CHORUS: An Agentic Framework for Generating Realistic Deliberation Data

TL;DR

Chorus is a framework that uses autonomous, persona-based AI agents to generate realistic online deliberation data, addressing scarcity and quality issues in discourse research.

Contribution

It introduces an agentic, memory-equipped, and temporally modeled framework for creating high-quality, realistic deliberation discussions with external resource access.

Findings

Framework generates discussions with high content realism.

Discussions exhibit coherence and analytical utility.

Evaluations confirm practical usefulness for discourse analysis.

Abstract

Understanding the intricate dynamics of online discourse depends on large-scale deliberation data, a resource that remains scarce across interactive web platforms due to restrictive accessibility policies, ethical concerns and inconsistent data quality. In this paper, we propose Chorus, an agentic framework, which orchestrates LLM-powered actors with behaviorally consistent personas to generate realistic deliberation discussions. Each actor is governed by an autonomous agent equipped with memory of the evolving discussion, while participation timing is governed by a principled Poisson process-based temporal model, which approximates the heterogeneous engagement patterns of real users. The framework is further supported by structured tool usage, enabling actors to access external resources and facilitating integration with interactive web platforms. The framework was deployed on the \textsc{Deliberate} platform and evaluated by 30 expert participants across three dimensions: content realism, discussion coherence and analytical utility, confirming Chorus as a practical tool for generating high-quality deliberation data suitable for online discourse analysis