OfTheCross
Veteran
New research, however, shows that this argument doesn’t truly hold water. Researchers from the University of Vermont and the University of Adelaide show that information gathered from users on social media platforms can be used to reliably predict another user’s later tweets. Furthermore, even if this latter user leaves the platform — or never joined in the first place — data mined from social media can be used to predict their future activities.
Mitchell and his team gathered over thirty million public posts on Twitter from 13,905 different users/accounts, clumping them in networks of 15 users. Then, using messages sent to and from as few as 8 or 9 of a certain user’s contacts (this user is named the “ego” and the contact its “alters”), they proved that they can predict the content and wording of that user’s later tweets with an impressive degree of accuracy — higher even that when the team drew on a user’s own past Tweets.
One disturbing finding the team reports on is that “there is so much social information that an entity with access to all social media data will have only slightly more potential predictive accuracy (~64% in our case) than an entity that has access to the activities of an ego’s alters but not to those of that ego (~61%).”
In other words, if you can access the alters’ data, you can predict what the ego is going to say/do roughly as reliably as if you had access to the ego’s data itself. The online posts of the alters provide about 95% of the “potential predictive accuracy” of a person’s future activities, the team writes.
When you sign up for Facebook or another social media platform “you think you’re giving up your information, but you’re giving up your friends’ information too!” says lead author Prof. James Bagrow, a mathematician at the University of Vermont.
Remarkably, this doesn’t really disappear if you delete your account.
The public consciousness is very interested in protecting their online privacy, especially after debacles such as the one involving Cambridge Analytica. Bagrow’s team showed that this concern is valid, and, although there is a mathematical upper limit on how much predictive information a social network can hold, it’s not limited to its users alone.
The paper “Information flow reveals prediction limits in online social activity” has been published in the journal Nature Human Behavior.