Visualizing the Relational Spaces of Hurricane Sandy

Nearly a year ago, Hurricane Sandy made landfall on the eastern seaboard of the US, wreaking havoc on the lives of millions of people in its path. At the time, we threw together some quick maps of where Sandy was being talked about on Twitter, and how the geographies of Sandy-related tweets were both intensely connected to the material impacts of the storm, but also somewhat incongruent.

Since then, we've been putting the finishing touches on a paper that extends our initial interest in the data shadows of Hurricane Sandy to a more comprehensive look at how we can use Sandy-related tweeting to understand the multidimensionality of the geographies of social media activity. All too often, a one-to-one connection is made between the location of a geotagged tweet or other piece of social media content and the content of that tweet [1]. We have instead been attempting to understand how we can think through, and then visualize, how geotagged tweets reflect and produce much more complex socio-spatial relations, which include both intense connections to the places where such content is produced, as well as much more physically distant locations which are brought closer in relational space through such informational flows. The rest of this post is adapted from our paper-in-progress, and outlines how we can map and measure the relational spaces of Hurricane Sandy.

Using T-100 Domestic Market data from the Research and Innovative Technology Administration (RITA) on flights and the number of passengers between city pairs in 2012, we determined the 50 cities that have the most passenger traffic with New York City, ranging from Chicago (3.5 million passengers back and forth) to Kansas City (175,000 passengers). Since operations and activities at some airports close to New York were directly affected by Sandy’s landfall, we exclude any airport within 500 kilometres of Manhattan in this analysis. For the remaining airports we used a buffer of 5km to collect all Hurricane Sandy related tweets and calculated the lower bound of the odds-ratio (or location quotient).  This metric measures the level of Hurricane Sandy tweets relative to overall Twitter activity . If relational networks did not play a significant role in Sandy-related tweeting, one would expect to see a direct distance decay effect: as the distance from New York City increases the odds-ratio should decrease.

Twitter Activity vs. Physical Distance

Our map shows, however, that physical distance has no significant relationship with the relative level of tweeting activity about Hurricane Sandy as is evidenced by both the scatterplot and the map (Spearman’s rho is -0.05). The map uses an azimuthal equidistant projection with New York City as the center, where the size of each airport is proportional to its odds ratio. Airports that are equally distant in physical terms from New York have widely diverging measures of Sandy-related Twitter activity. In addition, the average odds ratio in each 1000km zone does not decrease the further away one travels from New York.

In contrast, a slightly altered version of our map shows that the number of passengers between each city and New York City exhibits a much stronger positive correlation with the odds-ratio metric of Twitter activity (Spearman’s rho is 0.34). This figure preserves the directional bearing of each city with respect to New York City, but instead uses an inverse of the number of passengers to recalculate the relational distance between the cities. Airports are thus no longer displayed according to their physical distance from New York City, but rather based on the intensity of air traffic between the two cities. Since the bearing has remained the same, airports with a higher intensity will move closer to New York along that line, and vice versa. In addition to the correlation coefficient, we can also visually determine that cities with a lower odds-ratio, such as Pittsburgh and Memphis, have a tendency to move towards the outer circles while cities with a higher odds-ratio, such as San Francisco and Los Angeles, move relatively closer.

Twitter Activity vs. Air Traffic Interactivity

In other words, it is the relational connection to New York, measured by number of air travelers, not physical distance, which better explains the level of concern with Hurricane Sandy as expressed via Twitter. This concern, however, can vary within metropolitan territories depending upon the scale of analysis; some parts of an urban area may have much stronger relational ties to distant cities, while other parts are largely disconnected from such global flows.

To test the extent to which the data shadows of Sandy-related tweeting are a localized phenomenon within certain parts of metropolitan areas (rather than a more generalized territorial phenomenon), we increased the initial buffer around each airport from 5km to 25km. Thus, rather than just capturing neighborhoods that are spatially proximate to the airport, this measure captures a much wider swath of each metropolitan area. With this larger buffer, there is a near-reversal of the correlations illustrated in our first map, as Pearson’s rho for total number of passengers is now 0.06 (rather than 0.34), while the distance effect starts to emerge (rho is -0.15). In other words, even though the sociospatiality of a phenomenon like Sandy is expressed partly through a network of connections between territories, these connections are very much bounded by the locally-specific practices of place. This once again highlights the complex ways in which the digital data shadows of a material event are manifest through the intertwinement of different dimensions of social space.

