Identify user variants based on user behavior on social media

In social media, users are allowed to express their opinions by commenting on an item or rating an item with scores. The collection of user reviews would generate a positive or negative influence to the media audience. Some malicious users may create multiple variant accounts on the same social media so as to influence or manipulate public opinions for business or criminal purposes. To maintain good social environment, it is necessary to find those fake users. In this paper, we investigate the user variants identification problem using both user behavior and item related information. We study the characteristics of user behaviors on social media and introduce two concepts visibility and distingushibility to preliminarily quantify whether a fake user can be identified. To better understand user intention and characteristics, we profile a user with apparent and implicit features, which are extracted from three aspects: User Generated Contents (UGC), user behavior context and item information. Based on these features, we propose the user Variants Identification Problem (VIP) and an identification algorithm, which finds the top-k similar variants in a social media. We evaluate our methods against two real datasets MovieLens and Amazon and make comparison on the effectiveness against different features in identifying user variants.