Mapping #kony2012 on Twitter

A LOT has been said about the recent Kony 2012 video. There have been critiques, critiques of critiques, critiques of critiques of critiques.

Interestingly, there were many claims that video was one of the most successful viral campaigns in the history of the Web. In other words, the campaign was able to rapidly spread, in part, because of millions of people sharing it through social media.

And yet, I still haven't seen is any sort of time/space mapping of how the campaign spread. So, Devin Gaffney and I decided to do just that.

The map below was made by collecting all georeferenced tweets containing the terms "#kony", "#kony2012", and "#stopkony" between February 28 and March 13.

It is important to point out that we're only dealing with geotagged tweets here (which account for less than 1% of all content pushed through Twitter). We also are missing data from some of March 11 due to our script breaking down (so interpret the data from that day with particular caution).

Nonetheless, the map still does give us a rough sense of where and when the debate (on Twitter) was taking place.

We start by seeing a small amount of discussion in the U.S., that quickly spreads to Western Europe (especially the UK) on March 6. By the 7th, the conversation in North America and Western Europe has become much more dense and widespread. We also see people in Asia, South America and Africa joining the discussion. Then, from the 10th onwards, the intensity of mentions of Joseph Kony on Twitter starts to die down throughout much of the world.

The results that we see here are perhaps not that surprising, but they do allow us to trace some of the temporal and geographic contours of a much debated social and political moment in the Internet's history. #Kony's moment of visibility was both brief and largely transatlantic. This Western-centric pattern of information flow is not necessarily surprising and can be found on many other online platforms. However, given the video's relevance to East Africa, and the global diffusion of Twitter (e.g. Indonesians form the world's 6th largest population of Twitter users), we might have expected #Kony to have a slightly less clustered geography.

I'll soon try to make some graphics that map Kony-related tweets as a proportion of the total number emanating from each country. Doing so might give us a better sense of how prominent the narrative was in the consciousness of Internet users in each country.

In the meantime, if you're interested in exploring the data in more detail, the full time series is included below:

Augmented Realities and Uneven Geographies: Exploring the Geo-linguistic Contours of the Web

I've just had a paper accepted to Environment and Planning A (Augmented Realities and Uneven Geographies: Exploring the Geo-linguistic Contours of the Web). The paper (co-authored with Matt Zook) is concerned with the ways in which augmented inclusions and exclusions, visiblilities and invisibilities will shape the way that places become defined, imagined, and experienced.





The maps above are all taken from an earlier draft of the paper. They visualise the layers of information indexed by Google and segment the data by language in order to map some of the geo-linguistic contours of the Web. Have a glance through the paper, and let us know if you have any comments or questions. The publication date of the full paper should be some time in early 2013.

Mapping Edits to Wikipedia from the Middle East and North Africa

As a quick follow-up to one of my previous posts showing the geography of edits to Wikipedia, I wanted to share another map depicting the huge inequalities in where contributions to the encyclopedia in the MENA region come from.  

In particular, this map allows us to get a sense of how many edits (to any Wikipedia language version) come from Israel compared to other countries in the region. In other words, Israelis are far more active in creating/reproducing knowledge in one of the world's most used websites than their counterparts in the rest of the Middle East and North Africa. 

I'll post some versions on these maps normalised by population/Internet population soon-ish.

Mapping Edits to Wikipedia from Africa

A few days ago I blogged about a map that I made which depicts where edits to Wikipedia come from in Africa. I decided to play with the data some more in order to create this graphic.

It still visualises the number of edits created from each country, but gives us a much better sense of the scale of the differences in contributions. In particular, it really highlights how much participation we're seeing from Egypt.

As I mentioned in the last post, we're working on a paper that aims to explain some of these inequalities, and I'll post a draft of it as soon as possible.

Interactive visualisations for teaching, research, and dissemination

Everyone at a UK university should vote for our proposal to created interactive visualisations for teaching, research, and dissemination. Scott Hale has created a detailed proposal and video outlining what it is that we plan to do. Read more and vote here.

Geographies of the World's Knowledge - ebook now available for tablets

Our booklet, "Geographies of the World's Knowledge" is now available for iPads from Apple's iTunes store. The publication is free and is optimised for tablet viewing (we've included lots of cool interactive features). If you have a tablet, I highly recommend you check it out!

If you don't, you can always download our PDF version in both English and German. Let me know if you have any questions/suggestions.

Mapping Edits from Africa in Wikipedia

A few weeks ago, I wrote a post examining where Wikipedia edits come from. The map below drills into the data in slightly more detail in order to visualise where edits in Africa come from (the data include edits to all language versions of Wikipedia).

The answer is that there are way more edits originating in Egypt than anywhere else on the continent. South Africa is then a distant second. The grey patches in Africa indicate countries that didn't even register enough edits to show up in the Wikimedia Foundation's sampling of edits.

The number of Internet users in a country explains some of the patterns that we see here, but can't in any way explain all of the variance in the map (e.g. take a look at Nigeria - a country with over 45 million Internet users).

My team and I are currently working on a paper in which we use a range of secondary data to further examine these inequalities in participation in East Africa and the Middle East and North Africa. I'll share a draft as soon as possible.

Big Data and the End of Theory?

The Guardian just published a short post that I wrote which looks at the discourses surrounding 'big data.'

In it I argue that:

Gender, geography, race, income, and a range of other social and economic factors all play a role in how information is produced and reproduced. People from different places and different backgrounds tend to produce different sorts of information. And so we risk ignoring a lot of important nuance if relying on big data as a social/economic/political mirror.

We can of course account for such bias by segmenting our data. Take the case of using Twitter to gain insights into last summer's London riots. About a third of all UK Internet users have a twitter profile; a subset of that group are the active tweeters who produce the bulk of content; and then a tiny subset of that group (about 1%) geocode their tweets (essential information if you want to know about where your information is coming from).

Despite the fact that we have a database of tens of millions of data points, we are necessarily working with subsets of subsets of subsets. Big data no longer seems so big. Such data thus serves to amplify the information produced by a small minority (a point repeatedly made by UCL's Muki Haklay), and skew, or even render invisible, ideas, trends, people, and patterns that aren't mirrored or represented in the datasets that we work with.

Big data is undoubtedly useful for addressing and overcoming many important issues face by society. But we need to ensure that we aren't seduced by the promises of big data to render theory unnecessary.

We may one day get to the point where sufficient quantities of big data can be harvested to answer all of the social questions that most concern us. I doubt it though. There will always be digital divides; always be uneven data shadows; and always be biases in how information and technology are used and produced.

And so we shouldn't forget the important role of specialists to contextualise and offer insights into what our data do, and maybe more importantly, don't tell us.

You can check out the full piece here.