As evidenced by these examples, Sandy’s data shadows are not evenly distributed through the continental United States. They are instead quite intense in some locations, while hardly reaching others at all, demonstrating the multiple spatial dimensions of social processes such as the response to Hurricane Sandy.
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[1] We're as guilty of this as anyone.

Tweeting for Trayvon

While the not guilty verdict is in for George Zimmerman, the discussion about and ramifications of Trayvon Martin's killing seventeen months ago are only beginning, from protest marches throughout the country to tweeting with hashtags like #MillionHoodies. Plenty of people smarter than us have weighed in on what this means for the persistent racism and inequity of the justice system in the United States, so we'll leave that side of the analysis to them. But as we specialize in thinking about and analyzing the geographies of social media, we want to offer our own two cents on what we can collectively take away from the case based on an analysis of geotagged tweets reacting to George Zimmerman's acquittal for Trayvon Martin's slaying.

First, some quick notes on our methodology and general trends in the data. Using DOLLY, we collected all the geotagged tweets from July 1 through July 15, referencing either "JusticeForTrayvon" or "Not Guilty", capturing the usage of these phrases with or without an accompanying hashtag. There were a total of 27,863 tweets referencing "Not Guilty" in this time frame, and just 6,614 referencing "JusticeForTrayvon". We calculated location quotients using hexagonal binning in order to normalize the data based on a relative measure of tweeting activity, as well as to account for differential size of counties or other similarly arbitrary areal units [1]. More simply, this allows us to compare the relative level of Twitter activity in any particular location, rather than relying on raw counts which are biased by population density.

Timeline of Tweets Referencing "Trayvon" from July 13th-14th

In addition to our primary interest in the spatial dimension of tweeting, we're also able to visualize a timeline of tweeting activity, which shows a clear spike immediately following the verdict on Saturday evening around 10 pm. While we're sure that many people's timelines were filled with reactions to the verdict throughout the day on Sunday, it seems as though much of the tweeting became more dissipated throughout the day as protests heated up and others went back to their usual routines.

Taking a look at the spatial patterns of these keywords, there are some clear differences. While there are many fewer JusticeForTrayvon tweets overall, they tend to be generally scattered, but with some relative concentrations largely in the south, in cities like Shreveport and Alexandria, Louisiana and Durham, North Carolina. Again, these measured are normalized for overall level of Twitter activity and thus show that these places were more engaged in this topic via Twitter than other parts of the country.

References to Not Guilty, however, in addition to being far more prevalent, demonstrate significantly more clustering in areas of the country outside the south, especially in Texas (depending on whether or not you consider it to be Southern) and some of the Midwestern or Mid-Atlantic states. We should note that there is a greater concentration references to Not Guilty in the vicinity of Sanford, Florida, the location of Trayvon Martin's killing and the subsequent trial, than was visible in references to JusticeForTrayvon. 

It is also important to note, however, that large cities on the west coast, like Los Angeles, San Francisco and Seattle, have relatively little tweeting about the case for either term, as do major cities along the eastern seaboard, like New York, Boston, D.C. and Philadelphia, despite being the sites of the major protests following the verdict.

Comparing references to the two terms -- while keeping in mind that they are not entirely oppositional, i.e., "Not Guilty" is a much more neutral and contextually dependent phrase than JusticeforTrayvon, which explicitly 'takes sides' in this debate -- reveals a much clearer geographic pattern. This comparison brings the different geographies of these phrases into a stark contrast, with many more references to JusticeforTrayvon concentrated throughout the southern states of Arkansas, Louisiana, Mississippi, Alabama, Georgia and Kentucky (highlighted in purple), with a greater number of more generic references to the verdict (highlighted in green) scattered throughout much of the rest of the country. In short, the hashtag that is more closely associated with protesting the outcome of the court case, is more highly concentrated in Southern states.

