The research is significant for India, as its headed for a digital and social media boom, with presence of at least 258.7 million social media networks, including 34.4 million users on Twitter alone by 2019-end, as per Statistica. (Bloomberg)
The research is significant for India, as its headed for a digital and social media boom, with presence of at least 258.7 million social media networks, including 34.4 million users on Twitter alone by 2019-end, as per Statistica. (Bloomberg)

Privacy on social media, your friends have a say too: study

  • A person can be accurately profiled based on social media information provided by his friends, the study shows
  • If a person leaves a social media platform or has never joined, the online posts and words of his social ties can provide about 95% of the potential predictive accuracy about his future activities, the study says

NEW DELHI: Amid rising concerns over data privacy, a new research has found that a person can be accurately profiled based on the social media information provided by his friends, irrespective of whether he himself is on social media platform or not.

According to scientists, if a person leaves a social media platform or has never joined, the online posts and words of his social ties can still provide about 95% of the ‘potential predictive accuracy of a person’s future activities even without any of that person’s data.

The findings were published in journal Nature Human Behavior on Monday.

“Our results have distinct privacy implications," the researchers write in the paper, “the 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 research is significant for India, as its headed for a digital and social media boom, with presence of at least 258.7 million social media networks, including 34.4 million users on Twitter alone by 2019-end, as per Statistica. The tremendous growth of social media has also raised concerns over data privacy, fueling demand for stringent data protection laws.

The team of mathematicians from the University of Vermont, USA and the University of Adelaide, Australia collated more than 30 million public posts on Twitter from as many as 13,905 users. As many as 927 networks were created, each had one user with 15 of his most frequently mentioned Twitter contacts.

The entire data was quantified using algorithms and inferences were made about the users which were based on the mathematical information contained in the text. Using information theoretic tools, researchers estimated that as few as 8-9 of an individual’s contacts are sufficient to obtain predictability through any machine learning method.

“You alone don’t control your privacy on social media platforms. Your friends have a say too," says Professor James Bagrow, mathematician from University of Vermont, USA who led the research, “so when you sign up for any social media platform, you are not only giving up your information, but your friends’ information too."

The study shows theoretically that a company or government can accurately profile a person- think political party, favourite products, religious commitments from their friends, even if they have never been on social media or deleted their existing account. “It makes little difference if the person is profiled, or whose behaviour is being predicted, is on or off that network when their friends are on the network," stated the study.

Measuring the flow of information on social media platforms, like Facebook and Twitter has also become critical, because it has become a powerful factor in the conduct of national elections.

“There’s no place to hide in a social network," says co-author Lewis Mitchell, a senior lecturer in applied mathematics at the University of Adelaide in Australia.

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