Harmful Conspiracies in Temporal Interaction Networks: Understanding the Dynamics of Digital Wildfires through Phase Transitions (original) (raw)

2023, arXiv (Cornell University)

Shortly after the first COVID-19 cases became apparent in December 2020, rumors spread on social media suggesting a connection between the virus and the 5G radiation emanating from the recently deployed telecommunications network. In the course of the following weeks, this idea gained increasing popularity, and various alleged explanations for how such a connection manifests emerged. Ultimately, after being amplified by prominent conspiracy theorists, a series of arson attacks on telecommunication equipment follows, concluding with the kidnapping of telecommunication technicians in Peru. In this paper, we study the spread of content related to a conspiracy theory with harmful consequences, a so-called digital wildfire. In particular, we investigate the 5G and COVID-19 misinformation event on Twitter before, during, and after its peak in April and May 2020. For this purpose, we examine the community dynamics in complex temporal interaction networks underlying Twitter user activity. We assess the evolution of such digital wildfires by appropriately defining the temporal dynamics of communication in communities within social networks. We show that, for this specific misinformation event, the number of interactions of the users participating in a digital wildfire, as well as the size of the engaged communities, both follow a power-law distribution. Moreover, our research elucidates the possibility of quantifying the phases of a digital wildfire, as per established literature. We identify one such phase as a critical transition, marked by a shift from sporadic tweets to a global spread event, highlighting the dramatic scaling of misinformation propagation. Additionally, we argue that the driving forces behind this observed transition are attributed to influential users, who act as catalysts, accelerating the spread of misinformation. Lastly, our data suggest that the characteristics of such events may be predictable, at least in some instances. From this data, we hypothesize that monitoring minor peaks in user interactions, which precede the critical phase culminating in real-world consequences, could serve as an early warning system, aiding in the prediction and potentially the mitigation of digital wildfires.