Visual Analytics for Assessing Cryptocurrency Trade Manipulation Risk
Summary
ManiScope is a visual analytics system designed to help users assess trade-based manipulation risks in cryptocurrency markets, including wash trading. Its coordinated views bring together token ownership distribution, relationships among holders, individual holder behavior, price movements, and suspicious trading patterns. The goal is to help analysts connect evidence that may be difficult to assess through isolated rules or manual inspection.
The system also uses a human and LLM collaborative approach: the LLM infers analytical intent and developing hypotheses from user interactions, then surfaces relevant visual, statistical, and synthesized evidence for evaluation. The document reports two case studies and a user study involving 12 experienced cryptocurrency practitioners. These evaluations suggest the system can support risk assessment and reduce effort spent gathering evidence. The available description does not specify detection accuracy, false-positive rates, or performance across broader user groups, so it does not establish that the system can reliably identify manipulation on its own.
Key ideas
- Assessing manipulation risk can require connecting holder structure, behavior, price dynamics, and trading patterns.
- Coordinated visual views help analysts examine these evidence types together.
- An LLM can infer user hypotheses from interaction context and surface relevant evidence.
- Evaluation includes two case studies and a practitioner user study, but the description gives no accuracy metrics.
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Full text
# ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk # ManiScope: LLM-Assisted Visual Analytics of Cryptocurrency Manipulation Risk Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relationships, suspicious behaviors, and market dynamics for risk assessment. To address these limitations, we propose ManiScope, an LLM-assisted visual analytics system for analyzing trade-based manipulation risks in cryptocurrency markets. ManiScope provides coordinated views of token distributions, holder relationships, detailed holder behaviors, price dynamics, and suspicious trading patterns. To further enhance user analysis, ManiScope introduces a human-LLM collaborative visual analytics framework. Rather than acting as a basic reactive LLM assistant, the framework positions the LLM as a co-analyst that infers users' analytical intent and emerging hypotheses from interaction context and surfaces relevant visual, statistical, and synthesized evidence for hypothesis evaluation. This design reduces repetitive inspection and strengthens evidence-based reasoning. We evaluate ManiScope through two case studies and a user study with 12 experienced cryptocurrency practitioners. The results suggest that ManiScope supports effective risk assessment of manipulation, reduces manual effort in evidence-seeking, and organizes findings around user hypotheses.
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