Category Archives: data journalism

How to search for information in data black holes: Barbara Maseda and the Inventario project

FAQ: Books to read in preparation for doing a data journalism course

This is what you’ll look like after reading all of these books… (“Study of a Man Reading” by Alphonse Legros)

This latest in the frequently asked questions series is an answer to an aspiring data journalism student who asks “Would you be able to direct me to any resources or text books that might help [prepare]?” Here are some recommendations I give to students on my MA in Data Journalism…

Books on data journalism as a profession

Data journalism isn’t just the application of a practical skill, but a profession with a culture, a history, and non-technical practices.

For that reason probably the first thing to recommend is not a book, but just general reading (and listening and watching) as much data journalism, and journalism generally, as possible. These mailing lists (and these) are a good start, and following data journalists on Twitter, and the hashtag #ddj, will expose you to the debates taking place in the industry. Continue reading →

When you get data in sentences: how to use a spreadsheet to extract numbers from phrases

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Unduly lenient sentences review scheme inadequate

This BBC story involved converting phrases into numbers that could be used in calculations

Earlier this month the BBC Data Unit published a story on unduly lenient sentences which involved working with data that was trapped in phrases.

We needed to be able to take a collection of words such as “11 years and 5 months’ imprisonment” and convert that into something that could be used in spreadsheet calculations (specifically, comparing the lengths of time represented by two different phrases).

It’s a problem you come across every so often as a journalist — especially with FOI requests — so in this post — taken from the book Finding Stories in Spreadsheets — I’ll explain how to do that. Continue reading →

FAQ: What are the essential computational skills that a journalist should develop?

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Recognising patterns is a key skill in computational journalism (image by Stanley Zimny)

This latest group of frequently asked questions comes from an interview with Source, published here in full just in case it’s — you know — useful or something…

1. What are the essential computational skills that a journalist should develop?

Firstly, an ability to recognise patterns, or structured information. Spreadsheets are explicitly ‘data’ but some of the most interesting applications of computational journalism are where someone has seen data where others don’t.

Continue reading →

Here are 2 videos and slides from my MA/PGCert Data Journalism taster day

If we are using AI in journalism we need better guidelines on reporting uncertainty

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Chart: women speak 27% of the time in Game of Thrones

The BBC’s chart mentions a margin of error

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 →