Feedback welcome! Link: osf.io/gqfvm

2. Though elites rarely buck their party, candidates with counter-stereotypical or ambiguous platforms on an issue that matters deeply to opposing party voters may gain from targeted appeals. (The 2016 Trump campaign supposedly sought to do just this.)

1. Targeting by demographics may be ineffective not just b/c personalized rhetoric < personalized *issue selection*, but also b/c they are only noisy proxies of issue priorities. Explains why campaign analytics firms are now modeling attitudes using survey data + demographics!

Partisans chose opposing party candidates about 26–27% of the time with the baseline ad but about 42–43% of the time when the ad was issue-targeted. These results speak to the limits of demographic/personality microtargeting and to media campaign strategy.

The standout result: ads tailored to a voter’s personally important issue increased candidate support by ~10 percentage points relative to the pre-tested ad. In our conjoint, that equates to roughly one-fourth of the gap in support for co-partisans vs out-partisans.

We compared LLM-generated audio campaign ads tailored to demographics, Big Five traits, or self-identified top issues in a conjoint. Demographic and personality tailoring did little against a pre-tested cost-of-living message. Fully tailored personality ads actually did worse.

Most studies use demographic and personality data to optimize the framing and presentation of ads. But microtargeting isn’t just rhetorical. Critics typically envision multimedia campaign ads that selectively play to voters’ private and most salient concerns.

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.

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.

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...

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?

Our study draws renewed attention to the distinction between beliefs and attitudes. It also showcases how LLMs can be used to peer into belief systems. We welcome any feedback!

Across 2 studies, focal + distal counterarguments reduced focal + distal belief strength (respectively). But focal arguments had larger and more durable effects on downstream attitudes.

We explore mechanisms in the paper, e.g., ppl recalled focal args better than distal args a week later.

Ex: Respondent said they care about public infrastructure.

In the same wave, they held the following convo with an AI chatbot. After GPT synthesized a summary attitude, focal belief, and distal belief, they saw treatment/placebo text and answered pre- and post-treatment Qs.

Ordinarily, a design that a) elicits personally important issues + relevant beliefs through convos, b) uses tailored treatments, & c) measures persistence of effects would require 3 survey waves and immense resource/labor costs.

We overcome these issues (+ replicate) using LLMs.

We engaged ppl in direct dialogue to discuss an issue they care about and the reasons for their stance. We generated a “focal” belief from this text convo and a less relevant “distal” belief, then randomly assigned a focal belief counterargument, distal argument, or placebo text.

Identifying relevant beliefs is challenging! Fact-checking studies rely on databases to identify prevalent misinfo and network methods map mental associations at a group level, but the beliefs ppl personally treat as relevant on an issue are diverse and shaped by political preferences.

We build on classic psych models that represent attitudes as weighted sums of beliefs about an object. The impact of belief change on subsequent attitude change increases with the belief’s weight, capturing its relevance. Low relevance = small effect of info on attitudes.

There is a tendency to conclude that attitudes (evaluations of an object) are stickier than beliefs (factual positions) about the object, possibly b/c of motivations to preserve attitudes.

But this assumes beliefs targeted by the informational treatment matter for the attitude.

Puzzle: Studies widely find learning occurs w/o attitude change. Correcting vaccine misinformation fails to alter vax intentions, reducing misperceptions of the # of immigrants doesn’t reduce hostility, learning about govt spending doesn’t affect econ policy preferences… the list goes on.

When Information Affects Attitudes: The Effectiveness of Targeting Attitude-Relevant Beliefs Do citizens update strongly held beliefs when presented with belief-incongruent information, and does such updating affect downstream attitudes? Though fact-checking studies find that corrections reli...

Link: go.shr.lc/4j9My8H

We find arguments targeting relevant beliefs produce strong and durable attitude change—more than arguments targeting distal beliefs. To ID relevant beliefs, we elicited deeply held attitudes + interviewed ppl about their reasons using an LLM chatbot. More on why below!

🧵 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.