Research

Published

Legislative Support for Environmental Policy Innovation: An Experimental Test for Diffusion through a Cross-State Policy Network
Ishita Gopal and Bruce Desmarias.

Accepted for Publication at Applied Network Science

Abstract
In this registered report we describe a field experiment that has been designed to provide evidence of causal effects underlying the micro-foundations of public policy diffusion across the U.S. states. The aim of our study is to test how and if cross-state legislator level connections serve as a vector through which support for policies diffuses. We measure a novel cross-state legislative network dataset in which two legislators are connected through co-signing environmental policy statements organized by the National Caucus of Environmental Legislators. We propose to survey legislators' support for policies proposed in other states, and randomize the degree of information included in the policy description regarding support by other legislators in the network. Our study is situated to contribute to our understanding of state legislative politics, policy networks, and interest group politics. We focus on environmental policy due to the inherently nationalized consequences of state and local policy innovations.

Fig: Bipartite network between legislators and enviroment focussed bills

The National Network of U.S. State Legislators on Twitter
Ishita Gopal, Taegyoon Kim, Nitheesha Nakka and Bruce Desmarias.
Accepted for publication at Political Science Research Methods

Abstract
Networks among legislators shape politics and policymaking within legislative institutions. In past work on legislative networks, the ties between legislators have been defined on those who serve in the same legislature or chamber. Online information networks, which have been found to play important roles in legislative communication at the national level, are not bounded by individual legislative bodies. We collect original data for over four thousand U.S. state legislators and study patterns of connection among them on Twitter. We look at three types of Twitter networks---follower, retweets, and mentions. We describe these networks and estimate the relationships between ties and salient attributes of legislators. We find that networks are organized largely along geographic and partisan lines and that identity attributes---namely gender and race---exhibit strong associations with the formation of ties.

Fig: Follower, Mentions and Retweets Network amongst state legislators

The network science of public policy diffusion
Ishita Gopal and Bruce Desmarias.
Published in the Oxford Handbook of Engaged Methodological Pluralism, 2023

Abstract
For over half a century, rigorous empirical analysis of the patterns according to which policies spread across governments has lead to the substantial accumulation of knowledge regarding the attributes of governments and policies that predict the spread of policy. Recently, a more thorough integration of policy diffusion research with relational and network approaches to analysis has led to insights regarding the pathways that connect governments and facilitate the diffusion of policies. In this chapter we review recent developments regarding network approaches to policy diffusion research, and unify these findings and ideas as the, ``network science of public policy diffusion.'' We also offer guidance regarding directions in which the study of policy diffusion research can be advanced through network scientific approaches. Specifically, we suggest that researchers look to the policy networks literature for theoretical innovations, and that the adoption of multilayer network analysis methods would facilitate substantial advancements in the empirical analysis of the systems determinants of public policy diffusion.

Attention to the COVID-19 pandemic on Twitter: Partisan differences among U.S. state legislators
Taegyoon Kim, Nitheesha Nakka, Ishita Gopal, Bruce Desmarias, Abigail Mancinelli, Jeffrey J. Harden, Hyein Ko, and Frederick Boehmke.
Published in Legislative Studies Quaterly, 2021

Abstract
Subnational governments in the United States have taken the lead on many aspects of the response to the COVID-19 pandemic. Variation in government activity across states offers the opportunity to analyze responses in comparable settings. We study a common and informative activity among state officials—state legislators’ attention to the pandemic on Twitter. We find that legislators’ attention to the pandemic strongly correlates with the number of cases in the legislator’s state, the national count of new deaths, and the number of pandemic-related public policies passed within the legislator’s state. Furthermore, we find that the degree of responsiveness to pandemic indicators differs significantly across political parties, with Republicans exhibiting weaker responses, on average. Lastly, we find significant differences in the content of tweets about the pandemic by Democratic and Republican legislators, with Democrats focused on health indicators and impacts, and Republicans focused on business impacts and opening the economy.

Accessibility and Generalizability: Are Digital Media Effects Moderated by Age or Digital Literacy?
Kevin Munger, Ishita Gopal, Jonathan Nagler, and Joshua Tucker.
Published in Research & Politics, 2021

Abstract
An emerging empirical regularity suggests that older people use and respond to social media very differently than younger people. Older people are the fastest-growing population of Internet and social media users in the US, and this heterogeneity will soon become central to online politics. However, many important experiments in this field have been conducted on online samples that do not contain enough older people to be useful to generalize to the current population of Internet users; this issue is more pronounced for studies that are even a few years old. In this paper, we report the results of replicating two experiments involving social media (specifically, Facebook) conducted on one such sample lacking older users (Amazon’s Mechanical Turk) using a source of online subjects which does contain sufficient variation in subject age. We add a standard battery of questions designed to explicitly measure digital literacy. We find evidence of significant treatment effect heterogeneity in subject age and digital literacy in the replication of one of the two experiments. This result is an example of limitations to generalizability of research conducted on samples where selection is related to treatment effect heterogeneity; specifically, this result indicates that Mechanical Turk should not be used to recruit subjects when researchers suspect treatment effect heterogeneity in age or digital literacy, as we argue should be the case for research on digital media effects.

Working Papers

Automated Classification of Political Video without Text
PolMeth presentation, 2024

Abstract

This paper tests if we can exclusively use audio features and build language agnostic classification models to identify attack/negative advertising. We use transfer learning and train the Wav2Vec2 model on a dataset of 10000 televised ads shown in the US. The audio classifier has an accuracy of 85% in identifying negative ads. We then test how well this model performs out of domain on a dataset of political ads shown on Google in the US.