One thing that is clear is that although the experience of racism isn't unique to the American South, it is uniquely associated with and experienced in that place when viewed through geotagged social media content [2]. This isn't to say that the tweeting about the case throughout the south is, in and of itself racist, as many, if not most, tweets express outrage at Zimmerman's acquittal, as evidenced by the large number of tweets referencing the JusticeForTrayvon hashtag. But given the back-and-forth around the particularity of racism in the south or the universality of racism across the United States, the higher concentration of this Twitter discussion within the region suggests a process distinct from the rest of the country.

The fact that Trayvon Martin's killing took place in Florida, which shares a similar history with regard to race as the rest of the south, has clearly elicited a broader reaction from those in a (relatively) similar geographic context. The complexities of racism (both historical and contemporary) as expressed in part through problematically-enforced laws like stand-your-ground come to the fore in the south at a time like this, as can be seen in the much higher-than-usual tweeting about the case in Alabama, Georgia, Mississippi, Louisiana and Arkansas. If anything, the outpouring of tweets throughout the south in support of the Martin family and in favor of a more sensible and equitable justice system serves to destabilize the common narrative that the south is unitary, coherent region populated by those clinging to nineteenth century racial mores. The south is, like any other place, marked by conflict and contradiction, something evident nowhere more than in the way it continues to deal with (or ignore) persistent racial inequality like that seen in Trayvon Martin's killing and George Zimmerman's acquittal.
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[1] We've previously demonstrated the utility of this method for mapping concentrations of tweets about a given phenomena.
[2] See, for example, our work on mapping racist tweets in response to President Obama's re-election last November.

Welcome to 'Merica (or is it 'Murica?)

"Chicken & waffle flavored lays? #Murica."

On this day (well, technically the day before) in which we celebrate our independence from those limey redcoats and their tea-guzzling ways [1], it's time we take on one of the truly great debates tearing at the fabric of our country... 'Merica? or 'Murica?

When dropping the first letter of America (either sarcastically or to preserve our limited supply of vowels), is it more correct to (a) continue as if it were still there and use the term 'Merica? or (b) produce an altogether different word, 'Murica, to express our facetiousness and/or lack of spelling ability?

For instance, the emotionally incensed Twitter user below makes a compelling argument for 'Merica:

"P.s. please stop spelling it #murica or #mericuh or any other variation. It's #MERICA. #northerngirlprobs"

In contrast, this erudite tweeter prefers the more guttural 'Murica spelling:

"I don't know Harry, I heard the French are assholes" true statement. Elated to be back in 'Murica" 

But sadly, there is no consensus around this important issue, which if left unchecked (or at least unmapped) could threaten to undermine the very foundation of the nation. Even more tragic is that someone [2] was so unthoughtful as to bring up this topic on the day in which all 'Mericans/'Muricans should join together in our hatred of everyone who doesn't acknowledge that we're so totally superior to them. As such, we dutifully bring you an investigation of this debate that you may not have even been aware of. You're welcome.

In this endeavor, we collected all geotagged tweets referencing "murica" or "merica" in the United States from July 1, 2012 to June 30, 2013, producing 12,407 references to "murica" and 80,344 references to "merica". If you believe that absolute numbers solve the debate, read no further, as we should obviously err on the side of 'Merica. But if you believe that, you must also believe that "On dit que Dieu est toujours pour les gros bataillons" [3], which we must point out is in FRENCH, and hence your opinion on this day can easily be ignored. Again, you're welcome.

Seeing as there is such a significant preference for 'Merica, we created a normalized measure at the county level to allow for geographic comparison in spite of the massive difference in usage of the terms.  Thus, the maps below illustrate counties' share of tweets for each of the two terms.

For example, Cook County, Illinois had the absolute most tweets for either term, with 201 for "murica" and 782 for "merica". But because its 201 tweets represented 1.6% of all tweets referencing 'Murica, and its 782 were only 0.97% of the tweets refrencing 'Merica, it was determined to have a relatively greater usage of 'Murica, and is shaded as such on the map. So in this first map, the areas that are the darkest shade of red are those places where that place produces a significantly greater share of the overall number of tweets for 'Murica than it does for tweets referencing 'Merica. Confused? You're welcome.

