A Large-scale Time-aware Agents Simulation for Influencer Selection in Digital Advertising Campaigns

TL;DR

This paper introduces a large-scale, time-aware social influencer simulation using LLMs to improve influencer selection in digital advertising, outperforming traditional methods.

Contribution

The work presents a novel Time-aware Influencer Simulator leveraging LLMs for large-scale social network simulation in advertising campaigns.

Findings

Our method outperforms traditional numerical feature-based approaches.

Simulating user timelines simplifies scaling to large social networks.

LLM-based agents enhance decision-making in influencer marketing.

Abstract

In the digital world, influencers are pivotal as opinion leaders, shaping the views and choices of their influencees. Modern advertising often follows this trend, where marketers choose appropriate influencers for product endorsements, based on thorough market analysis. Previous studies on influencer selection have typically relied on numerical representations of individual opinions and interactions, a method that simplifies the intricacies of social dynamics. In this work, we first introduce a Time-aware Influencer Simulator (TIS), helping promoters identify and select the right influencers to market their products, based on LLM simulation. To validate our approach, we conduct experiments on the public advertising campaign dataset SAGraph which encompasses social relationships, posts, and user interactions. The results show that our method outperforms traditional numerical feature-based approaches and methods using limited LLM agents. Our research shows that simulating user timelines and content lifecycles over time simplifies scaling, allowing for large-scale agent simulations in social networks. Additionally, LLM-based agents for social recommendations and advertising offer substantial benefits for decision-making in promotional campaigns.