Detecting multiple userids on Korean social media for mining TV audience response

Possession of multiple userids by a single user happens when more than two userids actually belong to the same user. In analysis of audience response of TV program, it is so important to detect these multi-id users because they often use the multiple ids to manipulate audience response or to take illegal profits. Detecting multiple userids of a single user has similiar nature with authorship attribution in terms of identifying authorships for given arbitrary texts. The conventional supervised techniques for authorship attribution, however, are difficult to be employed directly to the problem of multiple userids detection. This is because we do not know real authors and multiple userids may belong to the same author in the task of multiple userids detection. In addition, since we can not have all authors in advance, userids can not be treated as classes. This paper proposes a method of learning the element-wise differences between multiple userids. Each userid is represented as a feature vector from their postings on web social media. Then the similarity vector between two userid vectors can be obtained by performing their element-wise difference. With the similarity vectors, we train the similarity patterns for detecting if multiple userids belong to the same user or not. In order to solve the problem successfully, we present six features which are effective for Korean social media. We conducted comprehensive experiments on the Korean social media dataset. The experimental results show that the proposed similarity learning method with all presented features is successful for detecting multiple userids on Korean social media.