Twitter’s analytics service is a useful tool for journalists to understand which tweets are having the biggest impact. The dashboard at analytics.twitter.com provides a general overview under tabs like ‘tweets’ and ‘audiences’, and you can download raw data for any period then sort it in a spreadsheet to see which tweets performed best against a range of metrics.
However, if you want to perform any deeper analysis, such as finding out which days are best for tweeting or which times perform best — you’ll need to get stuck in. Here’s how to do it. Continue reading →
Yesterday I spoke at the BBC Data Day: an event bringing together people at the BBC interested in data-related issues, techniques and tools. During the question and answer session following my talk one person mentioned a common reason why he wasn’t using data journalism techniques:
“I haven’t got the time.”
For some reason this time the phrase bristled. And later I realised why.
A journalist wouldn’t get away with saying they “hadn’t got the time” to get a response quote.
A journalist wouldn’t get away with saying they “hadn’t got the time” to get the background to a story.
A journalist wouldn’t get away with saying they “hadn’t got the time” to check a key fact. Continue reading →
My latest ebook – Finding Stories in Spreadsheets – is now live on Leanpub.
As with Scraping for Journalists, I’m publishing the book week-by-week so the book can be updated based on reader feedback, user suggestions and topical developments.
Each week you can download a new chapter covering a different technique for finding stories, from calculating proportions and changes, to combining data, cleaning it up, testing it, and extracting specific details.
There’s also a downloadable spreadsheet at the end of each chapter with a series of exercises to practise that chapter’s technique and find particular stories.
Along the way I tackle some other considerations in telling the story, such as context and background, and the importance of being specific in the language that you use.
I recently spent 2 days teaching the basics of data journalism to trainee journalists on a broadsheet newspaper. It’s a pretty intensive course that follows a path I’ve explored here previously – from finding data and interrogating it to visualizing it and mashing – and I wanted to record the results.
My approach was both practical and conceptual. Conceptually, the trainees need to be able to understand and communicate with people from other disciplines, such as designers putting together an infographic, or programmers, statisticians and researchers.
They need to know what semantic data is, what APIs are, the difference between a database and open data, and what is possible with all of the above.
They need to know what design techniques make a visualisation clear, and the statistical quirks that need to be considered – or looked for.
But they also need to be able to do it.
The importance of editorial drive
The first thing I ask them to do (after a broad introduction) is come up with a journalistic hypothesis they want to test (a process taken from Mark E Hunter’s excellent ebook Story Based Inquiry). My experience is that you learn more about data journalism by tackling a specific problem or question – not just the trainees but, in trying to tackle other people’s problems, me as well.
So one trainee wants to look at the differences between supporters of David and Ed Miliband in that week’s Labour leadership contest. Another wants to look at authorization of armed operations by a police force (the result of an FOI request following up on the Raoul Moat story). A third wants to look at whether ethnic minorities are being laid off more quickly, while others investigate identity fraud, ASBOs and suicides.
Taking those as a starting point, then, I introduce them to some basic computer assisted reporting skills and sources of data. They quickly assemble some relevant datasets – and the context they need to make sense of them.
For the first time I have to use Open Office’s spreadsheet software, which turns out to be not too bad. The data pilot tool is a worthy free alternative to Excel’s pivot tables, allowing journalists to quickly aggregate & interrogate a large dataset.
Formulae like concatenate and ISNA turn out to be particularly useful in cleaning up data or making it compatible with similar datasets.
The ‘Text to columns’ function comes in handy in breaking up full names into title, forename and surname (or addresses into constituent parts), while find and replace helped in removing redundant information.
It’s not long before the journalists raise statistical issues – which is reassuring. The trainee looking into ethnic minority unemployment, for example, finds some large increases – but the numbers in those ethnicities are so small as to undermine the significance.
Scraping the surface of statistics
Still, I put them through an afternoon of statistical training. Notably, not one of them has studied a maths or science-related degree. History, English and Law dominate – and their educational history is pretty uniform. At a time when newsrooms need diversity to adapt to change, this is a little worrying.
But they can tell a mean from a mode, and deal well with percentages, which means we can move on quickly to standard deviations, distribution, statistical significance and regression analysis.
One thing I do is ask one trainee to toss a coin 30 times and the others to place bets on the largest number of heads to fall in a row. Most plump for around 4 – but the longest run is 8 heads in a row.
The point I’m making is regarding small sample sizes and clusters. (With eerie coincidence, one of them has a map of Bridgend on her screen, which made the news after a cluster of suicides).
That’s about as engaging as this section got – so if you’ve any ideas for bringing statistical subjects to life and making them relevant to journalists, particularly as a practical tool for spotting stories, I’m all ears.
Visualisation – bringing data to life, quickly
Day 2 is rather more satisfying, as – after an overview of various chart types and their strengths and limitations – the trainees turn their hands to visualization tools – Many Eyes, Wordle, Tableau Public, Open Heat Map, and Mapalist.
Suddenly the data from the previous day comes to life. Fraud crime in London boroughs is shown on a handy heat map. A pie chart, and then bar chart, shows the breakdown of Labour leadership voters; and line graphs bring out new possible leads in suicide data (female suicide rates barely change in 5 years, while male rates fluctuate more).
It turns out that Mapalist – normally used for plotting points on Google Maps from a Google spreadsheet – now also does heat maps based on the density of occurrences. ManyEyes has also added mapping visualizations to its toolkit.
Looking through my Delicious bookmarks I rediscover a postcodes API with a hackable URL to generate CSV or XML files with the lat/long, ward and other data from any postcode (also useful on this front is Matthew Somerville’s project MaPit).
Still a print culture
Notably, the trainees bring up the dominance of print culture. “I can see how this works well online,” says one, “but our newsroom will want to see a print story.”
One of the effects of convergence on news production is that a tool traditionally left to designers after the journalist has finished their role in the production line is now used by the journalist as part of their newsgathering role – visualizing data to see the story within it, and possibly publishing that online to involve users in that process too.
A print news story – in this instance – may result from the visualization process, rather than the other way around.
More broadly, it’s another symptom of how news production is moving from a linear process involving division of labour to a flatter, more overlapping organization of processes and roles – which involves people outside of the organization as well as those within.
The final session covers mashups. This is an opportunity to explore the broader possibilities of the technology, how APIs and semantic data fit in, and some basic tools and tutorials.
Clearly, a well-produced mashup requires more than half a day and a broader skillset than exists in journalists alone. But by using tools like Mapalist the trainees have actually already created a mashup. Again, like visualization, there is a sliding scale between quick and rough approaches to find stories and communicate them – and larger efforts that require a bigger investment of time and skill.
As the trainees are already engrossed in their own projects, I don’t distract them too much from that course.
You can see what some of the trainees produced at the links below: