Death by Zoom: are we mistakenly trying to recreate the classroom instead of making something web-native?
A few weeks ago I was invited to talk at an online mini-fest about a ‘big idea’ for the future of online learning. I decided to talk about what I called ergodic education — how concepts from interactivity can be used to inform teaching as learners move online. In this post I talk about some of those concepts and how they can be adopted to a lockdown-era classroom.
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Yesterday I wrote the first of a two-part series on the 7 angles that are used to tell stories about data. In this second part I finish the list with a look at the three less common angles: those stories focusing on relationships; angles that focus on the data itself — its absence, poor quality, or existence — and exploratory stories that often provide an opportunity to get to the grips with the data itself.
This New York Times interactive became one of their most-read stories of all time
Exploratory angles are largely web-native. Its selling point is often characterised by a ‘call to action’ like “explore”, “play” or “Take the quiz”. Alternatively, it might sell the comprehensiveness of the analysis in the way that it is “Mapped” or documents “Every X that ever happened”, or simply answers the question “Who/how/where”. Continue reading
In my data journalism teaching and training I often talk about common types of stories that can be found in datasets — so I thought I would take 100 pieces of data journalism and analyse them to see if it was possible to identify how often each of those story angles is used.
I found that there are actually broadly seven core data story angles. Many incorporate other angles as secondary dimensions in the storytelling (a change story might go on to talk about the scale of something, for example), but all the data journalism stories I looked at took one of these as its lead.
In the first of a two-part series I walk through how the four most common angles can help you identify story ideas, the variety of their execution, and the considerations to bear in mind. Continue reading
Channel 4’s Who Knows Who project was an early adopter of network analysis
Network analysis offers enormous potential for journalism: able to tease out controversial connections and curious clusters, and to make visible that which we could not otherwise see, it’s also often about relationships and power.
It is both a data journalism technique and an open source intelligence (OSINT) technique — and yet it is relatively underused in both, most likely because the tools to do network analysis have only become accessible in the last few years.
Here, then, is an introduction for journalists, adapted from my lectures on the MA in Data Journalism at Birmingham City University.
Network analysis is, simply, a way of making relationships between entities visible.
It might be used in journalism to generate or check leads (by showing unusual patterns), to communicate the story itself (i.e. to show those patterns to others) or to allow readers to explore a system. Continue reading
Among the many claims made by UK Government adviser Dominic Cummings in his press conference on Monday was one that could be easily checked.
As evidence that he took the threat from coronavirus seriously he said that he’d written about the danger of coronaviruses last year.
“For years I’ve warned of the dangers of pandemics. Last year I wrote about the possible threat of coronaviruses and the urgent need for planning.”
Before the press conference was over, that claim had already been proven to be false, thanks to some underused journalistic tools of verification: the Wayback Machine and sitemap.
Here’s how it was done — and how journalists can use the same tools in their work, whether it’s to verify a claim made about the past, a claim about what was not said in the past, or to uncover details that may have been unwittingly revealed in earlier versions of webpages. Continue reading
R number ranges shown by the FT
Journalism doesn’t like uncertainty: editors are trained to cut out vagueness and journalists taught to be as concrete as possible in their reporting. In most cases it compels reporters to ensure they have a firm grip on the details and are confident in the story they are reporting.
But with coronavirus, this discipline becomes a systemic blind spot.
From prevalence to testing, and from deaths to infection rates, the story of this pandemic is full of uncertainty. Here, then, are 3 ways that journalists need to understand — and better communicate — the things that we don’t know, and won’t know, about it. Continue reading
I’ve written a piece for DataJournalism.com on covering the societal impact of a pandemic with data — it covers:
The BBC’s live stream included an alert that 122,347 tests had been “carried out” yesterday. In fact 40,000 of those had merely been sent out.
When a prominent UK politician announced on live TV that the Government had hit its target of 100,000 coronavirus tests a day by the end of April, on the very last day of that month no less, journalists faced a challenge.
Two hours earlier, specialist publication Health Service Journal had revealed that the figures had been fudged: instead of counting the numbers of tests that had been conducted on samples, a source informed them, the Government had quietly changed its own metric so that a test that had been sent out in the post — and not returned or tested — could now be added to the figures.
40,000 tests were then sent out in one day.
By any reasonable understanding, a test sent was not the same thing as a test done, as a raft of jokes — from people saying they had marked their students’ homework by sticking it in the mail, or paid their tax by receiving a letter from the taxman — pointed out.
And yet there was the Government making its claim — at length and without question, on the national broadcaster, and on the websites of national news organisations.
It was 20 minutes before the claim was queried by a reporter, by which time many viewers had switched off.
How journalists responded to this announcement — in different ways, at different times, and in different places — provides a valuable case study for anyone dealing with numbers and the claims that politicans make about them. Continue reading
The latest frequently asked questions post comes in response to a PhD student looking at data journalism and gatekeeping. Here are the questions and my answers:
Journalists and news organisations have both been forced to adapt by the increased competition, and the changing nature of the world that we report on (i.e. the fact that it is more data-driven).
Many publishers tell me they want to give their journalists data skills because they feel that they need to ‘up their game’ in order to compete with new entrants to the sector, and to create distinctive content in an environment where celebrities, politicians, sportspeople etc. all publish direct to audiences rather than via media. Continue reading
A couple weeks ago I published a guide to cognitive biases for journalists. I saved perhaps the biggest one of all — confirmation bias — for a post all of its own. It might be one of the best-known biases, but for that very reason it can be easy to underestimate. Here, then, is what you need to know — and what to do to reduce it.
Confirmation bias is the tendency to seek out — or more easily believe or recall — information that confirms our existing beliefs.
It leads us to make judgements that are not based on an equal assessment of all the evidence, but only that evidence we have cherry picked, remembered or attributed more credibility to.
Confirmation bias affects journalists in at least three ways: Continue reading
