The 5G COVID-19 Digital Wildfire: An evolving network of Twitter contacts to explore phase transition metaphors in viral misinformation

Shortly after the first COVID-19 cases became apparent in December 2019, 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 followed, 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 (DW). In particular, we investigate the 5G and COVID-19 DW 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 this particular DW by appropriately defining the temporal dynamics of communication in communities within social networks. We observe that, for this specific DW, the number of interactions of the users participating in the DW, as well as the size of the engaged communities, both exhibit visual patterns suggestive of power-law distributions, consistent with other social networks, though based on exploratory inspection rather than formal statistical tests. Moreover, our exploratory analysis elucidates the possibility of conceptualizing the phases of a DW, as per established literature. We identify one such phase as a potential critical shift, marked by a shift from sporadic tweets to a global spreading event, highlighting patterns suggestive of dramatic scaling in misinformation propagation, used heuristically without quantitative physical parameters. Additionally, we argue that patterns suggest the observed shift is associated with influential users, who appear to amplify the spread of misinformation, though causality is exploratory. Lastly, our data suggest that the characteristics of such events could contribute to prediction models, 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 anticipation and potentially the mitigation of DWs.