BibSonomy publications for /tag/social-networkingMon Mar 28 12:15:19 CEST 2011New York, NY, USAProceedings of the 19th international conference on World wide web351--360WWW '10Privacy wizards for social networking sites2010policies privacy social-networking Privacy is an enormous problem in online social networking sites. While sites such as Facebook allow users fine-grained control over who can see their profiles, it is difficult for average users to specify this kind of detailed policy. In this paper, we propose a template for the design of a social networking privacy wizard. The intuition for the design comes from the observation that real users conceive their privacy preferences (which friends should be able to see which information) based on an implicit set of rules. Thus, with a limited amount of user input, it is usually possible to build a machine learning model that concisely describes a particular user's preferences, and then use this model to configure the user's privacy settings automatically. As an instance of this general framework, we have built a wizard based on an active learning paradigm called uncertainty sampling. The wizard iteratively asks the user to assign privacy "labels" to selected ("informative") friends, and it uses this input to construct a classifier, which can in turn be used to automatically assign privileges to the rest of the user's (unlabeled) friends. To evaluate our approach, we collected detailed privacy preference data from 45 real Facebook users. Our study revealed two important things. First, real users tend to conceive their privacy preferences in terms of communities, which can easily be extracted from a social network graph using existing techniques. Second, our active learning wizard, using communities as features, is able to recommend high-accuracy privacy settings using less user input than existing policy-specification tools.Mon Mar 28 12:13:00 CEST 2011New York, NY, USAProceedings of the 2nd ACM workshop on Security and artificial intelligence5--10AISec '09Inferring privacy policies for social networking services2009policies privacy social-networking Social networking sites have come under criticism for their poor privacy protection track record. Yet, there is an inherent difficulty in deciding which principals should have access to user's information or actions, without requiring them to constantly manage their privacy settings. We propose to extract automatically such privacy settings, based on the policy that information produced within a social context should remain in that social context, both to ensure privacy as well as maximising utility. A machine learning approach is used to extract automatically such social contexts, as well as a tentative evaluation.Mon Mar 28 11:28:22 CEST 2011New York, NY, USAProceedings of the 2010 EDBT/ICDT Workshops32:1--32:5EDBT '10Analysis of social networking privacy policies2010policies privacy social-networking As the use of social networks becomes more widespread and commonplace, users are beginning to question how their privacy is protected by social networks. In this paper, we review a privacy taxonomy for data storage polices and models and extend it to support social networking. We then apply the extended taxonomy to the privacy policies of six commonly used social networks, and present our findings with regards to how the published privacy policies of these social networks protect the privacy of users in reality.Thu Jun 18 09:12:22 CEST 2009Proceedings from the 2nd International Conference on Weblogs and Social Media (AAAI)A Large-Scale Study of MySpace: Observations and Implications for Online Social Networks2008dataset myspace social-networking web2.0 Thu Feb 05 08:39:57 CET 2009New York, NY, USACSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work167--170A face(book) in the crowd: social Searching vs. social browsing2006facebook search social social-networking study Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.Tue Dec 16 21:13:26 CET 2008New York, NY, USACSCW '08: Proceedings of the ACM 2008 conference on Computer supported cooperative work711--720Motivations for social networking at work2008collaboration social-networking The introduction of a social networking site inside of a large enterprise enables a new method of communication between colleagues, encouraging both personal and professional sharing inside the protected walls of a company intranet. Our analysis of user behavior and interviews presents the case that professionals use internal social networking to build stronger bonds with their weak ties and to reach out to employees they do not know. Their motivations in doing this include connecting on a personal level with coworkers, advancing their career with the company, and campaigning for their projects.Tue Nov 20 17:12:20 CET 2007KES (2)conf/kes/2006-2417-424Lecture Notes in Computer ScienceSocial Capital in Online Social Networks.42522006articles capital conferences dblp fulltext internet networking networks online openaccess proceedings social social-capital social-networking social-networks socialcapital socialnetworking socialnetworks web Sat Aug 18 13:22:24 CEST 2007MayCopied citations create renowned papers?2003bliography buzz cib citation citation-analysis citations doctors evolution fun humour impact-factor index influence key key-thought-leader keyopinionleader kol leader medical network networking networks no-tag opinion opinionleader otl persuasion physicians physicsandsociety professionals psychology publishing research science scientific social social-network social-networking social-networks socialnetworkanalysis socialnetworks statistics status thoughtleader Recently we discovered (<a href="/abs/cond-mat/0212043">cond-mat/0212043</a>) that the majority of scientific citations are copied from the lists of references used in other papers. Here we show that a model, in which a scientist picks three random papers, cites them,and also copies a quarter of their references accounts quantitatively for empirically observed citation distribution. Simple mathematical probability, not genius, can explain why some papers are cited a lot more than the other.Tue Apr 17 15:32:58 CEST 2007AugThe Structure of Collaborative Tagging Systems20050-student-shelaev 519 _unfiled annotation archive asc bibliography bookmark bookmarking bookmarks categorisation citation citations classification clustering colaborative collaborate collaboration collaborative collaborative-filtering collaborative-tagging collaborative_filtering collaborative_tagging collective commentary community computer-networks cooperation coordination core_periphery cscw datamining delicious digital dr_kim dynamic ecology emergence emergent eni ewa export farsi-media folksonomies folksonomy golder history huberman ict imt595 indexing information_organization interface learning leei library20 linguistics metadata model network_analysis networkdynamics networking networks newmedia no-tag ontology overview p2p patterns properties quantitative references research retrieve scale-free search self-organization semantics semiotics shelaev-project sigcr2006 social social-bookmarking social-netowrks social-network social-networking social-networks social-space social-tagging social_networks social_software socialbookmarking socialnets socialnetwork socialnetworking socialsearch socialsoftware socialtagging sociology software structure Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.social-networkingCommunity for tag(s) social-networking