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We study the roles of elected officials in the dissemination of misinformation on Twitter. This is a particularly salient online population since elected officials serve as primary sources of information for many stakeholders in the public, media, government, and industry. We analyze the content of tweets posted from the accounts of over 3,000 U.S. state lawmakers throughout 2020 and 2021. Specifically, we identify the dissemination of URLs linked to unreliable content. Our starkest finding is that Republicans share more misinformation than do Democrats by an order of magnitude. Additionally, we uncover distinct patterns in the temporal trends of tweets and tweets associated with misinformation across party and state lines. Delving into the content of tweets referencing unreliable URLs reveals discussions of election integrity, abortion, COVID-19 policies, and immigration. Furthermore, consistent with the literature on asymmetric polarization, Republicans exhibit a greater inclination toward engaging in partisan attacks. We also find that state lawmakers often tweet about state-specific topics. These findings enhance our understanding of misinformation, political communication, and state politics.

The significance and influence of US Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology -- the citation exponential random graph model, for which we provide user-friendly software -- enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citation should incorporate both the effects of case characteristics and the structure of past citations., Comment: 34 pages, 10 figures

Information, ideas, and diseases, or more generally, contagions, spread over space and time through individual transmissions via social networks, as well as through external sources. A detailed picture of any diffusion process can be achieved only when both a good network structure and individual diffusion pathways are obtained. The advent of rich social, media and locational data allows us to study and model this diffusion process in more detail than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall process is difficult to trace. This propagation is continuous over space and time, where individual transmissions occur at different rates via complex, latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion (STAND) is developed based on the survival model in this research. Both time and spatial distance are used as explanatory variables to simulate the diffusion process over two different network structures. The aim is to provide a more detailed measure of how different contagions are transmitted through various networks where nodes are geographic places at a large scale.

Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. Here, we review the most prominent methods of inferential network analysis---both for cross-sectionally and longitudinally observed networks including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, we summarize its analytic form, identify prominent published applications in political science and discuss computational considerations. We conclude with a set of guidelines for selecting a method for a given application.

Previous research indicates that race, ethnicity, and gender influence legislative behavior in important ways. The bulk of this research, however, focuses on the way these characteristics shape an individual legislator's behavior, making it less clear how they account for relationships between legislators. We study the cosponsorship process in order to understand the race and gender based dynamics underlying the relational component of representation. Using a temporal exponential random graph model, we examine the U.S. House cosponsorship network from 1981 through 2004. We find that Black and Latino members of Congress are at a comparative disadvantage as a result of race-based assortative mixing in the cosponsorship process, yet this disadvantage is mitigated by the electoral pressures that all members face. Members representing districts with significant racial and ethnic minority populations are more likely to support their minority colleagues. We also find that women members do not appear to face a similar disadvantage as a result of their minority status. We argue that these race and gender dynamics in the cosponsorship network are the result of both the inherent tendency towards intra-group homophily in social networks and the electoral connection, which is manifested here as members supporting minority colleagues to broaden their own electoral base of support among minority constituencies.

Scholars, policymakers, and research sponsors have long sought to understand the conditions under which scientific research is used in the policymaking process. Recent research has identified a resource that can be used to trace the use of science across time and many policy domains. US federal agencies are mandated by executive order to justify all economically significant regulations by regulatory impact analyses (RIAs), in which they present evidence of the scientific underpinnings and consequences of the proposed rule. To gain new insight into when and how regulators invoke science in their policy justifications, we ask: does the political attention and controversy surrounding a regulation affect the extent to which science is utilized in RIAs? We examine scientific citation activity in all 101 economically significant RIAs from 2008-2012 and evaluate the effects of attention -- from the public, policy elites and the media -- on the degree of science use in RIAs. Our main finding is that regulators draw more heavily on scientific research when justifying rules subject to a high degree of attention from outside actors. These findings suggest that scientific research plays an important role in the justification of regulations, especially those that are highly salient to the public and other policy actors.

Political parties play a central role in democracies, helping to mediate between citizens and governing elites by running candidates for office who promise to pursue policy programs. But what if “the party” is really more of a collection of interest groups than a traditional party organization? Using data from US congressional elections and campaign finance, Bruce Desmarais, Ray La Raja and Mike Kowal show that parties are networks of partisan groups that converge on select groups of challengers. They explain that parties in the U.S. have become so distinctive and polarized, because they represent unique coalitions of policy demanders that influence the ideological composition of Congress.