- Stories to report in the short term
- Moving beyond health stories
- Looking for stories about changing behaviour
- Thinking creatively about data
- Stories from historical data
- Interactivity as a data angle
- Looking and planning ahead
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:
How do you think the role of journalists has changed during the 21st century, especially with the data explosion and the rise of misinformation and disinformation?
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
The latest frequently asked questions post is an answer to Ian Silvera who asks a number of questions about teaching journalism within the context a fast-changing industry. You can read his post here.
How do you think journalism lecturers should keep up with the fast-changing industry?
Following the industry press is pretty essential for anyone teaching in the field. Sites like Journalism.co.uk and Niemanlab are especially good at covering developments, but there’s also InPublishing and HoldtheFrontPage who cover it more broadly including new technologies and issues. And tons of email newsletters.
It’s easier than ever to follow individuals inside the industry, too – on Twitter as well as professional blogs, Medium.com and anywhere else. I maintain Twitter lists of people reporting in particular fields or in particular roles, for example, and generate Nuzzel newsletters for those lists so I’m up to date with what they’re sharing. Continue reading
There’s a story out this week on the BBC website about dialogue and gender in Game of Thrones. It uses data generated by artificial intelligence (AI) — specifically, machine learning — and it’s a good example of some of the challenges that journalists are increasingly going to face as they come to deal with more and more algorithmically-generated data.
Information and decisions generated by AI are qualitatively different from the sort of data you might find in an official report, but journalists may fall back on treating data as inherently factual.
Here, then, are some of the ways the article dealt with that — and what else we can do as journalists to adapt.
Margins of error: journalism doesn’t like vagueness
The story draws on data from an external organisation, Ceretai, which “uses machine learning to analyse diversity in popular culture.” The organisation claims to have created an algorithm which “has learned to identify the difference between male and female voices in video and provides the speaking time lengths in seconds and percentages per gender.”
Crucially, the piece notes that:
“Like most automatic systems, it doesn’t make the right decision every time. The accuracy of this algorithm is about 85%, so figures could be slightly higher or lower than reported.”
And this is the first problem. Continue reading
Last week saw the third Data Journalism UK conference, an opportunity for the country’s data journalists to gather, take stock of the state of the industry and look at what’s ahead.
The BBC Shared Data Unit’s Pete Sherlock kicked off the event, looking back at the first 18 months of the unit’s existence. In that period the unit has trained 15 secondees and helped generate over 600 stories across more than 250 titles in the regional press.
Sherlock highlighted two stories in particular to demonstrate how the data unit had helped equip regional reporters in holding power to account: the Eastern Daily Press’s Dominic Gilbert‘s story on legal aid deserts, and JPI Media’s Aimee Stanton‘s report on electric car charging points.
Both stories resulted in strong pushback – from the Ministry of Justice and the electric car industry respectively – but their new data journalism skills gave them the confidence to persist with the story. Continue reading
The latest in my series of FAQ posts follows on from the last one, in response to a question from an MA student at City University who posed the question “Do you think that an increase in algorithmic input is leading to a decline in human judgement?”. Here’s my response.
Does an increase in computation lead to a decline in human input?
Firstly, it’s important to emphasise that the vast majority of data journalism involves no algorithms or automation at all: it’s journalists making calculations, which historically they would have done manually.
You mention the possibility that “an increase in computation leads to a decline in human input”. An analogy would be to ask whether an increase in pencils leads to a decline in human input in art. Continue reading
I’ve now been teaching data journalism for over a decade — from one-off guest classes at universities with no internal data journalism expertise, to entire courses dedicated to the field. In the first of two extracts from a commentary I was asked to write for Asia Pacific Media Educator I reflect on the lessons I’ve learned, and the differences between what I describe (after Daniel Kahneman) as “teaching data journalism fast” and “teaching data journalism slow”. First up, ‘teaching data journalism fast‘ — techniques for one-off data journalism classes aimed at general journalism students.
Like a gas, data journalism teaching will expand to fill whatever space is allocated to it. Educators can choose to focus on data journalism as a set of practices, a form of journalistic output, a collection of infrastructure or inputs, or a culture (see also Karlsen and Stavelin 2014; Lewis and Usher 2014; Boyles and Meyer 2016). Or, they might choose to spend all their time arguing over what we mean by ‘data journalism’ in the first place.
We can choose to look to the past of Computer Assisted Reporting and Precision Journalism, emerging developments around computational and augmented journalism, and everything that has happened in between.
In this commentary, I outline the different pedagogical approaches I have adopted in teaching data journalism within different contexts over the last decade. In each case, there was more than enough data journalism to fill the space — the question was how to decide which bits to leave out, and how to engage students in the process. Continue reading