In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.
To the best of our knowledge, the first attempt to match user identities was proposed in @cite_32 . In that paper, the authors performed an in-depth analysis of 25 adaptive systems with the goal of identifying what user profile attributes were frequently recurrent in each system; the outcome of such an analysis led to isolate attributes like username, name, location and e-mail address.
@cite_32
2147734964 2802482737 2026910242 2107961038
Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) while using same or different email addresses. In this work, we address the problem of matching user profiles in its globality by providing a suitable matching framework able to consider all the profile’s attributes. Our framework allows users to give more importance to some attributes and assign each attribute a different similarity measure. The set of experiments conducted with our default recommended attribute similarity measures shows the superiority of our proposal in comparison with current ones. The existence of user profiles belonging to a single user across different social networking sites poses several challenges to the research community. The main technical issue in that context is to detect the same user profiles across several different social networks by leveraging a set of mechanisms that identify the similarity among the user profiles. This problem is commonly referred to as entity matching or identity linkage on social networks. In this review, we describe and compare the 27 most important (to the best of our knowledge) research papers in this area. The main contributions of this article are to provide a systematic and integrated review of papers in this area, to provide comparative points that simplify the understanding of such systems, and finally to discuss future research avenues. Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has showed that it is possible to accurately match profiles, but their evaluation focused only on sampled datasets. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks, by exploiting public attributes, i.e., information users publicly provide about themselves. Today's social networks have hundreds of millions of users, which brings completely new challenges as a reliable matching scheme must identify the correct matching profile out of the millions of possible profiles. We first define a set of properties for profile attributes--Availability, Consistency, non-Impersonability, and Discriminability (ACID)--that are both necessary and sufficient to determine the reliability of a matching scheme. Using these properties, we propose a method to evaluate the accuracy of matching schemes in real practical cases. Our results show that the accuracy in practice is significantly lower than the one reported in prior literature. When considering entire social networks, there is a non-negligible number of profiles that belong to different users but have similar attributes, which leads to many false matches. Our paper sheds light on the limits of matching profiles in the real world and illustrates the correct methodology to evaluate matching schemes in realistic scenarios. User attributes, such as occupation, education, and location, are important for many applications. In this paper, we study the problem of profiling user attributes in social network. To capture the correlation between attributes and social connections, we present a new insight that social connections are discriminatively correlated with attributes via a hidden factor -- relationship type. For example, a user's colleagues are more likely to share the same employer with him than other friends. Based on the insight, we propose to co-profile users' attributes and relationship types of their connections. To achieve co-profiling, we develop an efficient algorithm based on an optimization framework. Our algorithm captures our insight effectively. It iteratively profiles attributes by propagation via certain types of connections, and profiles types of connections based on attributes and the network structure. We conduct extensive experiments to evaluate our algorithm. The results show that our algorithm profiles various attributes accurately, which improves the state-of-the-art methods by 12 .