A detection system to counter identity deception in social media applications

Considering the current landscape of the internet where there is a plethora of social networking sites and collaborative websites like Wikipedia concern about malicious users keeping multiple accounts is of prime importance. Most of the collaborative sites allow users to easily create an account and start accessing the content. Social media services such as collaborative project's single user constantly creates many accounts with different account names not long after a block has been applied. The blocked person who creates multiple accounts is called sockpuppet. Current mechanism for detecting deception are based on human deception detection (e.g., speech or text). Although these method have high detection accuracy, but it cannot be applied in databases with large volumes of data. So they are computationally inefficient. There is an efficient method for detecting identity deception by using both Nonverbal (e.g., user activity or Movement) and Verbal Behavior (facial expression, text) in the social media environment. These methods increase high detection accuracy. Post examination and close monitoring on these methods which finds out that it can be applied to any social media environment.