# How topic content shapes LLM personality-tailored persuasion: semantic anchoring and topic stereotype effects

## TL;DR

This study shows how the content of messages generated by AI affects how well they persuade people based on personality traits and topic stereotypes.

## Contribution

The study introduces semantic anchoring and topic stereotype effects as critical factors in improving LLM-based personalized persuasion.

## Key findings

- Semantic anchoring to core features enhances consistent personality-matching effects in persuasion.
- Topic-specific stereotypes influence message effectiveness independently of recipient personality.
- Aligning messages with widely shared topic expectations increases audience preference across traits.

## Abstract

Large language models (LLMs) have shown promise in generating personality-tailored persuasive messages, yet their effectiveness remains inconsistent across contexts. This research systematically investigated how the characteristics of recommended products or actions shapes the efficacy of LLM- generated personality-tailored persuasion through three experimental studies (N = 618). Study 1 revealed that personality-matching effects were limited and inconsistent when the core features of the recommended product or action were not controlled. Qualitative analysis suggested that uncontrolled semantic variation across personality framings obscured persuasive effects. Study 2 demonstrated that explicitly anchoring messages to core product or action features—while allowing stylistic variation—produced robust personality-matching effects across multiple traits and topics. Study 3 extended findings across diverse domains (health, consumer products, entertainment, prosocial behavior) and confirmed that topic-specific stereotypes systematically influence message effectiveness independent of recipient personality. Messages aligning with widely shared topic expectations (e.g., high-Extraversion framing for music festivals, high-Agreeableness framing for donations) were preferred across audiences regardless of individual traits. These findings reveal two critical boundary conditions for LLM-based personalized persuasion: stabilizing core content through semantic anchoring facilitates the emergence of personality-matching effects, and topic stereotypes create baseline preferences that may amplify or attenuate personalization benefits. Practically, effective implementation requires anchoring core content while modulating style, and evaluating topic stereotypes before applying trait customization. This work clarifies when and how generative AI can reliably enhance persuasive communication across mental health, public health, and consumer domains. Study 3 extended the findings across diverse domains (health, consumer products, entertainment, and prosocial behavior) and suggested a consistent role of topic-specific stereotypes in shaping message effectiveness, above and beyond recipient personality.