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Using Social Semantic Web Data for Privacy Policies

Bachelorarbeit 2009 72 Seiten

Informatik - Angewandte Informatik


In the last years the web underwent a drastic shift from a static, centralised information system to a dynamic, user-generated, distributed and open platform, and users changed from passive consumers to active participants, interacting, creating and sharing content. This 'new' web is called Web 2.0. In the era of this movement new Social Web applications emerged creating an environment for people to publish, share and discuss content, plus enabling people to create descriptive profiles of themselves for self-expression and build social networks consisting of relationships with others with the purpose of interaction and communication. With the increasing popularity of such social networking applications the number of users has scaled up and is still growing. Not only the number of users but also the web traffic is an indicator to the growing importance of social networking platforms which are now among the most visited websites.
With over 100 million unique visitors worldwide, Facebook is one of the most popular networking sites on the web, moreover the site ranks third in the top visited sites on the web only being surpassed by Google and Yahoo! according to Alexa. YouTube (with over 80 million unique visitors), MySpace (with about 60 million unique visitors) and Flickr (about 30 million unique visitors) are other examples of prominent social networking platforms.
However, the availability of such a huge amount of information within the social networking sites and the open nature of the services and their usage also attracts the attention of parties with marketing purposes or malicious intent. Users are thereby put at risk of online stalking, phishing, identity theft, spamming, passing on data to third parties and privacy issues which are related to personal data exposure due to insufficient access control.
By maintaining social networks and actively participating in Social Web activities like interacting with others, users unwittingly expose sensitive and personal or inappropriate, even reputation-damaging data not only to friends but to an audience that mostly remains invisible and consists of strangers or acquaintances that potentially are not supposed to see such information. Thus the revealed information can lead to major consequences if read out of context or read by parties, like authorities or job recruiters, for whom this information was not intended. The reputation of social networking sites has been slightly […]


ISBN (eBook)
1015 KB
Institution / Hochschule
Gottfried Wilhelm Leibniz Universität Hannover – Informatik, Studiengang Informatik
netzwerke privacy policies social semantic



Titel: Using Social Semantic Web Data for Privacy Policies