rank Tag

Overview

The rank tag is used in Trusted Assertion events to convey computed ranking scores for various subjects. It provides a normalized way for service providers to express quality, popularity, or reputation metrics about users, events, addressable events, and external identifiers.

Specification

Property	Value
Tag Name	rank
Defined in	NIP-85: Trusted Assertions
Format	["rank", "<score>"]

Parameters

Position	Name	Description	Format	Required
0	Tag name	Always "rank"	string	Yes
1	Score	Ranking score normalized to 0-100 scale	string (integer)	Yes

Usage Context

The rank tag appears in the following event kinds:

• Kind 30382 (User Trusted Assertions): User reputation/quality ranking
• Kind 30383 (Event Trusted Assertions): Event popularity/quality ranking
• Kind 30384 (Addressable Event Trusted Assertions): Addressable content ranking
• Kind 30385 (External Identifier Trusted Assertions): External entity ranking

Format Details

Score Values

• Range: 0-100 (integer values)
• 0: Lowest possible ranking
• 100: Highest possible ranking
• Normalization: All service providers should normalize their scores to this 0-100 range for consistency

String Representation

• Scores are represented as string integers in the tag value
• Examples: "0", "50", "89", "100"

Client Behavior

Clients should:

1. Display Rankings:

   • Present ranking scores in user interfaces where appropriate
   • Consider visual representations (stars, bars, percentages)
   • Indicate the source service provider for transparency

2. Score Interpretation:

   • Treat scores as relative rankings within each service provider's algorithm
   • Handle different ranking methodologies from different providers
   • Allow users to compare rankings from multiple providers

3. Validation:

   • Verify scores are within the 0-100 range
   • Handle invalid or out-of-range values gracefully
   • Validate that scores are numeric strings

Service Provider Behavior

Service providers should:

1. Score Generation:

   • Normalize all ranking calculations to the 0-100 scale
   • Ensure consistent scoring methodology across their algorithm
   • Update rankings only when actual changes occur

2. Algorithm Transparency:

   • Document their ranking methodology in kind 0 metadata events
   • Use separate service keys for different ranking algorithms
   • Provide clear explanations of what their ranking represents

Use Cases

User Rankings:

• Web of Trust reputation scores
• Influence or authority metrics
• Community standing indicators
• Spam/quality detection scores

Event Rankings:

• Content quality assessments
• Engagement-based popularity scores
• Relevance or importance rankings
• Virality or trending indicators

Content Rankings:

• Article or long-form content quality
• Repository or project popularity
• Resource value assessments
• Community curation scores

External Entity Rankings:

• Website trustworthiness scores
• Book or media ratings
• Location or business rankings
• Product or service quality scores

Examples

User Ranking

{
  "kind": 30382,
  "tags": [
    ["d", "e88a691e98d9987c964521dff60025f60700378a4879180dcbbb4a5027850411"],
    ["rank", "89"]
  ],
  "content": "",
  "sig": "..."
}

Event Ranking

{
  "kind": 30383,
  "tags": [
    ["d", "b3e392b11f5d4f28321cedd09303a748acfd0487aea5a7450b3481c60b6e4f87"],
    ["rank", "92"]
  ],
  "content": "",
  "sig": "..."
}

External Entity Ranking

{
  "kind": 30385,
  "tags": [
    ["d", "isbn:978-0-321-35668-3"],
    ["k", "book"],
    ["rank", "85"]
  ],
  "content": "",
  "sig": "..."
}

Related Tags

• Metric Tags: Used alongside rank in trusted assertions

  • followers, post_cnt, reply_cnt (user metrics)
  • comment_cnt, reaction_cnt, zap_cnt (engagement metrics)
  • zap_amount, zap_avg_amt_day_recd (economic metrics)

• Identifier Tags:

  • d tag (identifies the subject being ranked)
  • k tag (specifies external identifier type in kind 30385)

References

• NIP-85: Trusted Assertions

Notes

• The rank tag provides a standardized way to compare quality assessments across different service providers and algorithms.
• Service providers should document their ranking methodology to help users understand what the scores represent.
• Multiple service providers can rank the same subject, allowing users to compare different algorithmic approaches.
• Rankings are subjective and depend on the service provider's algorithm and data sources.
• Clients should clearly indicate which service provider generated each ranking score.