Trust & Reviews

Learn how TRAST's trust and review system works, combining community feedback, expert validation, and AI analysis to build reliable reputation scores.

Overview

The platform's trust and review system provides tools for evaluating blockchain projects through a combination of technical analysis and user feedback. This document explains how the system works, from basic trust scores to detailed project reviews.

Trust score system

Score calculation

The trust score represents a project's overall reliability and quality. The system calculates scores using multiple factors:

Technical analysis examines the project's smart contracts, security measures, and implementation quality. This includes automated code review, vulnerability scanning, and architecture assessment. The technical score considers factors like code quality, security practices, and implementation standards.

User reviews contribute significantly to the trust score. Each review is weighted based on the reviewer's verification status and expertise level. Verified users' reviews carry full weight, while anonymous reviews have 30% impact. Users with proven expertise can have up to 200% impact, based on their track record of accurate assessments.

Market behavior analysis tracks transaction patterns, liquidity levels, and trading activity. The system monitors smart contract interactions, wallet movements, and trading patterns to identify potential risks or unusual behavior. This data helps detect issues like market manipulation or suspicious activities early.

Historical data provides context for current scores. The system considers how long the project has been active, its past behavior patterns, and any previous issues or concerns. This historical perspective helps prevent manipulation through sudden review campaigns.

Score display

Trust scores range from 0 to 100% and are displayed using an intuitive visual system:

Excellent trust (95-100%): five green squares (🟩🟩🟩🟩🟩) Indicates exceptional trustworthiness with strong technical implementation, positive user reviews, and stable market behavior.

Very high trust (90-94%): four green, one yellow (🟩🟩🟩🟩🟨) Shows very high trust with minor considerations or areas for improvement.

High trust (85-89%): four green, one red (🟩🟩🟩🟩🟥) Represents strong trust with specific identified concerns that users should review.

Good trust (80-84%): three green, two yellow (🟩🟩🟩🟨🟨) Indicates good overall trust with some caution areas requiring attention.

Lower scores use various combinations of green, yellow, and red squares to communicate trust levels clearly. This visual system helps users quickly understand a project's status while providing detailed information through the specific color combinations.

Review system

Review process

Users can submit reviews through a straightforward process that maintains quality while encouraging participation. When submitting a review, users provide:

Rating selection: users choose between positive (100%), neutral (50%), or negative (0%) ratings based on their assessment. This simple scale helps maintain clear and decisive feedback.

Optional comments: users can provide detailed explanations for their ratings, including specific observations about the project's strengths or concerns. These comments help other users understand the reasoning behind ratings.

Evidence support: users can include links to relevant information, transaction data, or other evidence supporting their assessment. This feature helps maintain review quality and credibility.

Review impact

The system weights reviews based on several factors to ensure fair and accurate project assessment:

Verification status: verified users' reviews carry full impact weight, while anonymous reviews have 30% impact. This difference helps maintain quality while still allowing privacy-conscious users to participate.

Expertise level: users can earn increased impact (up to 200%) through consistently accurate reviews and positive community recognition. This rewards quality contributions and helps identify reliable reviewers.

Time factors: recent reviews typically carry more weight than older ones, ensuring scores reflect current project status. However, the system maintains some weight for older reviews to preserve historical context.

Consensus alignment: reviews that align with community consensus May receive slightly higher weight, while significant outliers face additional scrutiny to prevent manipulation.

Quality control

The system includes several measures to maintain review quality and prevent manipulation:

Duplicate prevention: users cannot submit multiple reviews for the same project. They can update their existing review if their assessment changes.

Pattern detection: the system monitors for unusual review patterns that might indicate manipulation attempts. This includes sudden bursts of similar reviews or coordinated rating campaigns.

Content monitoring: review comments undergo automated analysis to detect inappropriate content, spam, or malicious links. This helps maintain a professional and helpful review environment.

Security measures

Review protection

The system includes multiple layers of protection against manipulation:

Rate limiting: users face limits on how quickly they can submit reviews to prevent spam and automated submissions. These limits adjust based on user verification status and trust level.

Impact Control: The system carefully controls how much individual reviews can affect overall scores. This prevents sudden score changes from manipulation attempts while still allowing scores to reflect genuine changes in project quality.

Pattern recognition: advanced algorithms detect unusual review patterns that might indicate coordinated manipulation attempts. This helps maintain score accuracy and system integrity.

Data security

All review data receives comprehensive protection:

Encryption: all sensitive data undergoes encryption during transmission and storage. This includes user information, review content, and system metadata.

Access control: the permission system ensures users can only access appropriate data and features. This helps protect user privacy and system integrity.

Audit trails: the system maintains detailed logs of all review activities, helping track any unusual behavior or potential security issues.

Future development

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This document describes the current trust and review system. Features and capabilities May be updated based on user feedback and technological developments.