Project Description
The primary goal is to develop and evaluate a framework, grounded in Information Science, that uses AI General Language Models (GLMs) to develop Russian disinfolklore counter narratives. Russian disinformation constantly evolves and is tailored to specific audiences. Fact-checking, which simply points out lies, is only effective for some audiences, while conspiracy theories have a much wider appeal. The Scottish diplomat and philosopher, Stephen Douglas, developed the concept of Russian disinfolklore. He suggested that Russian disinformation is so effective because it is simple and familiar, taking the form of folklore that everyone knows from childhood. This makes it difficult to stop the widespread circulation of sensational disinformation. Our project, divided among three talented undergraduates with diverse cultural backgrounds, will use an Information Science approach and new AI tools. We will map debunked Russian disinfolklore narratives to previously established techniques for effective counter-narratives.
Tasks and Responsibilites
This research is a direct extension of Phase 1 of our study, where debunked disinfolklore narratives penetrating Western media (in the U.S. and elsewhere) were mapped to their original sources. As Phase 2 of the project, each student will create an AI agent to generate counter-narratives for the most effective Russian disinfolklore themes identified. Each student in this project will capitalize on their unique and diverse cultural backgrounds to translate cultural knowledge into a credible AI agent persona to generate their own unique folkloric narratives. Their task will be to build with n8n tool an AI agent to recognize and analyze disinfolklore narratives originating directly from Russian sources.
Desired Qualifications
None Listed.