This note seeks to understand the extent of the disruptions to international relations caused by pandemics, focusing on one globally-endemic disease: malaria. We posit that longstanding diseases such as malaria have the potential to undermine the political ties of nation states, as well as the many benefits of these connections. Our argument is tested empirically using both directed-dyadic and monadic data, while incorporating methods that account for endogeneity and other relevant concerns. We find that the geographic malaria rates of a country not only serve to historically discourage foreign governments from establishing diplomatic outposts on a country's soil, but also lead to an aggregate decrease in the total diplomatic missions that a country receives. We then discuss the current implications of these findings.

Social media may help civil society organize and mobilize for different campaigns. However, the extent to which social media campaigns simply recruit like-minded individuals as compared to exerting a causal impact on joiners’ attitudes is difficult to disentangle. We test both the organizational and transformative potential of a civil society campaign in a randomized field experiment deployed via Facebook or an email newsletter in collaboration with a Bulgarian environmental campaign. As expected, we find that Bulgarian Facebook users who are active in pro-environmental groups, and those who decide to follow the campaign, are more highly educated than those who decide to stay at the sidelines. Moreover, beliefs in the effectiveness of civic society, character traits and prior activism systematically predict whether a Bulgarian Facebook user decides to join the cause on Facebook, or subscribe to the email newsletter. In contrast, we find little evidence that the campaign affected opinions, knowledge, or self-reported behavior. We conclude that social media campaigns that are commonplace among civil society organizations are effective at selecting activist-types, but changing the views and behaviors of the broader social media population may be more difficult than assumed.

Interactions between units in political systems often occur across multiple relational contexts. These relational systems feature interdependencies that result in inferential shortcomings and poorly-fitting models when ignored. General advancements in inferential network analysis have improved our ability to understand relational systems featuring interdependence, but developments specific to working with interdependence that cross relational contexts remain sparse. In this paper, I introduce a multilayer network approach to modeling systems comprising multiple relations using the exponential random graph model. In two substantive applications, the first a policy communication network and the second a global conflict network, I demonstrate that the multilayer approach affords inferential leverage and produces models that better fit observed data.