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Even though social media platforms have created opportunities for more efficient and convenient civic participation, they are unlikely to bring about social change if the online actions do not propagate to offline civic participation. This article begins by reviewing the meta-analytic evidence on the relation between social media use and offline civic participation. Following this discussion, we present a theoretical framework that incorporates the attitudinal, motivational, and relational processes that may mediate the effect of social media use on offline civic participation. The framework highlights how social media algorithms may shape attitudes on important societal issues, promote generalized action goals among habitual users, and build social capital. We further discuss factors that may strengthen or undermine each of these processes, suggest ways to design and implement algorithms that may promote offline civic participation, and propose questions for future research.

 

How do religious affiliation and beliefs shape vaccine attitudes and behaviors? This study examined the associations of attitudes and behaviors relevant to the flu, measles-mumps-rubella (MMR), and human-papillomavirus (HPV) vaccines with religious affiliations, as well as philosophical, spiritual, and moral beliefs. Respondents were 3005 adults from a probability-based, four-wave panel survey in the United States. Longitudinal structural equation modeling examined how religious affiliations and philosophical/moral beliefs shaped attitudes toward vaccines and actual vaccination. Stronger philosophical beliefs predicted more negative attitudes toward each vaccine and stronger moral beliefs more negative attitudes toward the HPV vaccine. Negative vaccine attitudes then predicted weaker intentions to encourage others to vaccinate and lower probability of receiving a vaccine. Theoretical and public health messaging implications are discussed. 

In demand of predicting new human immunodeficiency virus (HIV) diagnosis rates based on publicly available HIV data that are abundant in space but have few points in time, we propose a class of spatially varying auto-regressive models compounded with conditional auto-regressive spatial correlation structures. We then propose to use the copula approach and a flexible conditional auto-regressive formulation to model the dependence between adjacent counties. These models allow for spatial and temporal correlation as well as space–time interactions and are naturally suitable for predicting HIV cases and other spatiotemporal disease data that feature a similar data structure. We apply the proposed models to HIV data over Florida, California and New England states and compare them with a range of linear mixed models that have been recently popular for modelling spatiotemporal disease data. The results show that for such data our proposed models outperform the others in terms of prediction.