Identifying Fake Profiles in LinkedIn
This addresses the issue of fake profiles undermining trust and causing inefficiencies for organizations and users on LinkedIn, representing an incremental improvement over existing methods.
The paper tackled the problem of identifying fake profiles on LinkedIn by determining the minimal set of profile data needed and proposing a data mining approach, achieving 87% accuracy and 94% True Negative Rate, which is comparable to methods using more data and improves accuracy by about 14% over similar approaches.
As organizations increasingly rely on professionally oriented networks such as LinkedIn (the largest such social network) for building business connections, there is increasing value in having one's profile noticed within the network. As this value increases, so does the temptation to misuse the network for unethical purposes. Fake profiles have an adverse effect on the trustworthiness of the network as a whole, and can represent significant costs in time and effort in building a connection based on fake information. Unfortunately, fake profiles are difficult to identify. Approaches have been proposed for some social networks; however, these generally rely on data that are not publicly available for LinkedIn profiles. In this research, we identify the minimal set of profile data necessary for identifying fake profiles in LinkedIn, and propose an appropriate data mining approach for fake profile identification. We demonstrate that, even with limited profile data, our approach can identify fake profiles with 87% accuracy and 94% True Negative Rate, which is comparable to the results obtained based on larger data sets and more expansive profile information. Further, when compared to approaches using similar amounts and types of data, our method provides an improvement of approximately 14% accuracy.