iiinova — where your digital self keeps evolving

Yamil Ricardo Velez @yamilrvelez.bsky.social · 30/09/2026

I’ve been meaning to post about this repo, but fortunately, Bob beat me to it! Put it this way: I haven’t used the Qualtrics interface to create a survey once this year. Natural language is sufficient!

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Bethany Albertson @albertsonb2.bsky.social · 16/09/2026

Niche political science primary update - David Redlawsk is up in a big way in Delaware's 23rd district. 62% of the vote, with 22% in.

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Brendan Nyhan @brendannyhan.bsky.social · 13/09/2026

Now online: AI, Politics, and Political Science edited by @persily.bsky.social and @jatucker.bsky.social with many chapters by great folks, including ours on the effects of AI on the online info ecosystem Full PDF: drive.google.com/file/d/1Cd1g... Our chapter: preprints.apsanet.org/engage/apsa/...

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Kevin Collins @kwcollins.bsky.social · 21/08/2026

Another new paper on AI-moderated (and AI prompting within) online interviewing academic.oup.com/poq/advance-...

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 21/08/2026

Columbia Political Science is hiring in political methodology at the junior level, starting July 2027. Review of applications begins October 1. Apply here: apply.interfolio.com/190515

apply.interfolio.com

Apply - Interfolio {{$ctrl.$state.data.pageTitle}} - Apply - Interfolio

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 13/08/2026

New paper with Alec Ewig, conditionally accepted at Political Analysis: Retrieval-Augmented Surveys. Most research evaluates the link between elected officials and citizens on highly salient policies. But what if concerns are systematically excluded from the agenda?

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Kevin Elliott @kjephd.bsky.social · 09/07/2026

A very welcome digression into normative territory by @yamilrvelez.bsky.social in which he reflects over why a focus on the specific issues individual citizens care about matters for democracy—drawing from a recent paper of mine as well as Converse—to address deep polisci Qs like citizen competence.

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 08/07/2026

I veer dangerously into normative territory in this piece: how new measures allow us to grapple with the messy, idiosyncratic preferences of Americans, what political specialization means for democracy, and why I’m doing this work in the first place.

substack.com

Mapping Islands of Opinion

The case for measuring, rather than assuming, what voters care about

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Daniel de Kadt @dandekadt.bsky.social · 26/06/2026

Tomorrow I will co-moderate a panel on AI use in grad school with @aricaschuett.bsky.social, who has put together a superb panel: @yiqingxu.bsky.social Rachel Gillum @yamilrvelez.bsky.social @lpargyle.bsky.social Grad students (& others) should tune in! Register: connect.apsanet.org/graduate/

connect.apsanet.org

APSA Graduate Student Committee

American Political Science Association

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Jake Grumbach @jakemgrumbach.bsky.social · 18/06/2026

My new theory paper (early draft) A new reason why concentrated wealth destroys democracy: common ownership A standalone media owner maximizes profit. A common owner like Musk (who owns X + SpaceX) will run the media firm at a loss to increase the $ of his total portfolio tinyurl.com/mcekr7cd

Wealth Concentration and Democratic Erosion:
A Theory of Media and Common Ownership*
Jacob M. Grumbach
June 18, 2026
Abstract
I develop a model of how extreme wealth concentration harms democracy through
a common ownership problem. A standalone profit-seeking media firm will maximize advertising revenue and audience size, producing content that is orthogonal
to democracy. In contrast, an owner who controls both a media firm and other
large firms can use the media firm to produce anti-democratic content, which
reduces the profitability of the media firm but maximizes the profitability of the
overall business portfolio through greater rents from an authoritarian leader (e.g.,
preferential regulatory treatment or public contracts). Anti-democratic content
grows in the owner’s wealth and in the authoritarian premium, the differential
value of concentrated capital under authoritarian governance. I theorize three
extensions: an oligopolistic setting in which reduced democratic content is a common pool resource; an endogenous authoritarian premium based in the authoritarian’s targeting problem; and owners that vary in proactively or reluctantly
supporting the authoritarian.

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 17/06/2026

Why do arguments often change people’s beliefs without changing their attitudes? In a new APSR with @scottclifford.bsky.social and @patrickpliu.bsky.social, we point to belief relevance: arguments are more persuasive when they grapple with the idiosyncratic reasons people hold their political views.

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APSA Preprints @apsa-preprints.bsky.social · 29/05/2026

The APSA Pres Task Force on AI, Politics, & Political Science's report comes in the form of an edited volume identifying questions & establishing a foundation for the empirical study of how politics & governance are affected by AI. Check out these early chapter drafts: shorturl.at/cMZzI #polisky

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 12/05/2026

Happy to share that my Cambridge Element with @patrickpliu.bsky.social, Tailored Experiments: Personalized Interventions Using Generative AI, has been accepted as part of Jamie Druckman’s Experimental Political Science series.