The Misspellings of America

While it might be remarked that this unusual methodology unfairly tilts the linguistic playing field in favor of the much less used 'Murica, we would respond with: who cares? This is our map and we can do what we want with it. Also, we're academics (aka commies) and are totally OK with doing things like changing the rules to benefit the less well-off. Also, note the holiday appropriate color ramp of blues to white to reds. Clever, yes? You're welcome.

As you can see, use of 'Murica tends to be associated with the east and west coasts, with there being fairly little usage of the term, even by relative measures, in the interior of the United States. So it appears that those living in "flyover country" tend to prefer the more simple 'Merica, the coastal elite like to step up their sarcasm an extra notch by exchanging an 'e' for a 'u'.

While some of the country's biggest cities -- Los Angeles, New York City, Chicago, Boston, Phoenix, Minneapolis, Seattle and D.C. -- have a relatively greater amount of 'Murica-ness (or should that be 'Murica-lity), the divide between the two spellings doesn't break down along clear urban/rural lines. Oklahoma City and Indianapolis are two of the biggest users of 'Merica, while parts of the Charlotte and Atlanta metropolitan regions are also on the list of counties who believe that it's spelled 'Merica, not 'Murica.

Indeed, if you further normalize by creating a location quotient -- in effect controlling for absolute size -- a similar picture emerges, albeit one which tends to emphasize the large urban areas much less, regardless of whether they see themselves (or others) as 'Mericans or 'Muricans.

The Misspellings of America (by Location Quotient)

Unlike in the previous map, the most red end of the spectrum here actually shows the places where there is the most parity between the usage of the two spellings, even if there are still a greater number of absolute references to 'Merica than to 'Murica. So less populous counties, or those with many fewer Twitter users, such as Piscataquis County, Maine, with fewer than five or ten overall references to either term, will generally tend to be more red.

But perhaps the most interesting (and actually rather methodologically valid) ways of examining the data is to simply look at a ranked list of the top ten counties for each term. One sees here that the top ten counties for 'Merica are almost exclusively in the South, while the top ten counties for 'Murica are outside the South and within large metropolitan areas. So, our working hypothesis (which we suggest you discuss over beer and burgers on this fine day), is that 'Murica is likely a derivative of 'Merica, used ironically by slow-pour-coffee-drinking, skinny-jean-wearing hipsters in big cities. Our extensive examination of hipsters (n=1) confirms this hypothesis and places the epicenter of this plague somewhere in the Greater Boston area. But you can probably spell it however you'd like.

Happy 4th of July everyone!

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[1] No offense intended. Verily, some of the FloatingSheep collective members are British and have yet to make the move to the promised land of 'Merica/'Murica.
[2] That would be us.
[3] "It is said that God is always on the side of the big battalions." -Voltaire

The Geography of #StandWithWendy Tweets

The filibuster by Texas State Senator Wendy Davis on June 25th to block a new piece of legislation that would have resulted in many more restrictions on abortion in Texas brought a lot of attention to the Lone Star state this week. Day-long filibusters, parliamentary machinations, vocal protesters, and changing the time stamps on votes all make for great political theater, even more so as it involves a highly contentious issue and inter-party fights. From our perspective, one of the most compelling elements of this story was the strong response within social media (including Twitter) that this event engendered. In the course of a few hours, tens (or even hundreds) of thousands of tweets were sent using the hashtag #standwithwendy in order to show their support for the senator's efforts.

We collected all geocoded tweets from June 25th and 26th that contained the text "standwithwendy", resulting in a dataset of 3,702 tweets. Although we are primarily interested in the spatial dimension of tweeting activity, the way this event played out over time is particularly interesting. Using our dataset, one can see how this event - or at least its reflection in Twitterspace - started building around 8pm on the 25th and peaking around midnight as the deadline for the special session neared, though it maintained momentum well into the early hours of the 26th when the legislative session was officially declared over and the bill defeated.

