Identifying Fake Profiles in LinkedIn

Social networks have become an everyday tool in our lives and different social networks have different target groups. Among them LinkedIn is greatly preferred by the people who are in the professional occupations. With the rapid growth of social networks, people tend to misuse them for unethical and illegal conducts. Creation of a fake profile becomes such adversary effect which is difficult to identify without apt research. The current solutions that have been practically developed and theorized to solve this contention, primarily considered the characteristics and the social network ties of the user’s social profile. However, when it comes to LinkedIn such behavioral observations are highly restrictive in publicly available profile data for the users by the privacy policies. The limited publicly available profile data of LinkedIn makes it ineligible in applying the existing approaches in fake profile identification. Therefore, there is a need to conduct targeted research on identifying approaches for fake profile identification in LinkedIn. In this research, we identify the minimal set of profile data that are necessary for identifying fake profiles in LinkedIn and identify the appropriate data mining approach for such task. We demonstrate that with limited profile data our approach can identify the fake profile with 84% accuracy and only 2.44% false negative, which is comparable to the results obtained by other existing approaches based on the larger data set and more profile information.