Incentives

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 $𝑅_p$ is calculated as:

$$R_p = \frac{B_p \times (E_p + S_r)}{T_p}$$

Where:

• $R_p$: Reward for the publisher.

• $𝐵_p$: Base reward for publishing an article.

• $𝐸_p$: Engagement score (likes, comments, shares).

• $𝑆_𝑟$: Score from reviewers.

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

Reviewers' Rewards

The reward for the reviewer $𝑅_𝑟$ is calculated as:

$$R_r = \frac{B_r \times Q_r \times V_r}{T_r}$$

Where:

• $𝑅_𝑟​$: Reward for the reviewer.

• $𝐵_𝑟$: Base reward for reviewing an article.

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

• $𝑉_𝑟$: Volume of reviews (number of reviews submitted).

• $𝑇_𝑟$: Total number of reviews in a given period.

Readers' Rewards

The reward for the reader $𝑅_𝑒$ is calculated as:

$$R_e = \frac{B_e \times (L_e + C_e + S_e)}{T_e}$$

Where:

• $𝑅𝑒$: Reward for the reader.

• $𝐵_𝑒$: Base reward for engaging with content.

• $𝐿_𝑒$: Number of likes given.

• $𝐶_𝑒$: Number of comments made.

• $𝑆_𝑒$: Number of shares.

• $𝑇_𝑒$: Total engagement actions in a given period.

Example Scenarios

Publisher Reward Calculation

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

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

• Reviewer Score ($𝑆_𝑟$): 80.

• Total Articles ($𝑇_p$): 50.

The reward for the publisher is:

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

Reviewer Reward Calculation

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

• Quality Score ($𝑄_𝑟$): 90.

• Volume of Reviews ($𝑉_𝑟$): 10.

• Total Reviews ($𝑇_𝑟$): 100.

The reward for the reviewer is:

$$R_r = \frac{50 \times 90 \times 10}{100} = 450$$

Reader Reward Calculation

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

• Likes ($𝐿_𝑒$): 30.

• Comments ($𝐶_𝑒$): 20.

• Shares ($𝑆_𝑒$): 10.

• Total Engagements ($𝑇_𝑒$): 200.

The reward for the reader is:

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

Conclusion

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.