Problem
Research questions and friction points this paper is trying to address.
user simulation
temporal evolution
personalized behavior
event-driven modeling
behavioral dynamics
TWICE: An LLM Agent Framework for Simulating Personalized User Tweeting Behavior with Long-term Temporal Features
🤖 AI Summary
This work addresses the limitations of existing user simulation methods, which often treat users as static entities or rely on overly generalized historical contexts, thereby failing to capture the dynamic evolution of individual behavior. To overcome this, the paper proposes TWICE, a novel framework that introduces a life-event-driven memory mechanism to model how users’ past experiences shape their current expressions. TWICE integrates structured user profiles with a two-stage generation process—decoupling content planning from stylistic adaptation—to enable fine-grained modeling of behavioral dynamics. Built upon large language models, TWICE demonstrates significant improvements over strong baselines on a large-scale longitudinal Twitter dataset, achieving superior performance across comprehensive metrics including authenticity, consistency, and human-likeness.
📝 Abstract
User simulators are often used to generate large amounts of data for various tasks such as generation, training, and evaluation. However, existing approaches concentrate on collective behaviors or interactive systems, struggling with tasks that require modeling temporal characteristics. To address this limitation, we propose TWICE, an LLM-based framework that leverages the long-term temporal and personalized features of social media data. This framework integrates personalized user profiling, an event-driven memory module, and a workflow for personalized style rewriting, enabling simulation of personalized user tweeting behavior while capturing long-term temporal characteristics. In addition, we conduct a comprehensive evaluation with a focus on analyzing tweeting style and event-based changes in behavior. Experiment results demonstrate that our framework improves personalized user simulation by effectively incorporating temporal dynamics, providing a robust solution for long-term behavior tracking.
Innovation
Methods, ideas, or system contributions that make the work stand out.
event-driven memory
temporal evolution
personalized user simulation
LLM-based framework
two-stage workflow
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