Journalists get hold of data using four broad approaches: it might be newly published or issued; it might be leaked; they might request it; or they might seek it out based on an idea or in reaction to a news event.
In this second short video first made for students on the MA in Data Journalism at Birmingham City University and shared as part of a series of video posts, I go through the different ways that journalists obtain data and the different types of story that those sources can lead to.
Data journalism isn’t just about spreadsheets and interactives: in this video from my MA Data Journalism classes at Birmingham City University I look at why the news industry has expanded its focus on data journalism over the past decade, and how thinking about definitions of data journalism can help reporters think more broadly about potential stories and subjects beyond official statistics.
I also look at related terms such as computational journalism, robot journalism and augmented journalism — and what we can learn from those definitions as practitioners.
This is part of a series of videos recorded during the coronavirus pandemic.
The best online journalism has a range of qualities: it tends to be succinct, easy to scan, and it considers how a user might interact with it — whether through links or embedded elements, or more conversational elements like comments and social media hashtags.
One way to remember those qualities is the mnemonic BASIC: Brevity; Adaptability; Scannability; Interactivity; and Community/Conversation. In the video below I talk through those five qualities, and how to put them into practice when writing for the web.
This video was first made for students on the MA in Multiplatform and Mobile Journalism and the MA in Data Journalism at Birmingham City University and is shared as part of a series of video posts. A shorter version can also be found here.
Beatriz Farrugia used Brazil’s freedom of information laws to investigate the country’s hosting of the World Cup. In a special guest post for OJB, the Brazilian journalist and former MA Data Journalism student passes on some of her tips for using FOIA.
I am from Brazil, a country well-known for football and FIFA World Cup titles — and the host of the World Cup in 2014. Being a sceptical journalist, in 2019 I tried to discover the real impacts of that 2014 World Cup on the 213 million residents of Brazil: tracking the 121 infrastructure projects that the Brazilian government carried out for the competition and which were considered the “major social legacy” of the tournament.
In 2018 the Brazilian government had taken the website and official database on the 2014 FIFA World Cup infrastructure projects offline — so I had to make Freedom of Information (FOIA) requests to get data.
The investigation took 3 months and more than 230 FOIA requests to 33 different public bodies in Brazil. On August 23, my story was published.
Here is everything that I have learned from making those hundreds of FOIA requests:
Continue readingEmail newsletters are an excellent way for journalism students to build a profile in a field while also improving their specialist knowledge and editorial skills. I’ve put together a short video guide on some of the key techniques to use when starting an email newsletter — and why it’s a great way to stand out in the jobs market.
The video outlines three typical purposes of newsletters, the importance of visuals and links, and other key qualities of the genre. Watch it below.
This video was first made for journalism students on Birmingham City University’s MA in Multiplatform and Mobile Journalism. It also includes some advice on referencing reading and evidence in an evaluation of students’ work.
“Pajama Journalism”—reports you can do in nightclothes on a computer, without going anywhere or talking to anyone—should not define online news, but the practice is widespread. In a special guest post, Michael Bugeja argues that following just four basic principles of reporting can help improve this form of journalism.
Continue readingIf you follow me on Twitter you’ll know every so often I highlight a story which uses company accounts. This latest one has celebrity and fashion, and involves a charity that’s raising money through star-studded events — what more can you ask for?
It’s a great excuse to find out about a range of techniques for finding stories and background in company accounts. Follow the thread from the tweet embedded below, or read it on Threadreader here.
For reporters satellite data offers unique opportunities for original investigations and visual storytelling. But how do you get started? And what should you be looking out for? In a guest post for the Online Journalism Blog, MA Data Journalism student Niels de Hoog speaks to four journalists who regularly work with satellite data about how to start, best practices and —most importantly — mistakes to avoid.
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While researching my post on developing curiosity in journalism I came across Terry Heick‘s 4 stages of curiosity. It outlines 4 steps that learners go through as they grapple with new knowledge: firstly finding out what they are expected to do (the process); then understanding the content involved; then how to transfer that to particular situations; and finally how it applies to, and changes, them.
But the same model can also be adapted to provide a framework for investigations. Here’s how:
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In an extract from a new chapter in the ebook Finding Stories in Spreadsheets, I explain what regular expressions are — and how they can be used to extract information from spreadsheets. The ebook version of this tutorial includes a dataset and exercise to employ these techniques.
The story was an unusual one: the BBC Data Unit had been given access to a dataset on more than 200,000 works of art in galleries across the UK. What patterns could we find in the data that would allow us to tell a story about the nature of the nation’s paintings?
Some of the data was straightforward to work with: the ‘artist’ column was relatively clean, and allowed us to identify the most common male and female artist. It turned out that the latter – the Victorian botanist Marianne North – was relatively unknown. So, that was one story we could tell.
But other parts of the data were more problematic. The date column, for example, contained inconsistently formatted data: in the majority of cases a specific year had been entered, but in many others the data contained text such as “18th century” or “1900-1920” or “1800s”.
We also noticed that monarchs featured heavily in the art – but understandably there was no column that was specifically dedicated to classifying those. If we wanted to identify the most-painted monarchs we would have to create new data that somehow extracted those names from the paintings’ titles.
These problems – extracting data from existing data, particular text data – are what regular expressions are designed for. In this chapter I will explain what regular expressions are, and how to use them in spreadsheets.
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