We introduce the Digitally Accountable Public Representation (DAPR) Database, an innovative archive that systematically tracks and analyzes the online communications of federal, state, and local elected officials in the U.S. Focusing on X/Twitter and Facebook, the current database includes 28,834 public officials, their demographic information, and 5,769,904 Tweets along with 450,972 Facebook posts, dating from January 2020 to December 2024. The database integrates three interconnected datasets: metadata on elected officials, weekly aggregated X data, and weekly aggregated Facebook data.
Amid national gridlock on climate policy, state legislatures have become increasingly central to climate governance—yet we know little about how state legislators communicate climate issues online. Drawing on 6,177,988 social media posts from 6,353 legislators on Facebook and X (formerly Twitter) between 2020 and 2023, we fine-tune a large language model to classify climate-relevant content and stance. Among nearly 58,000 labeled posts between 2020 and 2021, 34,165 were supportive and 11,573 opposing, with clear variation across platforms.
Recent work in digital politics has begun to explore the role of race and ethnicity in digital communications. This research, however, has not fully addressed how lawmakers interact with their Asian constituents and the broader minority population. We take up this task by analyzing over 3 million tweets posted by state legislators between 2020 and 2021, focusing on messages targeting Asian ethnic groups. We fine-tuned a large language on classification task by using our labelled data and detected 7,202 anti-racism speech and 2,536 racism speech among 25,102 tweets that target Asian ethnic groups specifically.
Prominent recent works have measured democratic support using a single latent variable that purports to span a single dimension from steadfast opposition to whole-hearted support. This ignores ample evidence that support for democracy is complex and multidimensional. Here we provide a series of validation tests of the sort of cross-national time-series latent variable measures employed in recent research by reference to questions on support for liberal democracy and opposition to its erosion from multiwave surveys conducted around the world.
Prominent recent work argues that support for democracy behaves thermostatically—that democratic erosion boosts democratic support while deepening democracy yields public backlash—and further contends that there is no evidence for the classic argument that democracy itself increases democratic support over time. Here, we document how these conclusions depend on subtle choices in measurement coding that constitute “researcher degrees of freedom”: analyses employing alternative reasonable choices provide little or no support for the original conclusions.
How does public climate change concern and policy preferences affect climate policy outputs? Prevailing explanations regarding the adoption of climate change (mitigation) policies often focus on collective action, institutions, distributive politics, and policy diffusion. Despite being the focus of many studies on public policy in other issue areas, the role of public opinion on national climate policy has not been studied simply because of a lack of survey questions repeated consistently across years and countries.
Do democratic regimes depend on public support to avoid backsliding? Does public support, in turn, respond thermostatically to changes in democracy? Two prominent recent studies (Claassen 2020a; 2020b) reinvigorated the classic hypothesis on the positive relationship between public support for democracy and regime survival—and challenged its reciprocal counterpart—by using a latent variable approach to measure mass democratic support from cross-national survey data. However, both studies used only the point estimates of democratic support.
Political trust plays a central role in understanding and theorizing any essential political question, such as regime support, democratic support, and policy preferences. However, we know little about the effects of different types of political trust on policy outcomes. In this study, I examine the relationship between trust in policy-making institutions, trust in policy-implementing institutions, and immunization rates using time-series cross-sectional data. I measure the two types of trust by employing a sophisticated Bayesian Item Response Theory model on 2,108 national surveys covering 150 countries over 47 years.