Dataminr is a startup that recently raised a lot of funding - $130 million according to Techcrunch. It uses algorithms to sift through public tweets and other social media data to uncover talked about events as they happen in real time. Whilst originally designed as a tool to help track the world of finance it now also has clients in the public sector, news, security and crisis management.
Dataminr’s news tool was developed in partnership with Twitter and can be used to alert users to activity based on selected preferences, whilst also pointing towards any spiking behaviour across multiple platforms. Case studies on the Dataminr website include the Amtrak train derailment in Philadelphia which was detected just moments after the event, as passengers still inside the rail cars sent out tweets, which were then instantly triangulated by Dataminr to predict an incident location.
Alexis Sobel Fitts, has been writing about Dataminr and the importance of social listen tools for journalism, in the Columbia Journalism Review. She describes a morning spent at NowThis, whose snappy morsels of news are mined from social media, repackaged and delivered back to consumers via Facebook, Instagram, Snapchat, Kik, Twitter, Vine etc.
In the fast-paced news cycle of places like NowThis, an emerging generation of “social listening” tools like Dataminr looms large. These tools allow reporters to find a breaking story faster than news outlets have typically been able to, and they’ve proven so effective that even scoop-brokers like CNN, the Associated Press, and the New York Post employ them in their newsrooms. Their benefit comes from allowing editors to spot a story that might be lost in a cluster of their own feeds. “If I have one problem with Twitter it’s just that it’s so quick and so ephemeral. It’s so easy to miss things,” said Kurtzman. “If it’s a breaking story, nine times out of 10 we see it on Dataminr before we see it anywhere else.”
Dataminr is just one of a number of tools which can be pointed towards the social chatter. Sobel Fitts touches on CrowdTangle ‘a social listening device that locates well-performing posts across Facebook’, and other in house tools used by media organisations before going on to interview Paul Quigley of News Whip. Whilst marvelling at the ability of these tools to unearth breaking news signals, she raises some interesting questions about journalistic ethics. Is news consumption becoming increasingly directed by algorithms and filters, and what does that mean about the type of news that reaches the top of the social media pile?
…while tracking the most-shared content can be a powerful tool, it can also prove fallible. What people share on social media is only a small subset of what they actually read, a subset dominated by stories that provoke feelings of rage, triumph, or irreverence. What’s more, it’s hard to entirely eradicate the fact that social media algorithms can be gamed—by homogenous groups that cluster together to uplift a story beyond its natural reach, or by sneaky headlines.