Temporal, Network-Based Media Analytics: From Model to Application

Given our evermore complex world, keeping track of important events, developments, and interdependencies is increasingly time-consuming or even infeasible. To a large part, this complexity is caused by the systems' underlying dynamics and connectedness – characteristics found in various domains. Media, characterized by its high volatility and interwoven network of content and actors, is such a domain. Due to its ever-growing importance, not only from an academic but also from a societal perspective, understanding media-related phenomena is of huge importance. However, deriving insights from media data is not trivial, and the mentioned characteristics must be considered. Instead of simply asking a question like Which topics are currently discussed?, to gain a holistic perspective, one must also consider the underlying dynamics and ask Which topics are gaining in popularity? or How does the relevance of a topic change over time?. Similarly, it is insufficient to only investigate individual media actors without considering their social connectedness. To account for these requirements, in this thesis, we leverage temporal, network-based methods for analyzing media data. We do not limit ourselves to the development of novel methodological approaches but also put these into practice by bridging the gap between model and application.