While HyperFF is mathematically intractable, we provide theoretical justifications and mathematical analysis based on its simplified version.

Its merits are threefold: (a) Realistic: it successfully reproduces all seven patterns, in addition to five patterns established in previous studies, (b) Self-contained: unlike previously proposed models, it does not rely on oracles (i.e., unexplainable external information) at all, and it is parameterized by just two scalars, and (c) Emergent: it relies on simple and interpretable mechanisms on individual entities, which do not trivially enforce but surprisingly lead to macroscopic properties. We also propose HyperFF, a stochastic model for generating realistic hypergraphs.

To this end, we define new measures, extend notions of common graph properties to hypergraphs, and assess the significance of observed patterns by comparison with a null model and statistical tests. In this work, we closely examine seven structural and dynamical properties of real hypergraphs from six domains. However, the increased complexity makes it challenging to understand hypergraphs as thoroughly as graphs. Regarded as a generalization of graphs, hypergraphs allowing for various sizes of edges prove fruitful in addressing this limitation. What kind of macroscopic structural and dynamical patterns can we observe in real-world hypergraphs? What can be underlying local dynamics on individuals, which ultimately lead to the observed patterns, beyond apparently random evolution? Graphs, which provide effective ways to represent pairwise interactions among entities, fail to represent group interactions (e.g., collaborations of three or more researchers, etc.). The dissemination media are diverse, and even more so, it is precisely the mix of different media that makes a digital wildfire possible. Rather, a multitude of small cascades with partly contradictory statements are responsible for the rapid spread. Finally, it has been shown that the spread of a digital wildfire is not the result of an information cascade in the sense of single, particularly influential short messages within a single medium. On the contrary, it seems that it is the existence of contradictory explanations that unites supporters from different camps to support a new idea. It does not matter if ideas and explanations contradict each other. Digital wildfires do not emerge out of nowhere but find their origin in a multitude of already existing ideas and narratives that are reinterpreted and recomposed in the light of a new situation. Although published work suggests that a purely structure-based analysis of the information spread allows for early detection, there is no way of predictively labelling spreading information as probably leading to a digital wildfire. The main findings that emerge from the study of the 5G and corona misinformation event can be summarised as follows.

Furthermore, we present a framework that provides the playing field for investigating the spread of digital wildfires. In this context, we present a system whose application allows us to acquire large amounts of data from the online social network Twitter and thus create the database from which we extract the digital wildfire in its entirety. This thesis deals with the capture, automatic processing, and investigation of a complex digital wildfire, namely, the Corona and 5G misinformtionsevent - the idea that the COVID-19 outbreak is somehow connected to the introduction of the 5G wireless technology. Although the example of the British politician is a simple digital wildfire that only damaged the reputation of a single person, there are more complex digital wildfires whose consequences are more far-reaching. On the relatively new media of the internet and specifically in online social networks, digital wildfires, i.e., fast spreading, counterfactual or even intentionally misleading information occur on a regular basis and lead to severe repercussions. However, not only did the politician’s image suffer considerable damage, moreover, he was also able to sue the BBC for £185,000 in damages. When a high-ranking British politician was falsely accused of child abuse by the BBC in November 2012, a wave of short messages followed on the online social network Twitter leading to considerable damage to his reputation.

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