You judges have a tough job. I am sure you must have many strong candidates – I can only imagine the strong submissions you must get. If you were to pick this one, my guess would however be it is for one of the following reasons:
1. These projects suggest novel and often inspiring ways to approach a difficult subject – war and its consequences.
2. They are often very original: from training a machine learning algorithm to read twitter to using over 200 years worth of data to look at conquest
3. Some of the projects are of a different flavour than most data journalism: the story being not the data itself, but what insights you can derive from it – often via rigorous statistical modelling.
4. The portfolio shows a willingness to challenge readers: one attempts (and, I dare say, succeeds) at showing how knowing facts about statistical distributions can be cool (because they show that some countries actively faked covid data).
5. The projects show ambition: from using machine learning to model how the war might lead to more or less political unrest around the world (through price hikes in fuel and food, and more), to pioneering a new way to measure military spending (a “military PPP”).
To give a bit of background about me: I started working as a journalist in 2020, just before the pandemic began. Before that, I was at Princeton University doing a PhD, and before that, at New York University doing a BA. And before that, I spent a year as a conscript – not too far from the Russian border – in the Medic Battalion of the Norwegian Army. As a journalist, I am probably best known for being the person behind “the pandemic’s true death toll” – the project which first estimated how many really died due to the pandemic via excess deaths. In addition to my own work as a journalist, I have in the past year also intermittently acted as data editor at The Economist.
Beyond individual projects, I strive to make data journalism use more of the cutting edge methods found in social science and machine learning, and to make the field more transparent by sharing my code and data whenever I can, as well as helping the field grow through mentoring, presentations (of which I have done many in the past year – including some in person!), and by building bridges with academia (here hope to leverage my nomination to the Institute for Quantitative Social Science).
Thank you for reading!
Description of portfolio:
My portfolio submission shows my work on the Russia-Ukraine war and its consequences. The goal is to show some of the breadth of what I’ve produced on the subject in the past year. In addition to stories about how the war affects Ukraine and the rest of the world, I’ve tried to include stories of how the war is changing Russia. I’ve also included an article suggesting how hard it is to cover the country as a data journalist – providing statistical evidence that within it, even covid death statistics may have been actively faked.
To usefully highlight some challenges I encountered along the way:
“Russians in every major city and region call for #nowar” includes data from public profiles on Instagram. Instagram does not provide this to journalists, and have taken steps to make obtaining it via computer very difficult. Getting this data was therefore very, very, time-consuming.
“Russia is swaying Twitter users outside the West to its side” uses a machine learning algorithm to detect pro-Ukraine, neutral, and pro-Russia sentiment. Such an algorithm did not exist when I started the project, so I had to make and validate one myself. This involved manually reading and coding thousands of tweets to create training data.
“Vladimir Putin is dragging the world back to a bloodier time” begins with a map showing the approximate location of all wars on the planet since 1816. No dataset of these locations existed either – so I had to find them through other data and historical research.
In terms of metrics, I am not sure what is expected or what I’m allowed to say on my end. However, I can confidently say that all these articles were widely read: and some were among the most popular articles we have published all year. Moreover, the article “Costly food and energy are fostering global unrest” resulted in massive policy engagement. This included “a high-level discussion” at the United Nations’ headquarters (they told me I could not be more specific), which centred on how the UN could use the methods it pioneered in its work and planning.
I am the author of all these projects, doing the data collection, modelling, writing, and reporting. All were solo-authored except three: Daniella Raz collaborated with me on “How the war in Ukraine compares to other refugee crises”, Adam Roberts on “Vladimir Putin is dragging the world back to a bloodier time”, and “Costly food and energy are fostering global unrest” was the work of many hands – I did the data journalism sections, and Robert Guest, Joanna Lillis, Charlotte McCann, Lena Schipper, Mitra Taj, Liam Taylor, Namini Wijedasa, Piotr Zalewski contributed the rest. I gratefully acknowledge the support of the wider editorial staff, especially my editors and talented data visualizers and interactive designers.