MIT study finds fake news travels faster
Fake news spreads more rapidly on Twitter than real news does – and by a substantial margin, three scholars at the Massachusetts Institute of Technology (MIT) have found.
The study found that false news stories are 70 per cent more likely to be retweeted than true stories are.
It also takes true stories about six times as long to reach 1,500 people as it does for false stories to reach the same number of people.
The research project also discovered that humans, not bots, are primarily responsible for spreading misleading information.
"When we removed all of the bots in our dataset, [the] differences between the spread of false and true news stood," says Soroush Vosoughi, a co-author of the new paper and a postdoc at LSM whose PhD research helped give rise to the current study.
Sinan Aral, a professor at the MIT Sloan School of Management and co-author of the new paper, added: "We found that falsehood defuses significantly farther, faster, deeper, and more broadly than the truth, in all categories of information, and in many cases by an order of magnitude.
"False news is more novel, and people are more likely to share novel information."
To conduct the study, the researchers tracked roughly 126,000 cascades of news stories spreading on Twitter, which were cumulatively tweeted over 4.5 million times by about three million people, from the years 2006 to 2017.
Of the 126,000 cascades, politics comprised the biggest news category, with about 45,000, followed by urban legends, business, terrorism, science, entertainment, and natural disasters.
The spread of false stories was more pronounced for political news than for news in the other categories.
Deb Roy, an associate professor of media arts and sciences at the MIT Media Lab and director of the Media Lab's Laboratory for Social Machines (LSM), said: "These findings shed new light on fundamental aspects of our online communication ecosystem."
She added that the researches were "somewhere between surprised and stunned" at the different trajectories of true and false news on Twitter.