Mask images on Twitter increase during COVID-19 mandates, especially in Republican counties.

Wearing masks reduces the spread of COVID-19, but compliance with mask mandates varies across individuals, time, and space. Accurate and continuous measures of mask wearing, as well as other health-related behaviors, are important for public health policies. This article presents a novel approach to estimate mask wearing using geotagged Twitter image data from March through September, 2020 in the United States. We validate our measure using public opinion survey data and extend the analysis to investigate county-level differences in mask wearing. We find a strong association between mask mandates and mask wearing-an average increase of 20%. Moreover, this association is greatest in Republican-leaning counties. The findings have important implications for understanding how governmental policies shape and monitor citizen responses to public health crises.

How State and Protester Violence Affect Protest Dynamics.

How do state and protester violence affect whether protests grow or shrink? Previous research finds conflicting results for how violence affects protest dynamics. This article argues that expectations and emotions should generate an n-shaped relationship between the severity of state repression and changes in protest size the next day. Protester violence should reduce the appeal of protesting and increase the expected cost of protesting, decreasing subsequent protest size. Since testing this argument requires precise measurements, a pipeline is built that applies convolutional neural networks to images shared in geolocated tweets. Continuously valued estimates of state and protester violence are generated per city-day for 24 cities across five countries, as are estimates of protest size and the age and gender of protesters. The results suggest a solution to the repression-dissent puzzle and join a growing body of research benefiting from the use of social media to understand subnational conflict.

Coiling of elastic rods on rigid substrates.

We investigate the deployment of a thin elastic rod onto a rigid substrate and study the resulting coiling patterns. In our approach, we combine precision model experiments, scaling analyses, and computer simulations toward developing predictive understanding of the coiling process. Both cases of deposition onto static and moving substrates are considered. We construct phase diagrams for the possible coiling patterns and characterize them as a function of the geometric and material properties of the rod, as well as the height and relative speeds of deployment. The modes selected and their characteristic length scales are found to arise from a complex interplay between gravitational, bending, and twisting energies of the rod, coupled to the geometric nonlinearities intrinsic to the large deformations. We give particular emphasis to the first sinusoidal mode of instability, which we find to be consistent with a Hopf bifurcation, and analyze the meandering wavelength and amplitude. Throughout, we systematically vary natural curvature of the rod as a control parameter, which has a qualitative and quantitative effect on the pattern formation, above a critical value that we determine. The universality conferred by the prominent role of geometry in the deformation modes of the rod suggests using the gained understanding as design guidelines, in the original applications that motivated the study.