Temporal Distribution of Relative Frequency of 

Tweets Containing #StandWithWendy at the County Level

Blue = relatively more #StandWithWendy Tweets; Red = relatively fewer 

Source: DOLLY, n = 3702 #StandWithWendy tweets on June 25th and 26th, 2013; Normalized by the total number of tweets sent during the same time period; The peak is at ~700 tweets right around midnight

Returning to our primary interest in the spatial distribution of tweets, it should come as no surprise that Texas had by far the most tweets, around a thousand in all, or 28.7% of all tweets with the aforementioned hashtag. While Texas is home to six of the twenty largest cities in the US, and thus is likely to have a significant number of tweets based on its population alone, the state is over-represented in the corpus of #StandWithWendy tweets by ~3.5x, relative to its share of the total US population (Texas constitutes around 8% of the country's population), so there is an obvious localizing effect that comes with being the epicenter of this debate. But the phenomenon was far from limited to Texas, with many tweets coming from around the country, though the rest of these tweets much more closely resemble the distribution of population.

Percentage of Tweets by State (blue text) & Location of Each Tweet (pink dot)

Source: DOLLY, n = 3702 #StandWithWendy tweets on June 25th and 26th, 2013; Darker shading indicates greater intensity

The spatial differences are particularly telling when one looks not just at the raw number of tweets, but rather a value normalized by the total number of tweets sent during this time. Doing so allows us to avoid simply highlighting those places with a large number of people by comparing a given place's production of #StandWithWendy tweets relative to its 'usual' tweet output.

The map below shows this normalized distribution. Darker shaded states have relatively more tweets containing #StandWithWendy than the national average, and lighter states have relatively fewer tweets.  The darker the shading the greater the intensity. That Texas remains shaded dark grey in this map is further indication of the above point that its high volume of tweets in this case goes beyond simply its mass of population, while it becomes evident that the large amount of tweeting in California and New York is more dependent on its population than on any unusual interest in the issue by users in those states. South Carolina and Kentucky were the biggest standouts in terms of having relatively few tweets on the subject.

Geographic Distribution of Relative Frequency of 

Tweets Containing #StandWithWendy at the State Level

Source: DOLLY, n = 3702 #StandWithWendy tweets on June 25th and 26th, 2013; Darker shading indicates greater intensity; Normalized by the total number of tweets sent during the same time period

Overall, there seems to be a general pattern of more tweets in the Northeast, Upper Great Plains and West Coast, while states in the Southeast, Mid-Atlantic, Midwest and Southwest have relatively fewer.  But as this is a quick analysis, we'd caution against reading too much into this.

We can also look into the relative amount of tweets at the county level. The map below shows a small section of the country from Texas to South Carolina. One can see that Austin, the location of the state capitol and Senator Davis' filibuster, is very over represented in the number of tweets, as are many other places in Texas. One of the most interesting patterns is within larger metropolitan areas in which the level of tweeting activity around #StandWithWendy varies widely between neighboring counties, as in Atlanta. 

Geographic Distribution of Relative Frequency of 

Tweets Containing #StandWithWendy at the County Level

Blue = relatively more #StandWithWendy Tweets; Red = relatively fewer; White = no tweets; Darker shading indicates greater intensity

Source: DOLLY, n = 3702 #StandWithWendy tweets on June 25th and 26th, 2013; Normalized by the total number of tweets sent during the same time period

Mapping Zombies Book Chapter Now Available!

Not often do we get to write about zombies, internet geography, German Wikipedia articles, cats, and goatse.cx all in the same chapter. But that is precisely what we got to do when working on our newest book chapter "Mapping Zombies".

Feel free to download a pre-publication version of the paper below:

Graham, M., Shelton, T., and Zook, M. 2013. Mapping Zombies: A Guide for Pre-Apocalyptic Analysis and Post-Apocalyptic Survival. In Zombies in the Academy: Living Death in Higher Education. Eds. Whelan, A., Walker, R., and Moore, C. Chicago: University of Chicago Press.

