We already know how powerful techniques such as machine learning and sentiment analyis can be when it comes to deciphering consumer behavior online, and now it seems they can identify bullies, as well. A group of University of Wisconsin researchers have developed a machine learning algorithm that’s identifying more than 15,000 tweets per day relating to bullying — complete with loads of associated sociological insights — which begs the question of how to act on that data. How do you govern a social web that can be simultaneously a communication platform, a research lab full of unknowing subjects and a boiling-over pot of criminal evidence?

How the model works and what it found

In order to train their model, the researchers fed it two sets of tweets — one they had determined to be about bullying activity and another that was not. Once the model had learned the language identifiers…

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