Wolz, NicolasXu, ManningWang, TiantianGesellschaft für Informatik2021-12-152021-12-152021978-3-88579-751-7https://dl.gi.de/handle/20.500.12116/37777In computational communication science, social network data can be used to analyze trends in the communication behavior of people. For this work, a data set containing english Tweets was provided by the University of Technology Ilmenau, which was collected during the begining of the COVID-19 pandemic. The goal was to find hidden patterns within the data to show if and how the pandemic influenced our communication. This paper looks at the Distance Decay Effect, which says that near things are more related to each other than distant things, and therefore communication should get more sparse the greater the distance between users. Modeling the data with a Gravity Model shows that this relationship is true for the data provided, therefore reproducing earlier research on this topic. We were not successful in finding any clear trend showing that the strengh of the Distance Decay Effect changed over the course of the first weeks of the pandamic.enDistance Decay EffectGravity ModelCOVID-19Twitter1614-3213