Scientists have developed an algorithm-based system that identifies telltale signs in fake news stories could provide news aggregator and social media sites like Google News with a new weapon in the fight against misinformation.
Researchers from the University of Michigan in the US who developed the system have demonstrated that it is comparable to and sometimes better than humans at correctly identifying fake news stories.
In a recent study, it successfully found fakes up to 76 per cent of the time, compared to a human success rate of 70 per cent.
In addition, their linguistic analysis approach could be used to identify fake news articles that are too new to be debunked by cross-referencing their facts with other stories.
Rada Mihalcea, professor at University of Michigan, said an automated solution could be an important tool for sites that are struggling to deal with an onslaught of fake news stories, often created to generate clicks or to manipulate public opinion.
Catching fake stories before they have real consequences can be difficult, as aggregator and social media sites today rely heavily on human editors who often can’t keep up with the influx of news.
In addition, current debunking techniques often depend on external verification of facts, which can be difficult with the newest stories. Often, by the time a story is proven a fake, the damage has already been done.
Linguistic analysis takes a different approach, analysing quantifiable attributes like grammatical structure, word choice, punctuation and complexity. It works faster than humans and it can be used with a variety of different news types.
“You can imagine any number of applications for this on the front or back end of a news or social media site,” Mihalcea said.
“It could provide users with an estimate of the trustworthiness of individual stories or a whole news site,” she said.
“Or it could be a first line of defense on the back end of a news site, flagging suspicious stories for further review. A 76 per cent success rate leaves a fairly large margin of error, but it can still provide valuable insight when it’s used alongside humans,” she added.
Fake news appears and disappears quickly, which makes it difficult to collect. It also comes in many genres, further complicating the collection process.
The team created its own data, crowdsourcing an online team that reverse-engineered verified genuine news stories into fakes.
This is how most actual fake news is created, Mihalcea said, by individuals who quickly write them in return for a monetary reward.
Study participants were paid to turn short, actual news stories into similar but fake news items, mimicking the journalistic style of the articles.
At the end of the process, the research team had a dataset of 500 real and fake news stories.
They then fed these labelled pairs of stories to an algorithm that performed a linguistic analysis, teaching itself distinguish between real and fake news.
Finally, the team turned the algorithms to a dataset of real and fake news pulled directly from the web, netting the 76 per cent success rate.
The details of the new system and the dataset that the team used to build it are freely available, and Mihalcea said they could be used by news sites or other entities to build their own fake news detection systems.
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