Diary of an internet geography project #4

Reblogged from ‘Connectivity, Inclusivity and Inequality

Screen Shot 2014-08-05 at 1.31.00 PMContinuing with our series of blog posts exposing the workings behind a multidisciplinary big data project, we talk this week about the process of moving between small data and big data analyses. Last week, we did a group deep dive into our data. Extending the metaphor: Shilad caught the fish and dumped them on the boat for us to sort through. We wanted to know whether our method of collecting and determining the origins of the fish was working by looking at a bunch of randomly selected fish up close. Working out how we would do the sorting was the biggest challenge. Some of us liked really strict rules about how we were identifying the fish. ‘Small’ wasn’t a good enough description; better would be that small = 10-15cm diameter after a maximum of 30 minutes out of the water. Through this process we learned a few lessons about how to do this close-looking as a team. 

Step 1: Randomly selecting items from the corpus

We wanted to know two things about the data that we were selecting through this ‘small data’ analysis: Q1) Were we getting every citation in the article or were we missing/duplicating any? Q2) What was the best way to determine the location of the source?

Shilad used the WikiBrain software library he developed with Brent to identify all roughly one million geo-tagged Wikipedia articles. He then collected all external URLs (about 2.9 million unique URLs) appearing within those articles and used this data to create two samples for coding tasks. He sampled about 50 geotagged articles (to answer Q1) and selected a few hundred random URLs cited within particular articles (to answer Q2).

  • Batch 1 for Q1: 50 documents each containing an article title, url, list of citations, empty list of ‘missing citations’
  • Batch 2 for Q2: Spreadsheet of 500 random citations occurring in 500 random geotagged articles.

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Big Data and Small: Collaborations between ethnographers and data scientists

This article first appeared in Big Data and Society journal published by Sage and is licensed by the author under a Creative Commons Attribution license. [PDF]

Abstract

In the past three years, Heather Ford—an ethnographer and now a PhD student—has worked on ad hoc collaborative projects around Wikipedia sources with two data scientists from Minnesota, Dave Musicant and Shilad Sen. In this essay, she talks about how the three met, how they worked together, and what they gained from the experience. Three themes became apparent through their collaboration: that data scientists and ethnographers have much in common, that their skills are complementary, and that discovering the data together rather than compartmentalizing research activities was key to their success.

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