Detection of Users Suspected of Pretending to Be Other Users in a Community Site by Using Messages Submitted to Non-Target Categories

Some users abuse the anonymity and disrupt communications in a community site. Authorship identification based on analyzing stylistic features of messages is effective in detecting these inadequate users. However, in this method, the scope of target users was often limited because the criteria for selecting learning examples were strict. To relax the criteria and extend the scope of target users, we propose a method of detecting users suspected of pretending to be other users in a certain category of a community site by using their messages submitted to other (non-target) categories. Also, we show the accuracy of user identification when we relax the criteria of selecting learning examples and extend the scope of target users.