In this thesis we analyze the performance of social semantics in textual information retrieval. By means of collaboratively constructed knowledge derived from web-based social networks, inducing both common-sense and domain-specific knowledge as constructed by a multitude of users, we will establish an improvement in performance of selected tasks within different areas of information retrieval. This work connects the concepts and the methods of social networks and the semantic web to support the analysis of a social semantic web that combines human intelligence with machine learning and natural language processing. In this context, social networks, as instances of the social web, are capable in delivering social network data and document collections on a tremendous scale, inducing thematic dynamics that cannot be achieved by traditional expert resources. The question of an automatic conversion, annotation and processing, however, is central to the debate of the benefits of the social semantic web. Which kind of technologies and methods are available, adequate and contribute to the processing of this rapidly rising flood of information and at the same time being capable of using the wealth of information in this large, but more importantly decentralized internet. The present work researches the performance of social semantic-induced categorization by means of different document models. We will shed light on the question, to which level social networks and social ontologies contribute to selected areas within the information retrieval area, such as automatically determining term and text associations, identifying topics, text and web genre categorization, and also the domain of sentiment analysis. We will show in extensive evaluations, comparing the classical apparatus of text categorization – Vector Space Model, Latent Semantic Analysis and Support Vector Maschine – that significant improvements can be obtained by considering the collaborative knowledge derived from the social web.