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.
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A new study has found that social media sites like Facebook and Twitter can learn a shocking amount of information about users, even if they don't have an account
'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.'
For the study, researchers used statistical models to analyse data from more than 30 million publicly available Twitter posts by almost 14,000 users. The algorithm looked at each ego, or user, and calculated an 'entropy rate,' or a prediction of their next words using previous ones
'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.
From political affiliation to favorite television series, information shared online by friends and contacts could potentially be used to deduce many aspects of a person's life, the study found
Facebook, which tailors content and ads based on user activity, said it does not create profiles about non-Facebook users.
Both Twitter and Facebook allow users to control and delete data and information related to their accounts.
Last year Facebook, the world's largest social network, was buffeted by revelations that British consultancy Cambridge Analytica had improperly acquired data on millions of its U.S. users to target election advertising.
