However, even though these approaches significantly reduce the amount of manual labor required, machine learning models lack an understanding of context. Thus, features like humor or irony may not be taken into account, leading to miss-classifications. Due to these shortcomings, there is considerable motivation to explore other, more general detection methods. We aim for a more generic approach, exploiting not only the content but rather the underlying interactions within online social networks, to gain knowledge about the properties and dynamics of the spread of misinformation with harmful consequences on a societal scale. Specifically, we investigate the evolution of temporal networks induced by interactions between Twitter users during misinformation events.
There will be pizza. Welcome!
Daniel Schroeder (Simula and OsloMet) and Kaspara Skovli Gåsvær (former master of science student at the CS program