PUBLISH 2.0 motivates publishers, reviewers, and readers to actively participate in PUBLISH ecosystem. This mechanism involves earning and distributing rewards through tokens, which are regulated by formulas to ensure fairness and sustainability.

Incentive Mechanism Components

  1. Publishers:

    • Reward: Publishers receive $NEWS tokens for publishing high-quality content.

    • Evaluation: Quality is assessed based on peer reviews and reader engagement (likes, shares, comments).

  2. Reviewers:

    • Reward: Reviewers earn $NEWS tokens for providing thorough and constructive reviews.

    • Evaluation: Reviews are scored based on their helpfulness and accuracy, determined by both publishers and readers.

  3. Readers:

    • Reward: Readers earn $NEWS tokens for engaging with content (reading, liking, commenting).

    • Evaluation: Engagement is quantified by the frequency and quality of interactions.

Formula for Reward Distribution

Publishers' Rewards

The reward for the publisher 𝑅p𝑅_p is calculated as:

Rp=Bp×(Ep+Sr)TpR_p = \frac{B_p \times (E_p + S_r)}{T_p}


  • RpR_p: Reward for the publisher.

  • 𝐵p𝐵_p: Base reward for publishing an article.

  • 𝐸p𝐸_p: Engagement score (likes, comments, shares).

  • 𝑆𝑟𝑆_𝑟: Score from reviewers.

  • 𝑇p𝑇_p: Total number of articles published in a given period.

Reviewers' Rewards

The reward for the reviewer 𝑅𝑟𝑅_𝑟 is calculated as:

Rr=Br×Qr×VrTrR_r = \frac{B_r \times Q_r \times V_r}{T_r}


  • 𝑅𝑟𝑅_𝑟​: Reward for the reviewer.

  • 𝐵𝑟𝐵_𝑟: Base reward for reviewing an article.

  • 𝑄𝑟𝑄_𝑟: Quality score of the review (assessed by authors and readers).

  • 𝑉𝑟𝑉_𝑟: Volume of reviews (number of reviews submitted).

  • 𝑇𝑟𝑇_𝑟: Total number of reviews in a given period.

Readers' Rewards

The reward for the reader 𝑅𝑒𝑅_𝑒 is calculated as:

Re=Be×(Le+Ce+Se)TeR_e = \frac{B_e \times (L_e + C_e + S_e)}{T_e}


  • 𝑅𝑒𝑅𝑒: Reward for the reader.

  • 𝐵𝑒𝐵_𝑒: Base reward for engaging with content.

  • 𝐿𝑒𝐿_𝑒: Number of likes given.

  • 𝐶𝑒𝐶_𝑒: Number of comments made.

  • 𝑆𝑒𝑆_𝑒: Number of shares.

  • 𝑇𝑒𝑇_𝑒: Total engagement actions in a given period.

Example Scenarios

Publisher Reward Calculation

  • Base Reward (𝐵p𝐵_p): 100 $NEWS tokens.

  • Engagement Score (𝐸p𝐸_p): 150 (sum of likes, shares, comments).

  • Reviewer Score (𝑆𝑟𝑆_𝑟): 80.

  • Total Articles (𝑇p𝑇_p): 50.

The reward for the publisher is:

Ra=100×(150+80)50=100×23050=460R_a = \frac{100 \times (150 + 80)}{50} = \frac{100 \times 230}{50}= 460

Reviewer Reward Calculation

  • Base Reward (𝐵𝑟𝐵_𝑟): 50 $NEWS tokens.

  • Quality Score (𝑄𝑟𝑄_𝑟): 90.

  • Volume of Reviews (𝑉𝑟𝑉_𝑟): 10.

  • Total Reviews (𝑇𝑟𝑇_𝑟): 100.

The reward for the reviewer is:

Rr=50×90×10100=450R_r = \frac{50 \times 90 \times 10}{100} = 450

Reader Reward Calculation

  • Base Reward (𝐵𝑒𝐵_𝑒​): 30 $NEWS tokens.

  • Likes (𝐿𝑒𝐿_𝑒): 30.

  • Comments (𝐶𝑒𝐶_𝑒): 20.

  • Shares (𝑆𝑒𝑆_𝑒): 10.

  • Total Engagements (𝑇𝑒𝑇_𝑒): 200.

The reward for the reader is:

Re=30×(30+20+10)200=30×60200=9.0R_e = \frac{30 \times (30 + 20 + 10)}{200} = \frac{30 \times 60}{200} = 9.0


This incentive mechanism for PUBLISH 2.0, aims to create a fair and motivating environment for all participants. The formulas ensure that rewards are distributed based on meaningful contributions, encouraging continuous engagement and high-quality content production. By implementing this model, PUBLISH 2.0 can achieve a dynamic and sustainable ecosystem.

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