How Big Data will change our lives and our understanding of them

… If the invention of telescopes provided us with the ability to understand how galaxies behave, and the microscope allowed us to find the cure of such a huge amount of diseases, this century we are going to understand much more about the social systems because of big data. There is no doubt that humans are much more complicated than atoms or even planets and stars, but with the help of powerful mathematical tools and our ever-faster computers we will be able to find and reveal the universal laws of human societies in a numerical framework.

… Read More at Dataconomy. 

Image Credit: William Pearce

 

Breaking news in a connected world

The bitterness of the tragedy is the same, what has changed is the way that information spreads.

I heard about the Boston Marathon Bombing, first when I was preparing to go to bed, and as a recently emerged habit, I was doing my bed-time-Facebook “friend feed” check. The news-line was so shocking that I kept “browsing” for the next few hours. It was quite different to the case of 9/11 attacks when I encountered the story while having my afternoon snack and watching TV in a local snack-bar.

Although it was also hard to believe when I was watching the videos of the smoking buildings on TV some eleven years ago, but this time I was much more suspicious about what I was witnessing on my Facebook friend feed. I thought may be it’s a late arriving “April fool’s joke”! It’s not a totally unreasonable suspicion, given the fact that generally a TV news story is supposed to be much more reliable than a random post by a random guy on his Facebook wall. Then I checked Wikipedia (believe me or not, it’s usually the fastest in such cases, and I don’t have a TV!). I searched for “Boston” in Wikipedia search field and I ended up with a yet very short article titled “2013 Boston Marathon bombings“, and it became quite evident that something nasty has happened.

Although the nature of the terrorist attacks, the emotions involved in and evoked by, the bitterness of the memory, etc, have not changed much during the last decades, but the way of information exposure around these topics, as well as any other “breaking news” has changed dramatically.
The recently developed bottom-up social media offer totally different channels for information dissemination with their own pros and cons.

The rapid spread and deep penetration of information brought up by the social media is undeniable. However, in non-hierarchical structure of news production no one is responsible for the accuracy and correctness of the information, apart from the “citizen journalists” who produce and consume the information at the same time. In addition to that, the type of multimedia materials produced now on breaking news are also significantly different. Most of the videos and photos on such events are produced by “amateur crowed journalists” with their smart phones in hand. However they could draw a fairly accurate and multidimensional picture of the event in an incredibly short time. This could be quite valuable in cases like recent Iran earthquake where much earlier than the official sources could provide information on the casualties and damages caused by the earthquake in rural area and small villages with no official media coverage, you could see dozens of photos and even videos uploaded to the Web.

Publishing uncovered photos of suspects and asking citizens to help the police to spot them is a rather classic method, and has been in use for many years. However, new technologies could again be of great help in this field too. Do not forget that in the case of the marathon bombing, the police tracked the suspects by locating the cell-phone of the driver of the car hijacked by them. I believe this can go much further, remembering that a team from MIT could find 10 red balloons spread over the USA within the 2009 DARPA Network Challenge in less than 9 hours using crowd-sourcing and with the help of around 5000 random participants from public.

Back to the case of natural disasters, when proper distribution of resources and aids within the first few hours after the event, are extremely important and could decrease the casualties significantly, crowd-sourced information could potentially play an important role in assigning priorities and spotting regions in crucial conditions.

A less technically important topic yet with great deal of humanity and emotional aspects of socially connected world of today is the way that social media could provide a common medium to share emotions and sympathies with the people suffered in cases including natural disasters, terrorist attacks and any other of this kind. I remember that in 2001, people in Tehran went to the streets and light candles in memory of the victims of the 9/11 terrorist attack, however I’m not sure whether the suffered families and other USA citizens were exposed to this through the main-stream media. This year it was much easier to send a massage of condolence directly to the attacked nation by using the #preyforbostin “hashtag” in twitter. Therefore it’s no wonder that the hashtags of #preyforboston and #preyforiran, both became “trend” in twitter in mid-April 2013.

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.

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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).

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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.

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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!).

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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).

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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).

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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.

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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).

Aurora-Newtown

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.

 Aurora-Newtown-legth

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.