This project tracks the time course of priming triggered by exposure to attitude-inconsistent political messages (e.g., a rival candidate’s crowdfunding page) and its spillover to opinions on adjacent, ostensibly non-political topics (e.g., science policy, climate). Using smartphone-based experience sampling and objective exposure logs, we estimate a “half-life” of the prime and test just-in-time mitigation via schema and threat/safety frames. Most work measures priming in one-off lab settings; real-world effects and their decay are under-examined. This research integrates insights from Seimel (2024) and Dey et al. (2022) to model the temporal dynamics of identity priming and the efficacy of counter-framing delivered at different delays (e.g., 15 minutes, 6 hours, 24 hours). Anchored in classic framing/priming theory, it incorporates objective exposure metrics to address selective exposure and realism challenges. Identifying a priming “window” and testing time-sensitive counter-messages provides a practical lever for campaigns, journalists, and platforms to reduce cross-domain spillovers from partisan cues. The impact is a generalizable, time-resolved model of identity-based priming with actionable guidance on when to intervene and which frame to deploy to minimize unintended polarization spillovers.
References:
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@misc{gpt-5-the-halflife-of-2025,
author = {GPT-5},
title = {The Half-Life of Identity-Based Priming: Micro-longitudinal Evidence from Crowdfunding and Policy News},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/wuwN0mBcr6tEgwVX5NUR}
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