Social Tagging is a way of organizing shared content. In contrast to traditional approaches, the categorization is done by users who create or consume certain data objects, such as blogs, photographs, videos, literature and other content. In order to categorize these objects, users assign freely chosen tags which express their point of view. All tags together represent a pool of metadata that leads to an amateur categorization of shared content. The thesis deals with the effects of Social Tagging on the structure of organization and on social relationships. It is based on the latest scientific research and presents several findings from different scientific fields. My thesis points out that social tags are meaningful in general. As a result, they are suitable for organizing data objects, although there is a lack of accuracy that can be more or less critical. Social Tagging is best when it comes to organizing a big, heterogeneous amount of content, for instance within the internet or big libraries and which is likely to emerge in the future. In that case social Tags are more accurate than automatic generated metadata. In addition, a much bigger amount of data can be categorized in contrast to expert-categorization, which is usually done by a few professionals. Furthermore, Social Tagging supports team work and the building of communities around a variety of interests. For this reason, Social Tagging can be also applied within learning environments.