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Gatekeeping theory refers to the control of information as it passes through a gate (Shoemaker & Vos, 2009). The gate is guarded by gatekeepers, who make decisions about what information [...]

Abstract Chapter 6 of Library Technology Reports (vol. 48, no. 6) 'Running the Digital Branch: Guidelines for Operating the Library Website' by David Lee King discusses the use of social [...]

Recently, online social networks have emerged that allow people to share their multimedia files, retrieve interesting content, and discover like-minded people. These systems often provide the possibility to annotate the content with tags and ratings. Using a random walk through the social annotation graph, we have combined these annotations into a retrieval model that effectively balances the personal preferences and opinions of like-minded users into a single relevance ranking for either content, tags, or people. We use this model to identify the influence of different annotation methods and system design aspects on common ranking tasks in social content systems. Our results show that a combination of rating and tagging information can improve tasks like search and recommendation. The optimal influence of both sources on the ranking is highly dependent on the retrieval task and system design. Results on content search and tag suggestion indicate that the profile created by a user's annotations can be used effectively to adapt the ranking to personal preferences. The random walk reduces sparsity problems by smoothly integrating indirectly related concepts in the relevance ranking, which is especially valuable for cold-start users or individual tagging systems like YouTube and Flickr. Categories and Subject Descriptors: H.3.5 [Information Storage and Retrieval]: Online Information Services; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--Information filtering; Search process General Terms: Algorithms, Experimentation, Human Factors Additional Key Words and Phrases: Personalization, content retrieval, recommendation, social media, random walk, rating, tagging, librarything, movielens DOI = 10.1145/1852102.1852107

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.clsr.2010.07.006 Byline: Patrick Van Eecke (a)(b), Maarten Truyens (b) Abstract: This article discusses the complex relationship between social networks and the EU Data Protection Directive (95/46/EC). After a concise introduction to the general privacy impact of social networks, it discusses how the Directive applies to users and operators of social networks and social network applications. Particular attention is drawn to the scope of the Directive (including the 'household' exception), the obligations imposed on data controllers, the interpretation of the Directive by Working Party 29, as well as the difficulties that are encountered when applying the aging Directive to the technological reality of today's social networks. Author Affiliation: (a) University of Antwerp, Antwerp, Belgium (b) Intellectual property and technology department, DLA Piper UK LLP, Brussels, Belgium

Social web platforms succeed on if the attract the right individuals and motivate them to act the right way. Online reputation systems empower the contributors to know and trust others in the network. Insights on the four aims into designing the online reputation systems are also given.

The Department of Defense (DoD) acquisition workforce is reaching a point at which change--rapid, relentless change-will become the norm. The global marketplace and the billions of consumers that make up [...]

Customers are increasingly taking control of how a product or service is being viewed with the use of online social networks. Companies can deal with this topic by asking for their ideas regarding future products and interacting with them through online support forums. Other details on businesses' use of social applications are discussed.

Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing vast amounts of information. Here we collect data from a popular system and investigate the statistical properties of tag cooccurrence. We introduce a stochastic model of user behavior embodying two main aspects of collaborative tagging: (i) a frequency-bias mechanism related to the idea that users are exposed to each other's tagging activity; (ii) a notion of memory, or aging of resources, in the form of a heavy-tailed access to the past state of the system. Remarkably, our simple modeling is able to account quantitatively for the observed experimental features with a surprisingly high accuracy. This points in the direction of a universal behavior of users who, despite the complexity of their own cognitive processes and the uncoordinated and selfish nature of their tagging activity, appear to follow simple activity patterns. online social communities | statistical physics | social bookmarking | information dynamics