User data distributed on the social web: how to identify users on different social systems and collecting data about them

This paper presents an approach to uniquely identify users and to retrieve their data distributed in profiles stored in different systems. The objective is exploiting the public user data available in the Web and especially in social networks. The approach does not require the implementation of specific protocols and the provision of authentication data. The evaluation provides good results that encourage us in carrying on the extension of the project. The extension we are working on is aimed at aggregating, using heuristic techniques, the data stored in the retrieved profiles and at inferring new data about the user

User data discovery and aggregation: the CS-UDD algorithm

In the social web, people use social systems for sharing content and opinions, for communicating with friends, for tagging, etc. People usually have different accounts and different profiles on all of these systems. Several tools for user data aggregation and people search have been developed and protocols and standards for data portability have been defined. This paper presents an approach and an algorithm, named Cross-System User Data Discovery (CS-UDD), to retrieve and aggregate user data distributed on social websites. It is designed to crawl websites, retrieve profiles that may belong to the searched user, correlate them, aggregate the discovered data and return them to the searcher which may, for example, be an adaptive system. The user attributes retrieved, namely attribute-value pairs, are associated with a certainty factor that expresses the confidence that they are true for the searched user. To test the algorithm, we ran it on two popular social networks, MySpace and Flickr. The evaluation has demonstrated the ability of the CS-UDD algorithm to discover unknown user attributes and has revealed high precision of the discovered attributes