Towards Authority-Dependent Risk Identification and Analysis in Online Networks

Abstract

Interaction, discussion, and the exchange of diverse information make the Web the place it is today. Texts, images, videos, and even information such as geospatial and health data are shared at an unprecedented scale. This exchange of information on the Web generates an extensive, freely accessible data source for a variety of data-driven applications – with multiple opportunities, but also risks. In this paper, we present the overall idea of the research project ADRIAN – "Authority-Dependent Risk Identification and Analysis in online Networks" which is dedicated to the research and development of AI-based methods for detecting potential threats to individuals and institutions based on heterogeneous, online data sets. We will first monitor selected social sports apps and analyze the collected geospatial data. In a second step, the user profiles of sports apps and social media platforms will be correlated to be able to form a cluster of individuals and enable the identification of potential threats. Since a so-called "digital twin" can be reconstructed in this way, sensitive data is generated. If this data can also be correlated with other confidential data, it is possible to estimate the plausibility of the threat to individuals, groups or locations.