Vericast

VeriCast is a reputation system built for the Farcaster platform, designed to track, analyze, and reward user engagement and authenticity. FRE uses the several data and analytics provider like Open Rank APIs by karma labs , Neynar and Airstack to gather data and rank users accordingly , making the Social media more fun , engaging and trustworthy for everyone in the Farcaster ecosystem.

Presentation Link

https://www.canva.com/design/DAGAaEMbAoU/gBbXimPUuScoQ1Nxxzr9AQ/edit?utm_content=DAGAaEMbAoU&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton

Problem

Social media has multiple problems like Fake accounts, spam / bots , and lack of transparency which diminshes user experience. FRE addresses these issues by introducing a transparent and verifiable reputation system.

1. Reputation Tracking: FRE tracks user activities such as posting frequency, casts created & engagement levels to generate a reputation score for each user.
2. Analytics and Rewards: Users earn reputation points based on their contributions and interactions. Streaks, consistent engagement, and high-quality content lead to higher scores and potential rewards within the platform.

We used different APIs and services to fetch user data and calculate reputation scores. For gathering the user data , we identified multiple resources , which offers the user’s global and personal ranking , combining that with their casts and other engagement metrics to create the final score and ranking

OpenRank API by Karma3Labs:

OpenRank APIs were used to fetch the users global ranking for engagements and followings , to help calculate their score. This constitutes for the User Network points and the engagement points

Neynar:

For fetching user's casts , and reactions , which helps calculates the user's engagement with other posts in the community and also their Casting Frequency Score alongside.

Airstack:

Airstack's Onchain graph was also used to calculate the users onchain activity score and the we fetched the user's farcaster data like their profile Creation date to calculate the Age and longevity of the profile.

Frames.js

We also used Frames.js to generate all the farcaster frames related to this project. The reputation score frame is dynamically generated and it shows the users calculated reputation score based on different data-points fetched from above mentioned APIs

Here's the schema data-points using which we calculate the reputation score of a Farcaster user.

1. Post Frequency (150 points)
2. Activity - Engagement with Others' Posts (100 points)
3. Engagement on User's Posts (200 points)
4. Network Size (100 points)
5. Longevity (100 points)
6. Frame/On-Chain Interaction (200 points)
7. Post Quality (50 points)
8. Quests (100 points)

• Our main frame from can fetch rankings of a particular Farcaster handle.
• For the first time , we generate the score and rankings by fetching the data for the user and then storing the points & rank directly in the MRU.
• Events will be logged on the MRU to signify what actions awarded points to the user. Our Reputation algorithm currently runs in a seperate environement , to ensure no tampering. We want to run this inside Micro Rollup which would make it trustless and Verifiable at the same time.

Later on we plan to add quest frames, user can earn more points by like completing streaks and participating in quests, the action will be perform in the rollup.