Here, then, are some reflections on the 10 pieces which did best in 2016 (there were 100 posts across the year), plus the older posts which keep on giving, and a comparison of some pieces which did far better on Medium than on OJB. Continue reading →
2016 was the year of the bot in journalism. In this edited extract from the forthcoming second edition of the Online Journalism Handbook, I outline what bots are, how bots have been used by media organisations from early Twitter bots to the recent wave of ‘chatbots’, and some tips and tools for getting started with journalistic bots.
‘Bots’ are ‘robots’ – only on the internet. Without the mechanical body of their physical counterparts, all that leaves is a disembodied computer script, normally created to perform repetitive tasks.
This broad description takes in a whole range of activities, and so the term ‘bot’ is used to talk about very different things in different contexts:
In search you might talk about bots used to index webpages, such as the ‘Googlebot’.
In finance and commerce you might talk about bots used to monitor information online and respond to it by buying or selling things.
And in advertising and politics you might talk about bots being used for nefarious purposes: for example, to make it look like more people are viewing webpages, clicking on adverts, or arguing for a particular candidate.
This article isn’t about any of those.
In the context of journalism and publishing, the term ‘bot’ is normally used to refer to something which users can interact with. Examples include: Continue reading →
If there was always a suspicion that it would happen eventually, this year it was confirmed: in 2016 platforms from Facebook to Snapchat, Twitter to Tumblr, all took significant steps towards becoming fully blown publishers. Here are 7 things that happened this year that swung it. Continue reading →
Tonight many journalists will have Tweetdeck or similar social media dashboards ‘tuned in’ to coverage of the US election, typically by creating columns to monitor activity on key hashtags like #Election2016. But on a big occasion like this, the volume of tweets becomes unmanageable. Here then are a few quick techniques to surface tweets that are likely to be most useful to reporters:
Picking the right hashtags: Hashtagify
Hashtagify is a tool for finding out the popularity of certain hashtags. Type a tag into the search box and you’ll get a network diagram like the one shown above — but you can also switch to ‘Table mode’ to get a list of tags that you can sort by popularity, correlation, weekly or monthly trend. Continue reading →
Data scientist David Robinson was behind one of the most striking data stories of this US election season, when his analysis of Donald Trump tweets appeared to confirm that Trump was posting the angriest comments on that account (jointly managed by his campaign staff). Barbara Maseda spoke to Robinson about the story behind that text analysis and what comes next.
It was August 9 when David Robinson published his analysis of Trump tweets on his blog. Robinson had used a series of libraries in the programming language R to collect, clean, process and visualise the data. The process took just 12 hours, from Saturday night through Tuesday morning.
In the following days, the piece would be re-posted and cited by multiple websites, including The Washington Post and Mashable. The original piece alone had hundreds of thousands of views in just a few days.
The result wasn’t just one election story, but one of the biggest indications yet of the potential of text analysis for journalists, with three takeaways in particular: Continue reading →
Well here’s an update: not only is that infrastructure now a reality, but it has become much more complex. And as these tools have become more widely adopted it has shifted the focus on information management from the institution to the individual journalist. Continue reading →
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 →