Abstract:

Zombies exist, though perhaps not in an entirely literal sense. But the existence, even the outright prevalence, of zombies in the collective social imaginary gives them a ‘realness’, even though a zombie apocalypse has yet to happen. The zombie trope exists as a means through which society can playfully, if somewhat grimly and gruesomely, discover the intricacies of humanity’s relationship with nature and the socially constructed world that emerges from it. In this chapter, we present an analysis of the prevalence of zombies and zombie-related terminology within the geographically grounded parts of cyberspace, known as the geoweb (see also Haklay et al. 2008 and Graham 2010). Just as zombies provide a means to explore, imagine and reconstruct the world around us, so too do the socio-technical practices of the geoweb provide a means for better understanding human society (Shelton et al. 2013; Graham and Zook 2011; Zook et al. 2010; Zook and Graham 2007). In short, looking for and mapping geo-coded references to zombies on the web provides insight on the memes, mechanisms and the macabre of the modern world. Using a series of maps that visualize the virtual geographies of zombies, this chapter seeks to comprehend the ways in which both zombies and the geoweb are simultaneously reflective of and employed in producing new understandings of our world.

The Maps of IronSheep 2013

It's been about a month and a half since our IronSheep maphacking event at the AAGs in Los Angeles, but with the end of semester, the Geography of Hate map and a number of other goings on around Floatingsheep HQ, we've been negligent in posting the results. It was another great year, with about 35 participants divided up into seven teams (see below). But we'd like to give a special thanks to Rohit Shukla and Mike Rudis at LARTA for being such fantastic hosts, as well as John Yaist and Tim Flewelling at Esri for providing the resources for some pretty sweet prizes.

For reasons of propriety/reputation (you'll know why when you seem some of the results), we're not releasing the names of who belong to which team….but you know who you are! The rules of the event and the list of data made available is at the bottom of the post in case you are interested in the details.

For the actual maps used in the presentations (albeit cleaned up a bit as we try to run a PG-13 blog) see the powerpoint at slideshare embedded below.

Maps from IRONSHEEP 2013 from matthewzook

IRONSHEEP 2013 TEAMS w/ MAPS

Team Bo Peep: Justin Bieber and p0rn

Team Ewe: Gangs and Gangnam

Team Feta: A Field Guide to Tweeter Types

Team Ram: Using Argentine Racing Sheep as a Peri-Urban Transport System

Team Wool: Hipsters and Lattes

Team Mutton: Exploring the Spacio-cultural dimensions of Furries

IRONSHEEP 2013 PHOTOS

IRONSHEEP 2013 DATA

1. We collected all geocoded Tweets in LA county from June 2012 to April 2013 using the DOLLY system.
2. Keyword topics included a range of cultural, political and activity based indicators within the tweet text.  
• The full list of terms included "Beer", "wine", "marijuana", "beer pong", "Zombies", "hipster", "traffic", "accident", "surf* AND !web", "beach", "AK47 OR AK-47 OR "AK 47", "AR15 OR AR-15 OR "AR 15", "shooting*", "happy", "sad", "scared", "ghetto", "danger", "korean taco", "foodtruck OR "food truck", "sushi", "burrito", "latte", "hollywood", "celebrity", "actor* OR actress*", "movie star", "screenwriter OR "screen writer", "broken dream", "beiber", "Lindsay Lohan", "Matthew McConaughey", "hippie*", "yoga", "vegan", "organic", "earthquake", "porn or p0rn", "sunny", "the 405", "gangs", "bloods", "crips", "bloods AND !crips", "crips AND !bloods", 
3. Everyone got the same data and was allowed one special data pull as their “secret sauce”.

IRONSHEEP 2013 RULES

• Sheep come in herds, so work in your group.
• Come up with an entertaining or interesting question, And answer it with a geo-visualization.
• Ask a question that will help us save the world. And answer it with a geo-visualization.
• Bonus point for the gratuitous use of sheep.
• A series of visualizations would be great.
• 60 second lightning presentation of your visualizations.
• Prizes will be award by voting

Mapping Controversy in Wikipedia

Wikipedia, the collection of 37 million articles that anyone can edit, is defined by conflict. The ability for anyone to shape this global repository of knowledge inevitable means that we are presented with fascinating, shocking, and often hilarious discussions on the talk pages of articles. Just check out the talk pages of articles about Barack Obama, the Persian Gulf, and Freddie Mercury (or, if you really want to waste an afternoon, dive into Wikipedia's collection of 'lamest edit wars').  

