DNDS will develop a data-driven modelling framework to emulate the effect of algorithmic bias on the way people interact and share information in online social networks. By systematically analysing many filtering algorithms and diverse social behaviors, DNDS will devise tools to discover bias in algorithms, quantify it, and potentially correct it. Our project has the potential to inform the development of science-based policies to foster a healthy flow of information in our modern digital ecosystems.
Searching on the internet has got a considerable part of our everyday activity and studying the laws of this process with the goal of possible improvement of strategies and data structures is a real challenge. The approach of this project is not algorithmic; instead, we plan to understand how navigation strategies are influenced by cognitive and social biases, by the learning processes of individuals, their characteristics like age, gender, and cultural background. We will study the dynamics of the searches as recorded by clickstreams, which will be a tool to map out the complex information landscape. This landscape can be represented by a weighted network and it evolves during the learning process as exploration proceeds. A further interesting question will be to explore if the content, like Wikipedia categories of the target of search influence the strategy. We plan to study with similar tools the related problem of search for information, which, in contrast to navigation, has no a priory given target. An example can be the acquisition of news, where the search strategy can be a source of filter bubbles known as one of the origins of increasing political polarisation. We are planning to analyze big datasets from Wikipedia related games (Wiki Game and Wikispeedia), Wikipedia clickstream data, online social network data, and Mechanical Turk based experiments.
The project is administered at RWTH Aachen University.
Networks define our life. They are essential to biology, communications, social and economic systems, they influence virtually all areas of science and technology. But their workings are not fully understood. Laszlo Lovasz from the Hungarian Academy of Sciences and Jaroslav Nesetril from Charles University in Prague, renowned mathematicians specialising in graph theory, and Laszlo Barabasi, a leading expert in network science based at the Central European University in Budapest, aim to build a mathematically sound theory of dynamical networks. They want to transform our understanding of complex systems and prepare the ground for applications in multiple disciplines.
Both graph theory in mathematics and the study of networks have made major conceptual advances in the past decade. However, the research communities working in these two disciplines had little conversation between each other, and that limited our insight. The research funded with an ERC Synergy Grant can potentially change it, constructing a coherent theory of dynamical networks, and exploiting its applications and predictive power for various real systems. To enhance the wider impact of the proposed mathematical advances, the principal investigators plan to establish steady links with experts from different domains that encounter and explore networks, from cell biology to brain science and transportation and communication networks, inspiring with novel questions and helping the application of our advances in these domains.