Using AI to Automate the News

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Using AI to Automate the News

Robo-journalism is on the rise. Check out how certain parts of the journalist's job is being automated by artificial intelligence.

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A few years ago I touched upon the growing trend of automation in journalism, with startups such as Narrative Science leading the way in autonomously producing things, such as stock updates and sports results.

This isn't the only part of the journalist's job that has been automated, however. A recent paper outlines how Reuters uses AI to identify breaking news stories.

Tracing the News

The system Reuters use is called Tracer, and it autonomously crawls the web to identify breaking stories. The system relies on a combination of data mining and machine learning to identify what it believes to be the most important and relevant events, before then categorizing them into topics and ranking them by importance. It then autonomously creates a headline and a summary for them, before distributing them around the newswire.

The system first examines around 12 million tweets per day, which represents around 2% of the total volume. These are a combination of tweets from carefully curated accounts and a random sample of the general public.

Tracer then identifies when the topical event has occurred by analyzing the Twitter stream to spot when a single event is being spoken about by multiple people at once.

Suffice to say, this uncovers a lot of rubbish, so it next tries to classify the events that they identify and rank them in order of importance. The system is also capable of identifying the location of the event.

Determining Authenticity

The final stage before the summary is written is to determine the veracity of the story. Tracer first looks for the earliest mention of the topic on Twitter, and any sites that are linked to in those mentions. It cross-references this against a database of known fake news websites, together with satirical sites such as The Onion.

If they're happy with the authenticity of the story, then Tracer will produce a headline and summary based on its findings. The paper says that the system performed well during initial trials, both in its ability to provide timely analysis, but also a reliable commentary on the breaking news of the day.

Will the system eventually replace the 2,500 journalists who currently generate the 3,000 news alerts Reuters produce each day? It's too early to say, but the experiment does nonetheless highlight the potential for technology to do significant pieces of work.


With the rise in robo-journalism, it's perhaps not surprising that thought has been given to how people perceive stories written by machines.

The researchers presented several hundred participants with an article on one of a range of topics. Half of the participants were told the article was automatically generated, with the other half told it was written by a human journalist.

Interestingly, our preference for automation seemed to differ depending on the topic of the article. Readers preferred the robot when the article was about finance, but the human when the topic was healthcare.

"It seems that we might not be as comfortable with robots delivering news related to health," the authors say. "We suspect that this was because of an 'eeriness' or a creepy feeling the participants felt, and our results backed this up."

Time will tell just how far Tracer like applications go, and indeed how the public will respond to them. It is undoubtedly another aspect of an increasingly interesting trend, however.

ai, automation, autonomy, classification, data mining, machine learning, twitter

Published at DZone with permission of Adi Gaskell , DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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