Researchers looked at publicly available Twitter posts from almost 14,000 users
Algorithms accurately predicted 64% of the time what they were going to say
If the user didn’t have an account, that percentage dropped to 61% accuracy
Showed that sites can infer data about users by looking at their friends’ posts
A new study has found that social media sites including Facebook and Twitter can learn a shocking amount of information about users, even if they don’t have an account.
Researchers from the University of Vermont discovered that these platforms only need access to eight of your one-time contacts in order to infer information about you.
It comes as Silicon Valley giants face increased scrutiny about their data collection practices and whether users have enough control over their private information.
‘You alone don’t control your privacy on social media platforms,’ said Jim Bagrow, a mathematician at the University of Vermont who led the research published in the journal Nature Human Behavior.
‘Your friends have a say too.’
Bagrow and his team used statistical models to analyse data from more than 30 million publicly available Twitter posts by almost 14,000 users.
Although the study focused on Twitter, the same information could be gathered form posts on other social media, like Facebook, provided access to them, Bagrow said.
They found that machine learning algorithms may be able to infer with up to 64 percent accuracy what word a user was most likely to write next, based on what he and the people he interacted most often with had previously published.
Accuracy levels dropped only three percent to 61 percent when the algorithms were fed with text posted only by friends, according to the study.
If a user doesn’t have an account, the algorithm can draw information from up to 8 or 9 of an individual’s contacts to predict the user’s behavior, the study found.
The researchers believe content posted from a user’s friends provides about 95 percent of the ‘potential predictive accuracy’ needed to obtain information about a person, ‘without requiring the individual’s data.’
‘There’s no place to hide in a social network,’ study co-author Lewis Mitchell said in a statement.
From political affiliation to purchasing practices and favorite television series, information shared online by friends and contacts could potentially be used to deduce many aspects of a person’s life, Bagrow said.
‘Information is so strongly embedded in a social network that, in principle, one can profile an individual from their available social ties even when the individual forgoes the platform completely,’ the researchers wrote in the study.
Twitter declined to comment. Its global data protection officer Damien Kieran told the U.S. Congress in September the company believed privacy was a fundamental right.