Lehrstuhl für Informatik III

    Online Social Networks

    Future Internet Applications & Overlays


     Dr. rer. nat. Matthias Hirth


     Kathrin Borchert

    Student Helpers  

    Oskar Smietanka

    Online Social Networks

    Recently the characteristics of online communities changed dramatically. In the past years there existed only small communities of people sharing the same interests and organizing themselves in web forums or chat rooms. Nowadays huge online social networks (OSNs) like Facebook, YouTube, or Twitter consist of thousands or millions of people from all over the world and various social and cultural backgrounds. These online communities gain an ever-increasing influence on real life, as to many people the information on the platforms seem to be very trustworthy because the content is created by their friends and acquaintances. As a result it becomes important to know how information spreads in OSNs, e.g., to track down the source of inappropriate material. Furthermore OSNs contain a huge workforce and a lot of knowledge, which is already exploited in projects like Wikipedia. A new approach to use this workforce and the wisdom of the crowd is referred to as crowdsourcing. Thus, the modeling of OSNs, the dynamics within OSNs, and the spread of information is important and within the scope of the FIA research group.

    Growth Models for Social Media Networks

    Based on measurements of the number of users registered to Facebook, we investigated different population growth models. As a result, we found that a logistic growth model as well as a square growth model fits very well. However, the consequences of both models are completely different. Square growth is not bounded, in contrast to logistic growth. The logistic function is applied in various fields like biology, sociology or economics, and especially in demographics for describing population growth. The logistic growth model was developed by Pierre Verhulst (1804-1849) who suggested that the rate of population increase may be limited, i.e., it may depend on population density.

    The question arises which of both models will come true in the future. Or in other words "Will the growth of Facebook continue and reach about 2 billion users in 2014?" or "Will Facebook reach its limits in 2014 with 800 million users?"

    Information Spreading

    For many people the Internet is the most important source of information. Customers decide depending on other user's ratings in online shops whether to buy a product or not, the reputation of people, companies and countries is affected by positive and negative by articles in online magazines, blogs and discussions in social networks, people organize themselves in Facebook groups manipulating music charts or protesting against new laws. Information spreads fast in the Internet, as soon as certain popularity level is reached. However it is not totally understood how information spreads in online social networks or in the blogosphere and if it is possible to influence the spreading of certain information. Currently a popular way to "influence" spreading of information is search engine optimization (SEO). Due to the vast amount of information available in the Internet, users rely on search engines to filter the relevant content they are looking for. Therefore a high search engine ranking make a webpage or a certain information more relevant. Influencing the search engine ranking of a certain webpage using SEO can help to, e.g. spread positive information about a company to improve its reputation. In this context, the modeling of the dissemination of information and the interaction with advanced mechanisms like SEO or crowdsourcing are investigated.