Algorithm able to identify online trolls
A trio of researchers, two from Cornell the other from Stanford has developed a computer algorithm that is capable of identifying antisocial behavior as demonstrated in website comment sections. In their paper uploaded to the preprint server arXiv, Justin Cheng, Cristian Danescu-Niculescu-Mizil and Jure Leskovec describe their algorithm, how they came up with it and how they plan to improve on its accuracy.
On the Internet, people who engage in antisocial ways in the comments section of web content, have come to be known as trolls, and they represent, like those who dish out spam, a constant source of annoyance—so much so that big name websites like CNN, are working with academics to find ways to identify trolls and ban them before they cause too much trouble. Visitors to web sites that are harassed or made to feel bad often avoid websites where they feel they have been angered or annoyed. In this new effort, the researchers built their troll finding algorithm by engaging in an analysis of typical troll behavior with data provided by CNN.com, Breitbart.com and IGN.com.