Algorithms reveal forecasting power of tweets
Sang Won Yoon had a good Chinese meal recently — not always easy in America. It’s on his mind.
Imagine, he says, that you and your co-workers plan via social media to head for lunch about 12:30 p.m. most Thursdays. Usually that Italian place downtown. Frequently tweet about traffic on the way.
Now imagine that at 10 a.m., you’re tweeted a coupon from the Chinese place near the Italian joint — and directions around a traffic jam that will start in about 90 minutes. Score one Sichuan hot pot.
Yoon can make that happen. He and fellow Binghamton University systems scientist Sarah Lam have been working with Binghamton alumnus Nathan Gnanasambandam, a senior researcher at the Palo Alto Research Center (PARC), a division of Xerox Research. They used 500 million tweets to develop algorithms that not only paint a picture of everyday human dynamics, but can predict an individual’s behavior hours in advance. The team, which also included graduate students Keith Thompson and Bichen Zheng, recently published their findings in Industrial Engineer.
Think about what your typical social media post says about you: when you posted, where you were. Your networking relationships can be learned — and with context-based algorithms like those PARC and Binghamton University have developed — what you plan. They use what is called an artificial neural network.
How sure are they? Better than 90 percent for a typical social media user in a three-hour horizon. “If you look at the picture, it’s very static. But the individuals are all over the place,” Yoon says.