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Trolls, morons, and bots plaster toxic crap all over Twitter and other antisocial networks. Can machine learning help clean it up?
A team of computer scientists spanning the globe think so. They've built a neural network that can seemingly classify tweets into four different categories: normal, aggressor, spam, and bully – aggressor being a deliberately harmful, derogatory, or offensive tweet; and bully being a belittling or hostile message. The aim is to create a system that can filter out aggressive and bullying tweets, delete spam, and allow normal tweets through. Pretty straight forward.
The boffins admit it's difficult to draw a line between so-called cyber-aggression and cyber-bullying. And the line between normal and aggressive tweets is often blurred: after all, people enjoy ranting about things, from feminism to Brexit to tabs-versus-spaces. Having said that, the goal was to craft a system that can automatically and fairly – and by fairly, we mean consistently – draw a line between each category.
After analyzing more than two million tweets that discussed touchy topics such as Gamergate and gender pay inequality at the BBC, as well as more neutral matters like the NBA, the eggheads selected a sample containing 9,484 tweets, and hand labelled them as normal, aggressor, spam, and bully. Obviously, this means the academics' definition of what is aggressive or bullying forms the basis of the model.
About 80 per cent of these tweets were used to train the recurrent neural network, and the remaining 20 or so per cent was used to test it, according to one of the scientists: Jeremy Blackburn, an assistant computer science professor at Binghamton University in New York. We're told the code could sort the test tweets into the fo ..
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