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This time, there have been 8 candidates officially announced by the Ministry of Interior, from which 2 have withdrawn later.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.

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!) 

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 Refn, Inside 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.