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Community sites offer greater learning opportunities to users than search engines. One of the essential factors provides learning opportunities to users in community sites is anonymous submission. This is because anonymity gives users chances to submit messages (questions, problems, answers, opinions, etc.) without regard to shame and reputation. However, some users abuse the anonymity and disrupt communications in a community site. For example, some users pretend to be other users by using multiple user accounts and attempt to manipulate communications in the community site. Manipulated communications discourage message submitters, keep users from retrieving good communication records, and decrease the credibility of the communication site. To solve this problem, we conducted an experimental study to detect submitters suspected of pretending to be someone else to manipulate communications in a community site by using machine learning techniques. In this study, we used messages in the data of Yahoo! chiebukuro for data training and examination.

DETECTION OF SUBMITTERS SUSPECTED OF PRETENDING TO BE SOMEONE ELSE TO MANIPULATE COMMUNICATIONS IN A COMMUNITY SITE