Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks

Abstract

The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online Social Media Network (SMN) platforms. Recognizing anonymous, yet identical users among multiple SMNs is still an intractable problem. Clearly, cross-platform exploration may help solve many problems in social computing in both theory and applications. Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users' public profiles, are fragile. Some studies have attempted to match users based on the location and timing of user content as well as writing style. However, the locations are sparse in the majority of SMNs, and writing style is difficult to discern from the short sentences of leading SMNs such as Sina Microblog and Twitter. Moreover, since online SMNs are quite symmetric, existing user identification schemes based on network structure are not effective. The real-world friend cycle is highly individual and virtually no two users share a congruent friend cycle. Therefore, it is more accurate to use a friendship structure to analyze cross-platform SMNs. Since identical users tend to set up partial similar friendship structures in different SMNs, we proposed the Friend Relationship-Based User Identification (FRUI) algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and only UMPs with top ranks are considered as identical users. We also developed two propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better than current network structure-based algorithms.

Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

“…After constructing the multiplex networks, we use the FRUI [36], NS [78], and INOE [37] as the baselines for the experiments, where FRUI is the closest to the IDP algorithm for the interlayer link prediction as a state of the art. Each experiment is repeated 500 times.…”

“…Narayanan and Shmatikov [60] assumed that a natural person usually has a similar social network in the virtual world. Based on this assumption, Zhou et al [36] developed a network structure-based method known as the Friend pairs as belonging to the same person or not. The feature vectors of the classifier were constructed by profile information, descriptions of interests, and friend lists.…”

“…Three main approaches are available for interlayer link prediction in the multiplex social network: (i) feature-based interlayer link prediction, (ii) network-based interlayer link prediction, and (iii) a combination of multiple approaches. Among these, network-based methods have been applied extensively in recent years [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. Its strategies for predicting interlayer links rely on examining the structures of social graphs on different OSN platforms [32].…”

Exploring Identical Users on GitHub and Stack Overflow

“…They consider all attributes equally, however, this paper distinguishes attributes and classifiers take different importances for different combinations of attributes. Zhou et al [17] have also studied user identification basically on social media network platforms like Twitter 9 , Sina Microblog 10 , Facebook 11 , and RenRen 12 . They rely on topologies of social media networks, so their approach is not much applicable to datasets on GitHub and Stack Overflow.…”

An Overview on User Profiling in Online Social Networks

“…Since there is no connection or restriction for users to join different sites, an user can be involved in many or all social networking sites available. This may be useful in connecting to different kind of people, which may sometimes become danger for society [35] - [51].…”