article_snippet.sent: Researchers from Stanford and Cornell University recently studied 40 million posts made by 1.7 million web users to try to identify trolls.
article_snippet.sent: From this data Researchers from Stanford and Cornell University were able identify so-called Future-Banned Users (FBUs) - and Never-Banned Users (NBUs).
article_snippet.sent: The former are people that are more likely to exhibit antisocial behaviour on online forums, while the latter are everyday users that don't show signs of troll-like behaviour.
article_snippet.sent: Researchers from Stanford and Cornell University then built an algorithm that scans posts for signs of such antisocial behaviour, and a study shows an algorithm that scans posts for signs of such antisocial behaviour can identify potential trolls in 80% of cases.
article_snippet.sent: For example, posts made by  trolls are less readable.
article_snippet.sent: Trolls are more likely to veer off-topic and have fewer similarities in terms of language and content compared to other posts.
article_snippet.sent: Trolls are also less likely to use positive words than other users and Trolls swear more.
article_snippet.sent: In terms of activity, a study found that trolls make more comments each day, and post more times on each thread.
article_snippet.sent: Trolls were also more likely to have had posts deleted than other users.