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Curtis Puryear

@curtispuryear.bsky.social

Assistant Professor at UNCW studying morality, politics, and intergroup conflict. puryearc@uncw.edu http://curtispuryear.com

12 Posts434 Follower579 Folgt
Curtis Puryear@curtispuryear.bsky.social · 5mo ago

Interested in why moral conflict is so common on social media? Join us at #SPSP 2026 for our symposium. We’ll present new findings on how platforms shape digital discourse and explore pathways toward healthier online environments 🗓 Saturday, the 28th | 9:30–10:40 AM 📍 Room E270, Level 2

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Political Behavior@polbehavior.bsky.social · 10mo ago

Do ethnic minority interest parties grow through programs, or people? Schaaf, Otjes & Spierings show that DENK’s support in the Netherlands stems mainly from personal & religious networks, while online ties matter less. #ComparativePolitics Read more: link.springer.com/article/10.1...

The Role of Networks in Mobilization for Ethnic Minority Interest Parties - Political Behavior

Recently, parties that are run by and for ethnic minority citizens with a migration background have become more prominent. They can be considered a manifestation of ethnic political segregation. A key example of such a party is DENK in the Netherlands. So far, the explanatory literature has focused on how programmatic considerations drives voting for these parties. Other factors, such as the role of social networks in mobilization, have received limited testing and limited exploration in more detail. Furthermore, the literature on social networks is mainly based on majority populations. To inform our understanding of the role of social networks in voting (in general but also particularly among ethnic minority communities and for ethnic minority interest parties) this paper analyzes the voting behavior for DENK focusing on the role of personal, online and religious networks. The paper uses both qualitative interviews (with bicultural youth in the third largest city of the Netherlands in 2022) and quantitative surveys (the 2021 Dutch Ethnic Minority Electoral Study). Our analysis points to the importance of religious and personal networks for voting for DENK, whereas online networks appear to be less relevant.

link.springer.com

Eiko Fried@eikofried.bsky.social · 10mo ago

Intervening on a central node in a network likely does little given that its connected neighbors will "flip it back" immediately. Happy to see this position supported now. "Change is most likely [..] if it spreads first among relatively poorly connected nodes." www.nature.com/articles/s41...

Transformation starts at the periphery of networks where pushback is less - Scientific Reports

Scientific Reports - Transformation starts at the periphery of networks where pushback is less

nature.com

Danica Dillion@danicajdillion.bsky.social · 12mo ago

New preprint 🚨 Cognitive bottlenecks make LLMs more morally aligned with people 🧠🤖 We made AI “think” more like people by narrowing its focus to a few key moral cues. This AI better predicted people’s moral judgments & was more trusted. 🧵 ⬇️

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Tom Pepinsky@tompepinsky.com · last yr.

We live in an era of democratic backsliding. But the terminology of "backsliding" isn't up to the task of making sense of the deep crisis of liberal democracy around the world. I've just finished a working paper that lays out what I think is going on. tl;dr it's about the state and society 🧵

State, Society, and the Politics of Democratic Backsliding

Recent scholarship on democratic backsliding has focused on measuring its global prevalence and identifying the causal processes and mechanisms that produce or

papers.ssrn.com

Curtis Puryear@curtispuryear.bsky.social · last yr.

New preprint! We developed new measurement tools to examine moralization in ~2B Twitter/X & Reddit posts and ~5M traditional media texts. Key finding: moralization increased markedly on social media from 2013-2021; more than traditional media; associated with multiple user dynamics 🧵👇

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Julia M. Rohrer@dingdingpeng.the100.ci · last yr.

Thanks to everybody who chimed in! I arrived at the conclusion that (1) there's a lot of interesting stuff about interactions and (2) the figure I was looking for does not exist. So, I made it myself! Here's a simple illustration of how to control for confounding in interactions:>

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Julia M. Rohrer@dingdingpeng.the100.ci · last yr.

Does anybody have a good visualization to explain how interactions can be confounded, and why interactions require interaction controls? @urisohn.bsky.social maybe? (Asking because I have an idea, but want to check out what exists already before investing the effort)

Omar Wasow@owasow.bsky.social · last yr.

