As a good example of data journalism I used the buzzfeed news article “Built to Last” by Megha Rajagopalan, Alison Killing and Christo Buschek. Published on August 27, 2020, this article is the first of a five-piece series, providing insights into China’s re-education camps in Xinjiang.
I first heard of this during the conference when Alison Killing was presenting on it. Together the team could, via publically available satellite images and interviews with former detainees, shedding light on the scale of the internment camp system in Xinjiang, and details about the life on the inside.
I picked this project because I believe it’s a great illustration of how innovative approaches to data journalism can help journalists work around censorship. Even though the story is quite lengthy, the visualizations are good at showing the scale of how massive this infrastructure project is, and how China have been able to hide it.
Revisiting my good example I agree with my argument that it is a great illustration of how innovative approaches to data journalism can help journalists work around censorship. One thing I have learned during this course is that you as a journalist have to be multifaceted and smart in the ways to approach data and make sense of the enormous flow of information circulating around us. Verifying data is one of the most important parts of journalism (Kovach & Rosenstiel, 2021) and I think the journalists here have done an amazing job verifying the claims of China building detention centres for muslims by finding blind spots in the satellite images and by talking to eyewitnesses. Analysing this article by the FAIR principles, the data methodology falls short however. The FAIR principles, meaning that data should be Findable, Accessible, Interoperable and Reusable (Wilkinson et al, 2016), is a good framework to analyse transparency of a data journalistic piece. This article is findable by having a clear title and keywords making it easy to search for. It is also accessible since the article is free and without a paywall. Improvements can be made by providing more information about the data. While the article does a good job at describing how the satellite imagery made for the data by using images showing the reader, they do not provide raw data or any links to where that data may be accessible for the reader which would increase the transparency. One thing I still like about this article is the way it includes personal stories. Including these stories of people speaking about their time there makes the data more understandable for us as readers. It makes us empathise with the affected people and makes it easier to grasp the effects of the Chinese government’s decisions, it provides context which is a necessity for data journalism (Bradshaw, 2017).