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The Affiliated Institutions Could Not Be Determined From The Provided Context Or

The Affiliated Institutions Could Not Be Determined From The Provided Context Or: Research Institution — Prolific Citations Library

Browse 1 peer-reviewed paper from The Affiliated Institutions Could Not Be Determined From The Provided Context Or spanning Artificial Intelligence and Social Science, Persuasion Studies (2025). Research powered by Prolific's high-quality participant data.

This page lists 1 peer-reviewed paper from researchers at The Affiliated Institutions Could Not Be Determined From The Provided Context Or in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (1 of 1)

A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies

Authors: Z Chen, J Kalla, Q Le, S Nakamura-Sakai

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: The affiliated institutions could not be determined from the provided context or an external search of the URL.

Research Area: Artificial Intelligence and Social Science, Persuasion Studies, Political Persuasion, LLM Chatbots, Democratic Societies

Discipline: Artificial Intelligence, Social Science

The study evaluates the cost-effectiveness and persuasive risks of Large Language Model (LLM) chatbots in political contexts, finding that while LLMs are as persuasive as campaign ads under exposure, their large-scale influence is currently limited by scalability and cost barriers.

Methods: Two survey experiments combined with real-world simulation exercises to measure the persuasiveness of LLM chatbots compared to traditional campaign tactics, focusing on both exposure and acceptance phases of persuasion.

Key Findings: Short- and long-term persuasive effects of LLMs, cost-effectiveness of LLM-based persuasion ($48-$74 per persuaded voter), and scalability compared to traditional campaign approaches.

Citations: 7

Sample Size: 10417