Is America an oligarchy? Bernie and AOC say yes, touring the country to “Fight Oligarchy.” Other Democratic leaders aren’t so sure. With the debate heating up, I wanted to share a few insights from our recent paper on who’s funding American politics. 🧵 cup.org/4cfm0Az

Plutopopulism: Wealth and Trump’s Financial Base SEAN KATES, ERIC MANNING, TALI MENDELBERG and OMAR WASOW Comparative scholarship suggests authoritarian candidates often rely on backing from the wealthy. The wealthy are also said to play an important role in American campaign finance. Studies of Donald Trump, however, found that he drew significant support from white Americans with less education and privilege. We evaluate wealthy and non-wealthy Americans’ financial support for Trump, compared to other candidates, by constructing a comprehensive dataset of property values matched to contributions and voter files. We find Trump underperformed among wealthy Republican donors while mobilizing new non-wealthy donors. Trump also diversified the donorate, especially by education. That is, Trump built an unusual coalition of wealthy and non-wealthy donors. Our results support an alternative, “plutopopulist” model of Trump’s financial base.
Omar Wasow@owasow.bsky.social · last yr.

Were wealthy donors key to Trump’s campaigns in 2016 and 2020? I'm thrilled to announce a new paper in which Sean Kates, Eric Manning, Tali Mendelberg and I analyzed data on 108 million (!) homeowner-voters. See “Plutopopulism: Wealth and Trump’s Financial Base.” Open access: cup.org/4cfm0Az 🧵 1/

Title page of an academic article titled “Plutopopulism: Wealth and Trump’s Financial Base.” Authors listed are Sean Kates (University of Pennsylvania), Eric Manning (Princeton University), Tali Mendelberg (Princeton University), and Omar Wasow (University of California, Berkeley). The abstract below the title states: Comparative scholarship suggests authoritarian candidates often rely on backing from the wealthy. The wealthy are also said to play an important role in American campaign finance. Studies of Donald Trump, however, found that he drew significant support from white Americans with less education and privilege. We evaluate wealthy and non-wealthy Americans’ financial support for Trump, compared to other candidates, by constructing a comprehensive dataset of property values matched to contributions and voter files. We find Trump underperformed among wealthy Republican donors while mobilizing new non-wealthy donors. Trump also diversified the donorate, especially by education. That is, Trump built an unusual coalition of wealthy and non-wealthy donors. Our results support an alternative, “plutopopulist” model of Trump’s financial base. This study demonstrates the importance of studying both non-wealthy and wealthy Americans, the group who give the most but whose individual behavior has been studied the least. Open access link to paper: http://cup.org/4cfm0Az
Matthew Facciani@matthewfacciani.bsky.social · last yr.

New study analyzed 145 years of U.S. congressional speeches and found a clear shift away from evidence-based language toward more intuition-based language—especially since the mid-1970s. www.nature.com/articles/s41...

Computational analysis of US congressional speeches reveals a shift from evidence to intuition - Nature Human Behaviour

This research explores the linguistic traces of evidence-based reasoning and intuitive decision-making in congressional speeches from 1879 to 2022. The analysis suggests that evidence-based language h...

nature.com

Curtis Puryear@curtispuryear.bsky.social · last yr.

If you want to see more of our data on longitudinal trends in moralization, I'm giving a symposium talk tomorrow morning at 8:00AM in Four Seasons Ballroom 1 #SPSP2025

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William J. Brady@williambrady.bsky.social · last yr.

@curtispuryear.bsky.social a study of a decade of twitter posts shows and new measure of moralization shows that moralization has substantially increased over time (d = .45!). Driven by both self selection and within-user increase over time! #comppsych #spsp2025 (also stay tuned for this paper😎)

Jon Green@jongreen.bsky.social · 2y ago

Ok, thread time. Closed-ended survey responses are efficient and easy to analyze, but limit what respondents can say. Open-ended responses are useful for letting respondents answer with more depth in their own words (as opposed to yours).

Jon Green@jongreen.bsky.social · 2y ago

Online/open access in @polanalysis.bsky.social w/ @willrhobbs.bsky.social: a theory and method for inferring attitudes in open-ended survey responses www.cambridge.org/core/journal...

Abstract: Past work on closed-ended survey responses demonstrates that inferring stable political attitudes requires separating signal from noise in “top of the head” answers to researchers’ questions. We outline a corresponding theory of the open-ended response, in which respondents make narrow, stand-in statements to convey more abstract, general attitudes. We then present a method designed to infer those attitudes. Our approach leverages co-variation with words used relatively frequently across respondents to infer what else they could have said without substantively changing what they meant—linking narrow themes to each other through associations with contextually prevalent words. This reflects the intuition that a respondent may use different specific statements at different points in time to convey similar meaning. We validate this approach using panel data in which respondents answer the same open-ended questions (concerning healthcare policy, most important problems, and evaluations of political parties) at multiple points in time, showing that our method’s output consistently exhibits higher within-subject correlations than hand-coding of narrow response categories, topic modeling, and large language model output. Finally, we show how large language models can be used to complement—but not, at present, substitute—our “implied word” method.