Mining students’ social media posts for signs of trouble

There’s little doubt that students share information on social media school administrators might find useful. There is some debate over whether—or how—it can be accurately or ethically extracted by software.

Blake Prewitt, superintendent of Lakeview school district in Battle Creek, Michigan, says he typically wakes up each morning to twenty new emails from a social media monitoring system the district activated earlier this year. It uses keywords and machine learning algorithms to flag public posts on Twitter and other networks that contain language or images that may suggest conflict or violence, and tag or mention district schools or communities.

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