People can predict your tweets—even if you aren’t on Twitter

Science | AAAS | 1/21/2019 | Staff
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Companies have made billions of dollars by turning everything we say, do, and look at online into an experiment in consumer profiling. Recently, some users have had enough, curtailing their use of social media or deleting their accounts completely. But that’s no guarantee of privacy, according to a new study. If you can be linked to other users, their activity can expose you, too. Now, computer scientists have shown that the Twitter streams of your 10 closest contacts can predict your future tweets even better than your own stream.

“It’s much easier than it looks,” to figure out a person’s character from such second-hand surveillance, says David Garcia, a computational social scientist at the Medical University of Vienna in Austria who was not involved in the study.

Anyone - Tweets - Researchers - University - Vermont

Instead of predicting anyone’s actual tweets, researchers at the University of Vermont in Burlington estimated how predictable a person’s future words would be, using a measurement known as entropy. More entropy means more randomness and less repetition. They looked at the Twitter streams of 927 users, each of whom had 50 to 500 followers, as well as the 15 users each of them had tweeted at the most. In each individual’s stream, they calculated how much entropy the sequence of words contained. (On average, tweeters had more entropy than Ernest Hemingway, less than James Joyce.) They then plugged that number into a tool from information theory called Fano’s inequality to calculate how well a person’s stream could predict the first word in his or her next tweet. That upper bound on accuracy was, on average, 53%. But predicting each successive word is somewhat less accurate.

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“We used some very interesting mathematics from information theory to say: If you had the perfect machine...
(Excerpt) Read more at: Science | AAAS
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