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Brendan Nyhan @brendannyhan.bsky.social · 07/05/2026

Our chapter! preprints.apsanet.org/engage/apsa/... "AI is more likely to reinforce existing patterns of [information] exposure and behavior than it is to transform how people understand and relate to the political world" (w/@jenpans.bsky.social @aasiegel.bsky.social @yamilrvelez.bsky.social)

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Omar Wasow @owasow.bsky.social · 06/05/2026

“Writing is hard.” Thrilled to share that this simple idea led to a new paper in Political Analysis! Where most text methods focus on content, I test if expression is also effortful action. I find simple measures like character counts reveal attitudes and predict voting. cup.org/4cUmoXi 1/

Text as Behavior
Text as Behavior published in Political Analysis

by Omar Wasow

Abstract

Text analysis typically focuses on content—such as sentiment or topic—but expression is also a form of effortful action. Building on this insight, I propose using simple features of open-ended tasks to study text as behavior. This approach treats expression, such as writing, as cognitively, emotionally and temporally “costly” for subjects but inexpensive for researchers. I show basic statistics like the number of characters can approximate effort and significantly improve estimation of quantities of interest, including candidate choice, the probability of turning out to vote and psychological states about which a subject may not be fully aware. Further, these methods can convert nonresponse into informative data; validate survey instruments; serve as mechanism checks; be hard for a subject to “game”; work across different languages and analogize well to real-world situations. In sum, text as behavior can help address a range of issues related to quantifying attitudes and actions.

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 05/05/2026

It was a pleasure to chat with Andy Luttrell about my new paper on persuasion, joint with Scott Clifford and Patrick Liu. It's an exciting time to revisit classic debates about opinion formation and change!

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 01/05/2026

People already hand off decisions to AI when drafting emails, polishing papers, or writing code. Survey research will not be immune. In my new post, I offer an initial look at preferences for AI delegation in surveys and who is most open to it. newinstruments.substack.com/p/all-hail-o...

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John V. Kane @uptonorwell.bsky.social · 15/04/2026

🚨New working paper w/ the legendary @cbwlezien.bsky.social! Lots of talk about inflation & voters' political views. But how well do voters actually understand info about inflation? We find that most citizens conflate changes in rates w/ changes in prices. This has important consequences...

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 06/04/2026

New Substack: how I think about adaptivity in research design, and the strange path that got me there newinstruments.substack.com/p/surveys-th...

newinstruments.substack.com

Surveys That Listen

How Existential Terror Pushed Me Toward Adaptive Research Designs

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Jon Green @jongreen.bsky.social · 03/04/2026

extremely happy to share that "Political Pundits and the Maintenance of Ideological Coalitions" is conditionally accepted at @polbehavior.bsky.social osf.io/8c3fr/files/...

Title: Political Pundits and the Maintenance of Ideological Coalitions
Authors: Allison Wan and Jon Green
Abstract: Political ideologies help groups advance diverse sets of interests under common agendas. However, it is unclear how these groups maintain their norms regarding what it means to be
a member in good standing in a dynamic information environment. Building on theories of “long” political coalitions, we hypothesize that ideological elites’ rhetorical influence on one another will tend to be concentrated and specialized with respect to specific concepts. We
find support for this expectation using an original dataset of over 1,000 prominent political pundits in the United States, in which we infer coalition membership and the diffusion of novel language over a range of specific concepts. While pundits may discuss many concepts,
they tend to “send” language to other pundits in relatively few, resulting in both concentration and specialization of influence within coalitions. These results clarify conceptual distinctions between political ideologies and political philosophies, and demonstrate real-time dynamics of contemporary ideological coalitions.

Figure 1: Stylized expectations. Each node reflects a hypothetical coalition member, colors denote concepts, and arrows indicate members’ influence on each other. For example, member (a) influences members (b), (c), and (e) with respect to one concept, such as immigration, while member (d) influences members (b), (c), and (e) with respect to a different concept, such as health care. Member (e) acts as a broker, both sending and receiving influence across the coalition. Every member is influential, but influence tends to be concentrated (for any given concept, one member accounts for 3/4 of influence) and specialized (4/5 members are influential on only one concept).

Figure 3: Concentration of influence within concepts, observed network compared to permuted networks. Degree, Community, and Concept denote structural features either are or are not preserved in network permutations.

