jeou
7 United States Jesus Muhammad al-Durrah incident FC Barcelona Cyndi Lauper Moravia The Mortimer case Craiova Sunni Islam Ali Khamenei Daphni Leef One Piece Chen Shui-bian
8 Jesus Hugo Chávez Islamophobia Homeopathy Dilma Rousseff Sexual orientation change efforts Hungarian Far- right Romania Wahhabi Ruhollah Khomeini Gaza War Kim Yu-Na Mao Zedong
9 Race and intelligence Minimum wage God in Christianity Augusto Pinochet Luiz Inácio Lula da Silva Ross Hedvíček Jobbik Traian Băsescu Yasser Al-Habib Massoud Rajavi Beitar Jerusalem F.C. Mizuho Fukushima Second Sino-Japanese War
10 Christianity Rudolf Steiner Nuclear power debate Alianza Lima Guns N’ Roses Israel Polgár Tamás Romanian Orthodox Church Arab people Muhammad Ariel Sharon GoGo Sentai Boukenger Tiananmen Square protests of 1989

Interesting and familiar titles, right? Did you realise that some titles appear in many different language editions? Many of them are about religion: Jesus; countries: Israel, Brazil; politics: Ségolène Royal, George W. Bush.


If you’d  like to take a look at the top-100 or in case you fancy having the complete lists with controversy score, get them from here.

What you see at the right is  a Word Cloud of all the titles in top-100 lists.

There are interesting patterns. Similarities and differences. International and global issues and very local items. An interactive visualization of top-100 lists in different languages to show overlaps and similarities, is waiting for you here.

To have a more general picture, we would have to look further than just “titles”. We need to consider more general topics and concepts, which the articles  can be categorised based on.

We hand-coded all the articles in top-100 lists with 10 different category tags. See the population of topical categories in each language in the interactive chart below (click on it!).


Some interesting patterns: Religion and Politics are debated in Persian, Arabic, and Hebrew even more than the others.  Spanish and Portuguese Wikipedias are full of wars on football clubs. French and Czech Wikipedias have relatively more disputed articles on science and technology related topics. Chinese and Japanese Wikipedia are battle fields for manga, anime, TV series, and entertainment fans. TVB product appear quite often in the Chinese list, and well, the number 19 most disputed article in Japanese Wikipedia is “Penis”!

“So What?” is probably what you are asking. Generally speaking the implication of these kind of studies are two-fold:

1) These results could help Wikipedia and similar projects (which are already many, and growing) to be better designed, considering these experiences and the observations we made. Local effects shouldn’t be neglected and specially Wikipedias with smaller community of editors could be inefficiently very much focused on local issues.

2) we believe that this kind of case-studies (Wikipedia being the case) could help us and social scientist to understand more about human societies. Topics like conflict emergence, its dynamics, its universal features, and the resolution mechanisms could be  empirically examined for the first time.  Most of the theories in social science could have never been tested against real world experiments (in contrast to natural sciences). But now, thanks to our digital life of today, we are able to track and analyse all the actions and interactions of a huge society of individuals (here, Wikipedia editors), so why not test the pre-existing social theories in a large “social experiment” of Wikipedia?

Read more about this project:

Yasseri, Taha, Spoerri, Anselm, Graham, Mark and Kertesz, Janos, The Most Controversial Topics in Wikipedia: A Multilingual and Geographical Analysis (May 23, 2013). Fichman P., Hara N., editors. Global Wikipedia: International and Cross-Cultural Issues in Online Collaboration. Scarecrow Press (2014), Forthcoming. Available at SSRN: http://ssrn.com/abstract=2269392

And more on Wikipedia by our team:

Török, J., Iñiguez, G., Yasseri, T., San Miguel, M., Kaski, K., and Kertész, J. (2013) Opinions, Conflicts and Consensus: Modeling Social Dynamics in a Collaborative Environment. Physical Review Letters 110 (8).

Yasseri, T., Sumi, R., Rung, A., Kornai, A., and Kertész, J. (2012) Dynamics of conflicts in Wikipedia. PLoS ONE 7(6): e38869.

Yasseri, T., Kornai, A., and Kertész, J. (2012) A practical approach to language complexity: a Wikipedia case study. PLoS ONE 7(11): e48386.

Yasseri, T., Sumi, R., and Kertész, J. (2012) Circadian patterns of Wikipedia editorial activity: A demographic analysis. PLoS ONE 7(1): e30091.

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

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.

The coverage of a tragedy

“The Newtown School shooting is a school shooting that occurred on December 14, 2012 in Newtown, Connecticut, Connecticut. 24 persons are reported to have been killed, including 17 children.”

This is the whole content of the first revision of Wikipedia article on Sandy Hook Elementary School shooting. The tragedy happened at around 9.35 am in local time and this first version of coverage was written in English Wikipedia only 210 minutes after the tragic event. And of course other language editions started to cover the story consequently and after 8 hours, 14 language editions already had the corresponding article, though in some editions very briefly.

 In the Figure below, a diagram shows the growth and spread of the story in Wikipedias, in the sense of number of language editions with a coverage, versus the time elapsed since the start of the event. It compares very well with the coverage of the previous significant massacre in Aurora, back in July 2012. Despite all the differences between these two events, such as time of the day, place and demography of victims, etc, the growth of the Wikipedia coverage happens qualitatively in the same way: Within a short period of around 8 hours, around 15 “early adopters” will have an article and this number exceeds 30 in less than 48 hours.  In both cases, language editions like English, Spanish, Swedish, Finnish, Polish and French have the fastest reaction (see the bars at the margins of the Figure).


In contrast to this similarity, a big difference is observed in the length of articles for the two events. In the next Figure, the length of the corresponding articles in English Wikipedia is plotted against the elapsed time (curves are smoothed within a window of 20 edits). After a similarly growing phase of 12 hours, the article of the School Shooting continues to grow more than twice, compared to the Cinema Shooting article.


The article of the Newtown event is not only longer but also has got more edits compared to the Aurora article; 2600 vs. 1900 edits within the first 48 hours.

 There could be many reasons for this dissimilarity such as the different emotional atmosphere, the number of casualties, and the presence of contradictory stories about the Newtown event in other Media and therefore the need to a more detailed coverage in the Encyclopedia.

 I hope we do not get a chance to have more examples of such stories to be able to perform a systematic study (there are currently around 70 articles in the category of Massacres in the United States, many of them happened before the launch of Wikipedia), however, focusing on a sample of naturally similar events (e.g. earthquakes or other kind of natural disasters) with detailed analysis, could open new windows towards a better understanding of the mechanisms behind news spread and information diffusion.

 P.S.: The results presented here could be partially inaccurate due to many technical reasons and should be considered in the context of popular science.

 P.S.2: This post was inspired by a tweet from Brian Keegan.

P.S.3: Brian has a brilliant detailed analysis of the coverage in English Wikipedia.