Patrick Liu

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.

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.)

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.

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.

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.

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.

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.

🧵 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.

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.

🧵 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.

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.

🧵 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.

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.

🧵 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.

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.

🧵 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.

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!