Social media platforms generate massive volumes of heterogeneous data, capturing user behaviors, textual content, temporal dynamics, and network structures. Analyzing such data is crucial for understanding phenomena such as opinion dynamics, community formation, and information diffusion. However, discovering insights from this complex landscape is exploratory, conceptually challenging, and requires expertise in social media mining and visualization. Existing automated approaches, though increasingly leveraging large language models (LLMs), remain largely confined to structured tabular data and cannot adequately address the heterogeneity of social media analysis. We present SIA (Social Insight Agents), an LLM agent system that links heterogeneous multi-modal data -- including raw inputs (e.g., text, network, and behavioral data), intermediate outputs, mined analytical results, and visualization artifacts -- through coordinated agent flows. Guided by a bottom-up taxonomy that connects insight types with suitable mining and visualization techniques, SIA enables agents to plan and execute coherent analysis strategies. To ensure multi-modal integration, it incorporates a data coordinator that unifies tabular, textual, and network data into a consistent flow. Its interactive interface provides a transparent workflow where users can trace, validate, and refine the agent's reasoning, supporting both adaptability and trustworthiness. Through expert-centered case studies and quantitative evaluation, we show that SIA effectively discovers diverse and meaningful insights from social media while supporting human-agent collaboration in complex analytical tasks.

翻译：社交媒体平台产生海量异构数据，涵盖用户行为、文本内容、时间动态和网络结构。分析此类数据对于理解意见动态、社区形成和信息传播等现象至关重要。然而，从这一复杂环境中发掘洞见具有探索性、概念挑战性，且需要社交媒体挖掘与可视化领域的专业知识。现有的自动化方法尽管越来越多地利用大语言模型（LLMs），仍主要局限于结构化表格数据，无法充分应对社交媒体分析的异构性。本文提出SIA（Social Insight Agents），一种通过协调智能体流链接异构多模态数据——包括原始输入（如文本、网络和行为数据）、中间输出、挖掘的分析结果和可视化产物——的LLM智能体系统。该系统以自底向上、将洞见类型与适当挖掘及可视化技术相连接的分层分类法为指导，使智能体能够规划并执行连贯的分析策略。为确保多模态整合，SIA引入数据协调器，将表格、文本和网络数据统一为一致的数据流。其交互式界面提供透明的工作流，用户可追踪、验证并优化智能体的推理过程，兼顾适应性与可信度。通过以专家为中心的案例研究和定量评估，我们证明SIA能有效从社交媒体中发现多样且有意义的洞见，同时在复杂分析任务中支持人机协作。