A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios

• URL: http://arxiv.org/abs/2511.15857v1
• Date: Wed, 19 Nov 2025 20:25:34 GMT
• Title: A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios
• Authors: Anthony Wise, Xinyi Zhou, Martin Reimann, Anind Dey, Leilani Battle,
• Abstract summary: Similar to social media bots that shape public opinion, ChatBots like ChatGPT can persuade users to alter their behavior.<n>We conducted a crowdsourced study, where 336 participants interacted with a neutral or one of two value-framed ChatBots while deciding to alter US defense spending.<n>When the frame misaligned with their values, some participants reinforced their original preference, originally considered rare in the literature.
• Score: 8.230880820861133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Similar to social media bots that shape public opinion, healthcare and financial decisions, LLM-based ChatBots like ChatGPT can persuade users to alter their behavior. Unlike prior work that persuades via overt-partisan bias or misinformation, we test whether framing alone suffices. We conducted a crowdsourced study, where 336 participants interacted with a neutral or one of two value-framed ChatBots while deciding to alter US defense spending. In this single policy domain with controlled content, participants exposed to value-framed ChatBots significantly changed their budget choices relative to the neutral control. When the frame misaligned with their values, some participants reinforced their original preference, revealing a potentially replicable backfire effect, originally considered rare in the literature. These findings suggest that value-framing alone lowers the barrier for manipulative uses of LLMs, revealing risks distinct from overt bias or misinformation, and clarifying risks to countering misinformation.

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