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.
In @cite_18 the system Mypes is presented. Mypes supports the linkage, aggregation, alignment and semantic enrichment of user profiles available in various Social Web systems, such as Flickr, Delicious and Facebook.
@cite_18
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In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start. This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 . This paper presents an innovative approach for the publication and discovery of Web services. The proposal is based on two previous works: DIRE (DIstributed REgistry), for the user-centered distributed replication of service-related information, and URBE (UDDI Registry By Example), for the semantic-aware match making between requests and available services. The integrated view also exploits USQL (Unified Service Query Language) to provide users with a higher level and homogeneous means to interact with the different registries. The proposal improves background technology in different ways: we integrate USQL as high-level language to state service requests, widen user notifications based on URBE semantic matching, and apply URBE match making to all the facets with which services can be described in DIRE. All these new concepts are demonstrated on a simple scenario. Understanding the privacy implication of adopting a certain privacy setting is a complex task for the users of social network systems. Users need tool support to articulate potential access scenarios and perform policy analysis. Such a need is particularly acute for Facebook-style Social Network Systems (FSNSs), in which semantically rich topology-based policies are used for access control. In this work, we develop a prototypical tool for Reflective Policy Assessment (RPA) --- a process in which a user examines her profile from the viewpoint of another user in her extended neighbourhood in the social graph. We verify the utility and usability of our tool in a within-subject user study.