U.S. Social Fragmentation at Multiple Scales

Despite global connectivity, societies seem to be increasingly polarized and fragmented. This phenomenon is rooted in the underlying complex structure and dynamics of social systems. Far from homogeneously mixing or adopting conforming views, individuals self-organize into groups at multiple scales, ranging from families up to cities and cultures. In this paper, we study the fragmented structure of the American society using mobility and communication networks obtained from geo-located social media data. We find self-organized patches with clear geographical borders that are consistent between physical and virtual spaces. The patches have multi-scale structure ranging from parts of a city up to the entire nation. Their significance is reflected in distinct patterns of collective interests and conversations. Finally, we explain the patch emergence by a model of network growth that combines mechanisms of geographical distance gravity, preferential attachment, and spatial growth. Our observations are consistent with the emergence of social groups whose separated association and communication reinforce distinct identities. Rather than eliminating borders, the virtual space reproduces them as people mirror their offline lives online. Understanding the mechanisms driving the emergence of fragmentation in hyper-connected social systems is imperative in the age of the Internet and globalization.Comment: 24 pages, 9 figure

Strategizing COVID-19 Lockdowns Using Mobility Patterns

During the COVID-19 pandemic, governments have tried to keep their territories safe by isolating themselves from others, limiting non-essential travel and imposing mandatory quarantines for travelers. While large-scale quarantine has been the most successful short-term policy, it is unsustainable over long periods as it exerts enormous costs on societies. As a result, governments which have been able to partially control the spread of the disease have been deciding to reopen businesses. However, the WHO has warned about the risks of re-opening prematurely, as is playing out in some countries such as Spain, France and various states in the US such as California, Florida, Arizona, and Texas. Thus, it is urgent to consider a flexible policy that limits transmission without requiring large scale and damaging quarantines. Here, we have designed a multi-level quarantine process based on the mobility patterns of individuals and the severity of COVID-19 contagion in the US. By identifying the natural boundaries of social mobility, policymakers can impose travel restrictions that are minimally disruptive to social and economic activity. The dynamics of social fragmentation during the COVID-19 outbreak are analyzed by applying the Louvain method with modularity optimization to weekly mobility networks. In a multi-scale community detection process, using the locations of confirmed cases, natural break points as well as high risk areas for contagion are identified. At the smaller scales, for communities with a higher number of confirmed cases, contact tracing and associated quarantine policies is increasingly important and can be informed by the community structure.Comment: 16 pages, 8 figure