# From memes to liquidity traps, OFUYC MetaRadar maps scams through data-driven “scam profiling”

In traditional finance, investment decisions typically rely on professional analysts, audit reports, and fund rating systems. By contrast, the Web3 world resembles a trustless yet highly emotional market: investment behavior often occurs before cognition is complete and before risks become clear. User decisions are driven by meme propagation, KOL promotions, herd behavior, and emotional resonance.
After long-term tracking of scamming behavior, the OFUYC digital asset exchange platform has observed that most victims are not lacking information, but rather a structured cognitive alert mechanism. This insight led to the creation of “MetaRadar”—a multidimensional anti-scam alert system that integrates risk indicators, linguistic sentiment, technical structures, and historical patterns. Its goal is to reorganize fragmented and latent risk signals into an intuitive, decision-ready, visual map.


## Five modules, one integrated system: From structural loopholes to psychological manipulation

At the core of MetaRadar is the integration of five risk dimensions, visually synthesized into a “project profile” to provide users with a real-time “risk heat signal” when evaluating a project:
**Risk structure identification**: Detects on-chain structural anomalies such as “nested yield schemes”, “high-slippage liquidity”, and “over-leveraged staking”. Triggers “exit risk warnings”.
**Sentiment language monitoring**: Uses NLP models to analyze manipulative language patterns in project messaging—such as excessive FOMO cues, fear-based mobilization, or moralizing narratives—and quantifies a “manipulation language index”.
**KOL propagation tracing**: Evaluates whether project endorsements are concentrated in a few accounts, spread virally, or mimic “copy-trade” replication behavior. Triggers “hype risk” alerts.
**Technical transparency scoring**: Quantifies metrics such as number of audits, contract readability, and customization of parameters, creating a “black-box assessment” score.
**Historical similarity modeling**: Compares current projects to known past scams using vector analysis, generating a “similarity radar chart” to indicate when certain structures have repeatedly appeared in scamming schemes.
These modules are not isolated; they form a dynamic risk map. Users can view the five-dimensional project scoring and evolving risk trends in real time within the OFUYC platform interface.

## User experience flow: Anti-scam can be “seamless launch”

We understand that if an anti-scam system disrupts user experience or causes excessive friction, it is likely to be ignored. Therefore, MetaRadar is designed around a “subtle prompt + smart intervention” mechanism, supporting the following integration scenarios:
**Before wallet interaction**: When a user attempts to connect to a DApp or transfer assets on the OFUYC platform, the plugin automatically analyzes the contract of the counterpart and displays a “current risk heatmap”. Users may choose to “verify further” or “dismiss the risk”.
**Browser plugin notification**: When users browse the website or community of a project, a small red dot appears at the page edge. Clicking it reveals the five-dimensional risk scoring of a project.
**Before clicking community links**: When users receive project recommendations via Telegram, X (formerly Twitter), or Discord, MetaRadar can intercept external links and display “historical similarity score + language spike analysis” of the project.
Furthermore, the MetaRadar API will be opened to external platforms, laying the foundation for **a shared, cross-industry anti-scam infrastructure.**

## From warnings to consensus: Building a Web3-wide anti-scam ecosystem

MetaRadar is not merely an internal user protection tool within OFUYC; it is positioned as a “risk governance consensus layer” for the entire Web3 ecosystem:
We will gradually release open-access versions for DAOs, wallets, and data platforms to embed into their user interfaces, establishing an “on-chain project trust standard”.
Developers can set customized risk thresholds via API integration, such as flagging projects with high manipulation indices in red or blocking interaction with black-box contracts.
In the education module, we will use MetaRadar data to develop visual anti-scam learning courses that help users understand the logic behind the five-dimensional map.
Community members can contribute scoring feedback via the “project warning correction” mechanism, continuously improving the precision of the model and truly enabling co-constructed anti-scam consensus.
OFUYC believes that the end of scam will not be achieved through censorship, but through transparent cognitive structures. The goal of MetaRadar is to add an extra layer of “self-protection” with every user click.