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In this paper, we argue that the consequences of democratic backsliding are not limited to the large and growing number of international organizations (IOs) made up and governed by illiberal or outright authoritarian regimes. Many of these authoritarian IOs (AIOs) formally adopt good governance mandates, linking goals like democracy promotion, anti-corruption policies and human rights to their broader mission. Why do AIOs adopt good governance mandates that appear to conflict with the norms and standards these regimes apply at home? We argue that AIOs adopt these standards when they face substantial pressure from inside or outside the IO to adopt them. Central to our argument is that not all aspects of good governance are inherently or equally threatening to autocratic regimes. They pursue strategies that minimize the threat by externalizing policy outside the membership and strategically defining the goals to avoid or enact. This allows autocratic governments to uptake good governance talk but lessen any deep commitment to the norms and sometimes even to use them strategically to project their own power outside of the organization. Using data on 48 regional IOs with primarily autocratic membership between 1945 and 2015, we demonstrate that AIOs facing pressure from external good governance promoters will adopt good governance mandates but strategically shape those mandates in their favor if they can form bargaining coalitions with likeminded governments. The findings have sobering implications for the future of good governance promotion through IOs.

Extending literature on political polarization and political expression, we study patterns of polarized expression by vocal partisans from opposing camps on social media. Specifically, we argue that polarized partisan expression can be characterized by three divergences: 1) different thematic emphases on the same issue; 2) response to different real-world events on the same issue; and 3) a temporal disconnect at the aggregate level. Highlighting how online expression by different partisan groups is animated by discrete concerns and events and exhibits different temporality, the three divergences in polarized partisan expression not only reflect and explain existing polarization concepts but also speak to the epistemological chasm between partisan groups. Our empirical analysis is based on Twitter discussion about the issue of immigration in the U.S. and applies topic modeling and time series analysis. Results demonstrate that liberal and conservative tweets exhibit different thematic emphases, are often spurred by different event features, and remain largely temporally independent, though both Trump's tweets and emotionally evocative events can draw simultaneous reaction from both sides. These findings suggest that opposing partisan groups not only hold different views on the same issue, but also weave different events and facts about the issue into partisan expression in response to different exogenous factors. In short, they "talk past each other." These polarized partisan expression patterns indicate a splintered public sphere, a concerning quality for deliberative democracy. [ABSTRACT FROM AUTHOR]

Marked by both deep interconnectedness and polarization, the contemporary media system in the United States features news outlets and social media that are bound together, yet deeply divided along partisan lines. This article formally analyzes communication flows surrounding mass shootings in the hybrid and polarized U.S. media system. We begin by integrating media system literature with agenda setting and news framing theories and then conduct automated text analysis and time series modeling. After accounting for exogenous event characteristics, results show that (a) sympathy and gun control discourses on Twitter preceded news framing of gun policy more than the other way around, and (b) conservatives on Twitter and conservative media reacted to progressive discourse on Twitter, without their progressive counterparts exhibiting a similar reactiveness. Such results shed light on the influence of social media on political communication flows and confirm an asymmetry in the ways partisan media ecosystems respond to social events. [ABSTRACT FROM AUTHOR]

This study explores how Presidential candidates compete for Twitter attention during a televised debate using two datasets of tweets posted during the first 2012 and 2016 U.S. Presidential debates. Using a time series analysis, we find that both debates exhibited zero-sum attention dynamics such that when one candidate gained attention, the other lost attention. However, our error correction models also revealed that the attention dynamics of Romney and Obama in 2012 produced an equilibrium that neither candidate could break from, whereas in 2016, Twitter attention to Trump overwhelmed Clinton. We therefore conclude that social media attention competition in during the 2012 U.S. presidential debate was more tightly contested compared to 2016. [ABSTRACT FROM AUTHOR]

Political campaigns often feature jarring revelations against candidates. This study examines how audiences come to understand major campaign events, the extent to which they shape evaluations of candidates, and how their impact is filtered through an increasingly partisan news media environment. Using national rolling cross-sectional survey data collected over the 2016 U.S. presidential election period, we show partisan asymmetries in the way major campaign events influenced candidate appraisals. Event effects during the 2016 campaign were dependent on various media use patterns and concentrated among Independents. In particular, the reopening of the investigation into Clinton's email server by James Comey reduced her favorability, especially when paired with liberal and conservative partisan media use. By providing a nuanced picture of partisan selective exposure and campaign effects, our findings reinforce that the role of campaigns in candidate appraisals should be understood at the intersection of media use, partisanship, and specific events during a contentious race. [ABSTRACT FROM AUTHOR]