Validating generative agent-Based modeling in social media simulations through the lens of the friendship paradox

Generative Agent-Based Modeling (GABM) is an emerging simulation paradigm that integrates the reasoning capabilities of Large Language Models (LLMs) with traditional Agent-Based Modeling to replicate complex social behaviors, including user interactions on social media platforms. While GABM has been employed to study localized phenomena on social media, such as opinion formation and information propagation, its capacity to capture global network-level phenomena remains underexplored. In this paper, we address this gap by investigating whether GABM-based social media simulations exhibit the Friendship Paradox (FP) – a counterintuitive network phenomenon where individuals, on average, have fewer friends than their friends. We design a GABM-based framework for social media simulation, featuring generative agents that emulate real users by incorporating distinct personalities, interests, and behaviors. Leveraging three real-world Twitter datasets centered on the US 2020 Election, UK Brexit, and the QAnon conspiracy, we demonstrate that the FP and its generalized forms emerge in GABM-based simulations. Consistent with real-world social media, we observe a hierarchical structure where generative agents preferentially connect with others exhibiting superior attributes, such as greater activity or influence, without being instructed with any behavioral rules. Furthermore, our analysis reveals that infrequent connections with highly connected agents primarily drive the Friendship Paradox, mirroring established patterns in real-world networks. Overall, our findings validate the ability of GABM to replicate global social media phenomena, highlighting its potential as a robust framework for modeling and analyzing complex social behaviors at scale.