Social Computing
Social Computing


Portrait of Elisabeth Lex Kontakt send
Elisabeth Lex Research Area Manager
Portrait of Matthias Traub Kontakt send
Matthias Traub Business Area Manager
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Social networks and media, such as Facebook and Twitter & Co., shape our communications and knowledge sharing more than ever. In the "Social Computing" Area we investigate the benefits and added value that they can offer companies and institutions. Area Social Computing addresses issues associated with knowledge extraction and construction and utilization of social network data with the objective to deliver the relevant information to businesses and the users.

Our goal is to utilize the resources of the digital age for human benefit
Methods and Applications

Webshops in which products ‘find’ their buyers or relevant information on a certain topic that is provided quickly and easily by a community – by analyzing and meaningfully furnishing information from social networks, useful knowledge for a variety of thrilling applications can be derived from our interactions. In Social Computing, we closely investigate generating knowledge from social network data. For example: What is the best way to distribute content via social media?  How can users be sensibly classified? How trustworthy is social media? These challenges shape our services, e.g., crowd-based recommender systems, expert search, trend detection and social media marketing campaigns.

Specifically, our task is to use information and knowledge collected from social networks or media to generate added value for businesses and the users. To that end, we apply methods from the field of collaborative filtering to provide customized recommender services. We employ social network analysis methods to analyze interactions and find experts or influencers or to detect communities and predictive modeling methods to analyze trends. Using methods from the fields of data mining and machine learning, social network analysis and Web Science, we investigate novel approaches to determine the quality of social content.



All solutions that we developed together with our industrial and project partners involve basic research. Currently, we are participating in two EU projects dedicated to generating and linking knowledge from various sources, processing it in the best possible way and providing it in a structured manner, especially in the health sector. In this context, our established know-how in the field of Social Network Analysis, User Modeling, Predictive Modeling and Social System Design and Development can be applied to create a novel knowledge environment for the user, in which information is not only socially exchanged but also automatically delivered to the relevant persons. The issue of information quality is particularly important, especially in terms of web-based sources used for decision-making. In this regard, we are conducting research within a FP7 Marie Curie IRSES project that brings together international experts from top universities to stimulate research in the field of information quality. To that end, we are developing methods that will allow users to filter massive amounts of information on the social web according to their own quality needs. As for Science 2.0, we are investigating methods of improving information sharing and collaboration between scientists from various disciplines. In that context, we are working, for example, on creating knowledge environments for researchers that provide novel perspectives on research fields and that encourage collaboration.


  • Blanc Noir

    For the Graz-based Agency Blanc Noir, we are developing a recommendation system and novel algorithms that analyze big data to provide the user with targeted and tailored products or advertising – and, considering his/her social interactions, only the kind that the user may be interested in. “This way we can simultaneously address customers of online shops or internet platforms in order to provide them with personalized information and thereby raise the Internet shopping experience to a new level,” says Stefan Kahr, Managing Director of Blanc Noir.

  • Exthex

    For the Styrian company Exthex, we are developing innovative social media marketing methods to increase customer loyalty to its products. In this context, we not only create marketing strategies based on social network analysis, but also novel recommender mechanisms and algorithms in this domain.

  • Organic Lingua

    Within the project Organic Lingua, knowledge construction and structuring is enhanced by enriching resources with metadata to facilitate the exchange and delivery of information in organic agriculture. In this context, we are developing new services, such as knowledge construction based on Natural Language Processing (NLP) techniques and collaborative filtering methods.

  • WiQ-Ei

    With Know-Center as the scientific coordinator, within the project Web Information Quality Evaluation Initiative (WiQ-Ei) an international consortium is developing algorithms and methods to assess the quality of social media and web content based on various criteria, such as factual density, sentiment, objectivity and trust. This way, we help individuals and businesses to assess the reliability and relevance of the social media and web content.

  • Stellar

    As a member of the Network of Excellence STELLAR, Know-Center particularly contributes to analyzing interaction networks within the scientific community. Via these networks, areas of interest can be determined and experts in each area can be recommended. We use our expertise in Science 2.0, social network analysis and machine learning to assist the scientific community in the best possible way.

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