So, a natural question for was whether we can model and map the controversiality of Wikipedia articles. Does controversy have distinct geographies? It turns out that it does.

To quantify the controversiality of an article based on its editorial history, we focused on “reverts”, i.e. when an editor undoes another editor’s edit completely. We counted all of the reverts in the history of every article and gave a higher weight to editors that revert each other repeatedly. To validate everything, we measured the classifier against human judgement. If you want to read more about the method check articles by friends of the sheep here or here. 

This all allowed us to get a sense of what the most controversial articles in each Wikipedia language editions are.  In English, the most controversial article is George W. Bush, followed by Anarchism, followed by Muhammed. Whereas in French, the top-three most controversial articles are Ségolène Royal, UFOs, and Jehovah's Witnesses (we're certain there are some good jokes hiding in the orders of these lists). For the full list of top-10 controversial articles in ten languages, check out our in press chapter on the topic (or look at the complete lists here and an interactive visualisation of Wikipedia conflicts at this link). But the short version is that at the top of the lists in multiple languages we see articles related to religion, politics, and football; i.e. pretty much exactly what you would expect people to be arguing about.

But what about the geography of these controversial articles in different languages? Where do we see the most controversial articles in different languages? Below is the full list of maps that we created:

What do these maps tell us? First, we see an interesting amount of difference between the various language editions of Wikipedia. Some of the smaller Wikipedias have a high-degree of self-focus in articles that are characterized by the greatest degree of conflict (check out some of Brent Hecht's work for more on this). For instance, we see articles with the highest amount of conflict in the Czech and Hebrew Wikipedias being about the Czech Republic and Israel respectively. 

Even when looking at large languages that are primarily spoken in more than one country, we are able to see that a significant amount of self-focus occurs (look at the Arabic and Spanish maps of conflict for examples of this). 

The interesting exception to this rule is the Middle East. All languages in our sample apart from Hungarian, Romanian, Japanese, and Chinese actually include articles in Israel as some of those characterised by a large amount of conflict. 

Also, worth pointing out is the fact that we see significant differences in the geographic topics that generate the most conflict. The articles in Japanese that generate the most conflict are not only all located in Japan (and are all educational institutions). The Portuguese articles that generate the most conflict are similarly all located in Brasil (the world’s largest Portuguese-speaking nation), with four out of the top five conflict scores being about football teams. 

Within our sample, we actually only see the English, German, and French Wikipedias with a significant amount of diversity in the topics and patterns of conflict in geographic articles. This probably indicates the less significant role that specific editors and arguments play in these larger encyclopaedias. 

Ultimately by visualizing the geography of conflict in Wikipedia, we're able to see both topics that appear to have cross-linguistic resonance (e.g. Arab-Israeli conflict), and those of more narrow interest such as the Islas Malvinas/Falkland islands article in the Spanish Wikipedia.

These maps therefore offer a window into not just the topics that different language communities are interested in, but also the topics that seem worth fighting about.



To read more about conflict and Wikipedia:


Yasseri, Taha, Spoerri, Anselm, Graham, Mark and Kertesz, Janos, (2014) The Most Controversial Topics in Wikipedia: A Multilingual and Geographical Analysis. In: Fichman P., Hara N., editors, Global Wikipedia: International and cross-cultural issues in online collaboration. Scarecrow Press. Available at SSRN.

Graham, M., M. Zook., and A. Boulton. 2012. Augmented Reality in the Urban Environment: contested content and the duplicity of code. Transactions of the Institute of British Geographers. DOI: 10.1111/j.1475-5661.2012.00539.x

Graham, Mark, The Virtual Dimension (2013). Global City Challenges: Debating a Concept, Improving the Practice, M. Acuto and W. Steele. Available at SSRN: http://ssrn.com/abstract=2212824

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

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).