Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916782560772096 |
|---|---|
| author | Linegar, Mitchell Sinclair, Betsy van der Linden, Sander Alvarez, R. Michael |
| author_facet | Linegar, Mitchell Sinclair, Betsy van der Linden, Sander Alvarez, R. Michael |
| contents | We present a generalizable AI-assisted framework for rapidly generating effective "prebunking" interventions against misinformation. Like mRNA vaccine platforms, our approach uses a stable template structure that can be quickly adapted to counter emerging false narratives. In a preregistered two-wave experiment with 4,293 U.S. registered voters, we test this framework against politically-charged election misinformation -- one of the most challenging domains for misinformation intervention. Our design directly tests scalability by comparing human-reviewed and purely AI-generated inoculation messages. We find that LLM-generated prebunking significantly reduced belief in election rumors (persisting for at least one week) and increased confidence in election integrity across partisan lines. Purely AI-generated messages proved as effective as human-reviewed versions, with some achieving larger protective effects, demonstrating that effective misinformation inoculation can be achieved at machine speed without proportional human effort, offering a scalable defense against the accelerating threat of false narratives across all domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19202 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives Linegar, Mitchell Sinclair, Betsy van der Linden, Sander Alvarez, R. Michael General Economics Economics J.4.1; J.4.2 We present a generalizable AI-assisted framework for rapidly generating effective "prebunking" interventions against misinformation. Like mRNA vaccine platforms, our approach uses a stable template structure that can be quickly adapted to counter emerging false narratives. In a preregistered two-wave experiment with 4,293 U.S. registered voters, we test this framework against politically-charged election misinformation -- one of the most challenging domains for misinformation intervention. Our design directly tests scalability by comparing human-reviewed and purely AI-generated inoculation messages. We find that LLM-generated prebunking significantly reduced belief in election rumors (persisting for at least one week) and increased confidence in election integrity across partisan lines. Purely AI-generated messages proved as effective as human-reviewed versions, with some achieving larger protective effects, demonstrating that effective misinformation inoculation can be achieved at machine speed without proportional human effort, offering a scalable defense against the accelerating threat of false narratives across all domains. |
| title | Towards Generalizable AI-Assisted Misinformation Inoculation: Protecting Confidence Against False Election Narratives |
| topic | General Economics Economics J.4.1; J.4.2 |
| url | https://arxiv.org/abs/2410.19202 |