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Polarization dynamics analysis seeks to understand how opposing stances on specific issues and the hostility between them evolve over time. These evolutions become particularly complex in today's multi-platform social media environment, where shifting topics, platform-specific polarization patterns, and cross-platform influence intertwine. Existing studies typically focus on single-platform scenarios or merely rely on macro statistics, oversimplifying fine-grained evolution patterns. To bridge the gap, through expert collaboration and a literature review, we characterize polarization dynamics at a detailed level and systematically summarize underlying factors that can inform hypotheses about possible mechanisms. To effectively visualize these detail-level dynamics, we propose a novel weather-inspired visual metaphor, in which temperature (encoded by colors) represents stances, air masses depict polarization dynamics, and fronts illustrate interaction patterns between dynamics. The metaphor is implemented by our polarization visualization algorithm, which places multi-platform polarization dynamics with a hierarchical force-directed layout and visualizes their intensity and interaction patterns with a stance-based scalar field. We further present PolarWeather, a visual analytics system that enables experts to explore multi-platform polarization dynamics and conduct factor-informed hypothesis generation. We validate the effectiveness and usability of PolarWeather through real-world case studies, expert interviews, and a user study.

The emergence of social finance (SocialFi) transforms online communities into complex socio-economic systems. Within these spaces, collective decisions shape a “digital commons” characterized by social capital (e.g., community trust) and financial health (e.g., market liquidity). Governing such hybrid ecosystems is challenging because real-world interventions are costly and irreversible. While counterfactual simulation is essential for exploring alternative governance strategies, existing approaches fail to capture the non-linear interplay between governance rules, individual behaviors, and emergent economic outcomes. To systematically unpack this complexity, we operationalize the Institutional Analysis and Development (IAD) framework as our theoretical foundation, synthesizing prior literature with insights from formative expert interviews. Built on this framework, we present SocialFiVis, an IAD-embedded visual analytics sandbox. It introduces a robust model to quantify the dual-track digital commons, coupled with a two-phase simulation engine. This engine combines LLM-derived personas with a mechanism-guided Perception–Reasoning–Action (PRA) runtime to simulate heterogeneous, context-aware agents empirically grounded in the retained messaging cohort. A hierarchical multi-view interface with interpretable reasoning pathways enables community operators to explore counterfactual policies and trace system-level outcomes back to individual behavioral rationales. We evaluate SocialFiVis through two case studies, a user study, and follow-up interviews. Results demonstrate that SocialFiVis supports fine-grained behavioral attribution and helps explain emergent phenomena such as the structural decoupling of social capital and the resilience of messaging members under localized governance shocks.

Mental health is an urgent societal issue, and social scientists are increasingly turning to online mental health communities (OMHCs) to analyze user behavior data for early intervention. However, existing sequence mining techniques fall short of the urgent need to explore the behavior progression of different groups (e.g., recovery or deterioration groups) and track the potential long-term impact of behaviors on mental health status. To address this issue, we introduce EMINDS, a visual analytics system built on a novel automatic mining pipeline that extracts distinct behavior stages and assesses the potential impact of frequent stage patterns on mental health status over time. The system includes a set of interactive visualizations that summarize the meaning of each behavior stage and the evolution of different stage patterns. We feature a pattern-centric Sankey to reveal contextual information about the impact of stage patterns on mental health, helping experts understand the specific changes in sequences before and after a stage pattern. We evaluated the effectiveness and usability of EMINDS through two case studies and expert interviews, which examined the potential stage patterns impacting long-term mental health by analyzing user behaviors on Reddit.

Social media has become a central platform for public discourse, yet understanding the causal mechanisms underlying opinion dynamics remains challenging due to semantic noise, causal hallucinations, and the lack of theoretical grounding in existing computational approaches. We present SOCAS, a visual analytics framework that bridges unstructured social media discourse and interpretable causal relationship extraction(CRE). Grounded in Causal Funnel Theory, SOCAS introduces a hierarchical transition model that organizes influencing factors from distal exogenous conditions to proximal political actions. To support scalable analysis, we develop a zero-shot CRE pipeline leveraging Large Language Models (LLMs), enabling domain-agnostic extraction of event units and causal relationships, with confidence scores and temporal post-filtering to mitigate hallucinations. To enhance interpretability and user engagement, SOCAS incorporates an interactive structural sensitivity analysis module that supports structural perturbation reasoning and dynamic exploration of causal structures, complemented by a natural language interface(NLI) for conversational refinement. In addition, we design specialized analytical modules to trace complex behavioral mechanisms, such as communication and political translation.

Social media analysis is widely used by domain experts to understand public stances on complex issues. However, analyses that focus on isolated targets or single-perspective groupings can lead to biased interpretations and misleading attributions. We present SMG-Vis, an interactive visual analytics system that supports expert judgment in multi-target social media analysis. SMG-Vis represents each user as a stance signature over multiple targets and constructs a multi-target stance space in which groups are defined by identical stance signatures. This representation enables analysts to explore structured group relationships formed by combinations of stances across targets. SMG-Vis supports iterative group exploration through coordinated views of mentioned targets, keywords, and supporting posts. To assist reasoning, the system provides computationally assisted cues that expose alternative explanations and enable comparison of competing hypotheses across groups. By juxtaposing focal groups with related or contrasting groups, SMG-Vis helps analysts assess the robustness of their interpretations and mitigate misinterpretations caused by limited analytical perspectives. We demonstrate the utility of SMG-Vis through real-world case studies on social media datasets covering major public issues, showing how it supports nuanced group understanding and more reliable analytical judgments.

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.

Science museums are a rich environment for visualization research where researchers have designed new visualizations, interaction modalities, and educational approaches. But they are also socio-technical places where power, context, and organizational structures influence how science communication is produced and perceived. In this paper, we draw on our collaboration with a local science museum, where we worked on creating a new interactive digital exhibit. Through this process, we encountered a socio-technical phenomenon: these exhibits rarely acknowledge the work or people behind them. We explored the reasons for this invisibility through an interview study with science museum staff and leadership. Our results reveal crediting of interactive exhibits to be a complex, situated challenge. This challenge, however, offers visualization research more than a practical problem to solve — it is an opportunity to reframe credit as a socio-technical design space, one where visualization can make visible not only the outcomes of science, but the processes and people that produce it. A free copy of this paper and all supplemental materials are available at https://osf.io/9vyu3