This year I’ve been working with my MA Data Journalism and MA Multiplatform and Mobile Journalism students on techniques for telling longer form stories. In this post I explain how a consideration of story structure can help you clarify the sources that you will need to talk to in order to gather the elements needed for an effective longform story.
In a previous post I discussed how different plot frameworks identified by Christopher Booker in his book ‘The Seven Basic Plots‘ – such as the ‘quest’ or ‘tragedy’ – can help a journalist think about longer investigations. In addition to those types of story, however, Booker also identifies 5 stages of a story. These are:
Anticipation: setting, character and – crucially – ‘problem’ are introduced.
Dream: we begin exploring/solving the problem.
Frustration: we hit more problems.
Nightmare: this is the ‘final battle’ of fiction narratives.
Miraculous Escape/Redemption/Achievement of the Prize or (in the case of Tragedy) the Hero’s Destruction.
How the 5 stages work in journalism
I would argue that you can see these stages at work in most longform journalism, too. Here’s how: Continue reading →
This year I’ve been working with my MA Data Journalism and MA Multiplatform and Mobile Journalism students on techniques for telling longer form stories. In this post I explain how a consideration of seven common plot types can help you clarify what story it is you’re telling – and what you might need to tell that.
There are many ways to tell a story, and many stories to tell. An investigation can be trying to establish the cause of a problem, or solutions to that problem; it can be revealing previously hidden unethical behaviour, or shining a light on issues which are ‘hidden in plain sight’; it can be holding a mirror up to a part of society to reveal its scale; or giving a voice to that part of society as a step towards a more sophisticated understanding of problems affecting it. And depending on the type of story, you might adopt different approaches to telling it. Continue reading →
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.
Earlier this month I held a special open taster class at Birmingham City University for anyone interested in my full time MA and part time PGCert courses in Data Journalism. As some people couldn’t get to the UK to attend the event I put together two video screencasts recapping some of the material covered in the session.
This latest set of frequently asked questions comes from a MA student at Coventry University who is researching Instagram. Their questions revolve around the impact of social media on journalism and Instagram in particular.
How are the new social media apps changing the way journalism is produced, distributed and consumed?
There’s a lot of scope in that question so in breaking it down it’s firstly worth making a distinction between apps (i.e. tools, used by producers to capture, publish and share) and platforms (i.e. a place where content is hosted).
So for example Instagram is a platform that hosts content which can be accessed on a tablet, or on mobile, or a desktop or laptop computer, but can also be published to through an app on mobile or tablet. 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.”