Marrying human intelligence and judgment with technological capabilities could give newsgathering organizations a great leap forward.
What’s news? Well, it’s not what it used to be, and probably not what it’s going to become.
Over the past decade, the landscape of news and information has shifted dramatically. Journalists now routinely mine huge data sets to uncover hard-to-find stories. Automations fire off news headlines at sub-second speeds. Rich multimedia presentations are the rule, not the exception. Platforms like Facebook and Twitter dominate the distribution of news. And new startups are trying out and testing new business models every month.
At Reuters, we track all of these changes closely – not only because we’re one of the world’s largest news organizations, but also because we supply news and information to thousands of other newsrooms. As Executive Editor for Editorial Operations, Data and Innovation, the pace and shape of change is something I observe with special focus. What’s clear is that all this is likely only the beginning of even more seismic change in the industry.
For all the upheaval in news, some core parts of the business remain nearly untouched. People still create most of the content. Stories are still created for mass audiences. Technology is more a tool than a partner. All of that will likely change.
We’re already rapidly adopting new tools to help us find news faster. We use Reuters Tracer, a technology developed by the Thomson Reuters Research and Development team that algorithmically detects newsworthy events breaking on Twitter and rates the likelihood that they’re true so that reporters can get a head start on confirming the news.
But it goes far beyond just using technology to help us do what we do more effectively. It’s using technology to rethink what we do and how we do it.
For example, language generation systems – technologies that can understand documents, analyze data and create text – have been largely focused so far creating short stories at lightning speed, or on turning out vast numbers of relatively simple, routine stories. But machines are capable of much more, especially trawling through huge amounts of data and surfacing interesting patterns and outliers.
So far, we haven’t trusted those systems to turn them into stories and that’s probably the right call. Knowing if some trend or change is significant is something that humans (still) do better than machines. So, why not marry the two capabilities – machine analysis and human judgment – into a single system and take advantage of the strengths of both parties?
That’s an approach we’re investigating at Reuters – using technology to crawl through multiple proprietary databases, unearth insights, turn them into sentences and paragraphs, and then offer them to journalists, who can use them as tips, integrate them into stories, or simply discard them. Call it the cybernetic newsroom.
And as the system harnesses feedback from how journalists use the insights, it could steadily improve, presenting fewer but more insightful pieces of information, or even being so deeply embedded in the newsroom workflow that it would be flagging reporters to leads they could follow up on.
This capability also means we can think about delivering not just more insightful news, but also more personalized news.
Imagine if the market report didn’t come to readers just when the market closes or when journalists write it, but was instead created by a machine whenever a reader wanted it. And that the report didn’t simply recite how the market performed, but how that reader’s specific portfolio performed compared to the broader market. Or even better, if the report could analyze the key reasons for why the reader’s portfolio performed as it did, and present that information as well.
For example: “It’s 3:35 pm. The market is currently up 1 percent, but your portfolio is down 2 percent. A key reason for your portfolio’s performance is the purchase of XX stock last week, which has fallen sharply since….”
How close are we to being able to do this, and much more? The technologies are very nearly here already, and newsrooms are starting to embrace the possibilities. But there’s probably even more that we haven’t imagined yet, and that we’re just at the start of a brave new era of news and information.
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