Figure 4: Specialization of influence within concepts, observed network compared to permuted networks. Degree, Community, and Concept denote structural features either are or are not preserved in network permutations.

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Jay Van Bavel, PhD @jayvanbavel.bsky.social · 02/04/2026

Come share your research at NYU! The NYU Social Psychology Program is inviting speakers for 2026-2027! If you’re passing through NYC next year and are interested in giving a research talk nominate yourself using this short form by MAY 15th: docs.google.com/forms/d/1yTI...

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Kevin Collins @kwcollins.bsky.social · 31/03/2026

A thoughtful essay from @yamilrvelez.bsky.social on the LLM-bots-in-surveys problem newinstruments.substack.com/p/who-answer...

newinstruments.substack.com

Who Answered This Survey?

Deception, Satisficing, and Delegation in the Agentic Era

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Patrick Liu @patrickpliu.bsky.social · 25/03/2026

New paper w/ @yamilrvelez.bsky.social! A lot of great research on political microtargeting discounts personalization: tailored ads (using AI or not) rarely beat a single-best message. We define two types of microtargeting, clarify when tailoring matters, & showcase a novel audio-based design.

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Alex Coppock @aecoppock.bsky.social · 19/03/2026

New paper with Don Green and @ethanvporter.bsky.social in the QJPS. After much deliberation, we went with a title that just states the result. 📝 journal: www.emerald.com/qjps/article...

abstract: While attempts to change Americans’ partisanship via persuasive treatments largely fail, partisanship can and does change over time. In this paper, the authors first confirm, via survey and field experiments, that typical campaign messaging in the United States does not budge partisanship. The authors then present experiments in which participants encounter extraordinary hypothetical scenarios (e.g. one party causes economic collapse) before reporting what their partisanship would be under such circumstances. Twelve percent of partisans imagine switching parties in the pro-out-party hypothetical conditions, compared with 5% in the control hypotheticals in which the status quo persists, for a seven-percentage point (SE 1.5 points) difference. These hypothetical shifts are on par with the largest changes in American macropartisanship ever recorded. While the act of ruminating on hypothetical scenarios is not followed by changes in partisanship measured post-treatment, the evidence suggests that extraordinary world events may be able to shift partisan affiliation.

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 19/03/2026

New paper with Stephanie Zonszein! Political news is more important than ever, but local papers are shuttering across the US. In recent years, innovative community-centered outlets led by journalists have taken to WhatsApp and social media to reach groups such as immigrants. What are their effects?

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Alex Coppock @aecoppock.bsky.social · 09/03/2026

Congratulations to @yamilrvelez.bsky.social, @patrickpliu.bsky.social, and @scottclifford.bsky.social ! we think attitudes are some function of beliefs; our exps routinely move beliefs but not (even correlated) attitudes. This team found a way to guess which beliefs matter more (and they do!)

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 09/03/2026

Conditionally accepted at the APSR (w/ @scottclifford.bsky.social & @patrickpliu.bsky.social): Why does political information so often change beliefs but NOT attitudes? We highlight the role of belief relevance, or the extent to which beliefs bear on attitudes.

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Semra Sevi @semrasevi.bsky.social · 03/03/2026

Science communication has never been more important. In this animation, @yamilrvelez.bsky.social, Donald Green and I break down our research exploring whether AI chatbots can increase political engagement among young, politically unaligned voters. Link to animation: www.youtube.com/watch?v=iCuX...

youtube.com

Can AI Chatbots Increase Political Engagement of Young Unaligned Voters?

YouTube video by Semra Sevi

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Dan Silverman @dmsilverman.bsky.social · 19/12/2025

People feeling hopeless about AI/LLMs spoiling all online polling and survey research should take a look at what @yamilrvelez.bsky.social is doing with “Pulse” — a tool to detect proof of life that’s compatible with Quatrics

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Jay Van Bavel, PhD @jayvanbavel.bsky.social · 17/12/2025

An AI Voter bot improves knowledge about politics But, the AI bot has weak effects on downstream outcomes like vote preferences and party evaluations among respondents whose primary issue position aligns closely with one of the parties. Partisan action is hard to change. www.pnas.org/doi/10.1073/...

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 12/12/2025

This paper was a blast to work on. The challenge: present party positions across many issues, in real time, using language voters actually use. 🧵 on why we went with a more involved retrieval-based approach and where I think these tools are headed.

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Semra Sevi @semrasevi.bsky.social · 08/12/2025

🚨Excited to share our new paper published in PNAS (joint with @yamilrvelez.bsky.social and Don Green)! AI can enhance political knowledge and provide balanced information about politics with proper guardrails and vetted sources (e.g., party platforms). www.pnas.org/doi/full/10....

