Linking Heterogeneous Data with Coordinated Agent Flows for Social Media Analysis

Authors

Shifu Chen (Zhejiang University), Dazhen Deng (Zhejiang University), Zhihong Xu (Zhejiang University), Sijia Xu (School of Software Technology), Linyu Qin (Zhejiang University), Tai-Quan Peng (Michigan State University), Yingcai Wu (Zhejiang University)

Presentation

Session: My followers need to know about this!
Time: Thursday, Nov 12, 11:00 – 11:12 (US/Eastern) · session 10:00 – 11:30
Location: Hall America center

Keywords

Social Media Data, Heterogeneous Data, LLM Agent, Insight Discovery, Visual Analytics

Abstract

Social media platforms generate volumes of heterogeneous data, capturing user behaviors, textual content, 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, including 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), mined analytical results, and rendered visual artifacts, through coordinated agent flows. Guided by an insight-oriented taxonomy connecting insight types with suitable mining methods and visualization strategies, SIA adopts a stage-synchronized strategy that proceeds through goal decomposition, query, mining, visualization, and reporting stages. At each stage, it collects prior information to jointly plan and execute agent actions, while the coordinator maintains cross-stage action dependencies and assembles and distributes data to agents. Through quantitative evaluation and case studies supported by an interactive interface, we show that SIA can discover diverse and meaningful insights from social media with opportunities for subsequent reliability assessment.

For Practitioners

social scientists, data scientists.