Leveraging Generative AI to identify narrative evolution, and target audiences in social media

Oregon Convention Center 

In an era where information is ubiquitous but increasingly unregulated, malicious actors are leveraging the ambiguity of the information environment to provoke specific responses within target audiences via the use of narratives. In public sector applications, the intent of narrative manipulation is often to affect public debate on issues, electoral processes, and policy decisions. To formulate effective responses, policy makers must understand what narratives are being propagated, who is being targeted, and the potential impacts. However, this type of analysis is often complicated by the volume of content and noise in the information environemnt. Leveraging large volumes of data from social media, inputs from geopolitical monitoring systems, and a predictive modeling capability combining LLMs with traditional statistical simulation approaches, we seek to (1) identify key features in specific narratives in social media data, (2) identify shifts in narratives over time, (3) identify potential target audiences of specific narratives, and (4) identify impact to a target audience.

social media

public policy impact

narrative assessment

generative AI

large langauge models 

Section on Text Analysis