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Lisa Argyle @lpargyle.bsky.social · 04/12/2025

Looking for a tl;dr for these two excellent papers? I've got you covered: www.science.org/doi/10.1126/...

science.org

Political persuasion by artificial intelligence

Large-scale studies of persuasive artificial intelligence reveal an extensive threat of misinformation

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David Rand @dgrand.bsky.social · 04/12/2025

🚨 New in Nature+Science!🚨 AI chatbots can shift voter attitudes on candidates & policies, often by 10+pp 🔹Exps in US Canada Poland & UK 🔹More “facts”→more persuasion (not psych tricks) 🔹Increasing persuasiveness reduces "fact" accuracy 🔹Right-leaning bots=more inaccurate

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 24/11/2025

As @seanjwestwood.bsky.social's terrifying new PNAS article demonstrates, LLMs can now pass almost every attention check, mirror personas, stay consistent across pages, and systematically bias responses in the aggregate. So here’s a different angle: verify physical presence, not text.

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Julia M. Rohrer @dingdingpeng.the100.ci · 25/08/2025

Ever stared at a table of regression coefficients & wondered what you're doing with your life? Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social) Preprint: doi.org/10.31234/osf... Website: j-rohrer.github.io/marginal-psy...

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities

Abstract
Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals).

Illustrated are
1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals
2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and
3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

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Ethan Porter @ethanvporter.bsky.social · 19/08/2025

Today, @catiebailard.bsky.social and I take the reins at GW's Institute for Data, Democracy + Politics! We'll work to make IDDP a one-stop shop for research on misinfo, platform manipulation, and threats to democratic ideals. To follow our work + learn about opportunities, visit: bit.ly/45GLggi

signup.e2ma.net

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 08/07/2025

I had a blast presenting my work on tailored experiments and adaptive surveys at the Summer Institute of Computational Social Science. Nothing better than a room full of engaged and smart people getting into the weeds about algorithms, causal inference, and the messy realities of working with AI.

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Political Analysis @polanalysis.bsky.social · 04/06/2025

Currently in FirstView: In “Crowdsourced Adaptive Surveys,” @yamilrvelez.bsky.social introduces a methodology (CSAS) that converts open-ended text from participants into survey items and applies a multi-armed bandit algorithm to determine which questions should be prioritized in the survey.

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Soubhik Barari @soubhikbarari.bsky.social · 15/04/2025

Another survey methods talk at 𝗡𝗬𝗔𝗔𝗣𝗢𝗥 coming up next week to put on your calendar: Wed April 23 - 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗦𝘂𝗿𝘃𝗲𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 (led by @yamilrvelez.bsky.social , @joshua-lerner.bsky.social , and myself) RSVP below 👇

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Patrick Liu @patrickpliu.bsky.social · 02/04/2025

🧵 Why do facts often change beliefs but not attitudes? In a new WP with @yamilrvelez.bsky.social and @scottclifford.bsky.social, we caution against interpreting this as rigidity or motivated reasoning. Often, the beliefs *relevant* to people’s attitudes are not what researchers expect.

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Tom Costello @tomcostello.bsky.social · 03/04/2025

New paper! AI lets us query humanity’s collective knowledge (or a subset of it) – rapidly addressing any given concern about vaccines in detail. So can information-focused LLM conversations shift vaccination intentions? In an RCT of 1,124 HPV vax-hesitant parents: yes!

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Scott Clifford @scottclifford.bsky.social · 02/04/2025

New working paper with two great coauthors!

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 02/04/2025

While there is a growing body of work suggesting information can change beliefs, effects on attitudes tend to be muted. We sketch out why this might be the case, drawing attention to the role of belief relevance. See 🧵 below!

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Yamil Ricardo Velez @yamilrvelez.bsky.social · 12/02/2025

🧵 Knowing which questions to ask is one of the most critical aspects of survey design, but we often rely on guesswork to devise questionnaires. What if we were to rely directly on participants?

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Cambridge University Press Political Science & IR @cambup-polsci.cambridge.org · 12/02/2025

#OpenAccess from @polanalysis.bsky.social - Crowdsourced Adaptive Surveys - cup.org/4aY2OH4 - @yamilrvelez.bsky.social "This paper introduces a crowdsourced adaptive survey methodology (CSAS) that unites advances in natural language processing and adaptive algorithms..." #FirstView

The image features the text "POLITICAL ANALYSIS" in large white letters on a crimson background, with "#OpenAccess" in smaller white letters above a yellow bar below.

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