Twitter bots spread misinformation in 2016 US presidential election: Study

These accounts were also responsible for 34 per cent of all articles shared from "low-credibility" sources, according to the study

Automated Twitter accounts or "bots" played a disproportionate role in spreading misinformation online during the 2016 US presidential election, according to a study.

The research, published in the journal Nature Communications, analysed 14 million messages and 400,000 articles shared on Twitter between May 2016 and March 2017.

The period spans the end of the 2016 presidential primaries and the presidential inauguration on January 20, 2017.

Researchers from Indiana University in the US found that a mere six per cent of Twitter accounts that the study identified as bots were enough to spread 31 per cent of the "low-credibility" information on the network.

These accounts were also responsible for 34 per cent of all articles shared from "low-credibility" sources, they found.

The study also found that bots played a major role promoting low-credibility content in the first few moments before a story goes viral.

The brief length of this time -- 2 to 10 seconds -- highlights the challenges of countering the spread of misinformation online, researchers said.

Similar issues are seen in other complex environments like the stock market, where serious problems can arise in mere moments due to the impact of high-frequency trading.

"This study finds that bots significantly contribute to the spread of misinformation online -- as well as shows how quickly these messages can spread," said Filippo Menczer, a professor at Indiana University.

The analysis also revealed that bots amplify a message's volume and visibility until it's more likely to be shared broadly -- despite only representing a small fraction of the accounts that spread viral messages.

"People tend to put greater trust in messages that appear to originate from many people," said Giovanni Luca Ciampaglia, an assistant research scientist at Indiana University.

"Bots prey upon this trust by making messages seem so popular that real people are tricked into spreading their messages for them," Ciampaglia said.

Information sources labelled as low-credibility in the study were identified based upon their appearance on lists produced by independent third-party organisations of outlets that regularly share false or misleading information.

These sources -- such as websites with misleading names like "USAToday.com.co" -- include outlets with both right- and left-leaning points of view, researchers said.

They also identified other tactics for spreading misinformation with Twitter bots.

These included amplifying a single tweet -- potentially controlled by a human operator --across hundreds of automated retweets; repeating links in recurring posts; and targeting highly influential accounts.

For instance, the study cites a case in which a single account mentioned @realDonaldTrump in 19 separate messages about millions of illegal immigrants casting votes in the presidential election -- a false claim that was also a major administration talking point.

The researchers also ran an experiment inside a simulated version of Twitter.

They found that the deletion of 10 per cent of the accounts in the system -- based on their likelihood to be bots -- resulted in a major drop in the number of stories from low-credibility sources in the network.

"This experiment suggests that the elimination of bots from social networks would significantly reduce the amount of misinformation on these networks," Menczer said.