FairShare

FairShare: Transparent, quality-driven rewards for creators, with secure points, real-time insights, and fair earnings

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Inspiration

Creators often face opaque reward systems where engagement doesn’t always translate to fair earnings. I wanted to build a platform that brings transparency, fairness, and security, empowering creators while ensuring platform integrity. My girlfriend also had a goal to do live streaming and be more active on social media but she was unsure about the rewards and how the system works due to lack of transparency online

What it does

FairShare provides:

  1. Detailed earnings estimates based on views, likes, shares, and points.
  2. Quality-driven scoring factoring Engagement, Consistency, Growth, and Content Quality.
  3. Secure points transfers from viewers to creators with live updates and visible history.
  4. Dynamic risk management with real-time AML checks and fraud detection.
  5. Comprehensive dashboards tracking rewards, content quality, compliance, performance, and system health.
  6. Real-Time Monitoring & System Health: Monitor platform stability, fund safety, and transaction performance with a comprehensive health score and detailed breakdown of success rate, risk management, performance, and fund flow.

How I built it

  • Languages & Frameworks: Python 3.9+, Streamlit
  • Libraries: Pandas, NumPy, Datetime, ZoneInfo
  • Assets: TikTok creator mock data, transaction datasets, custom UI icons

Technical Features:

  1. Real-time monitoring of fund flow and system health
  2. Dynamic AML thresholds and automated fraud flagging
  3. AI-powered content quality metrics

Development Tools: VS Code, Git, local Streamlit deployment

Challenges we ran into

  1. Balancing fairness, performance, and usability
  2. Presenting multi-metric dashboards clearly
  3. Implementing dynamic risk management without slowing down transactions
  4. Simulating realistic datasets for engagement, points flow, and risk scenarios

Accomplishments that we're proud of

  1. Probably my first and top 1, this is my first hackathon experience and learning how this whole thing works is wonders and it's the fastest way to push my learning curve as i just got in touch with programming

  2. Fully transparent earnings calculation with detailed quality scoring

  3. Real-time points and fraud monitoring

  4. Comprehensive, user-friendly dashboard for creators and admins

  5. Successfully integrated security, fairness, and usability in one platform

What we learned

  1. Transparent, data-driven reward systems build trust with creators 2.Combining data science and UX design is key for engagement
  2. Real-time monitoring requires balancing accuracy, speed, and scalability

What's next for FairShare

  1. Integrate video content analysis and audience retention metrics
  2. Add personalized recommendations to help creators improve engagement
  3. Expand to more platforms and live data streams for real-world deployment
  4. Being able to incorporate TikTok API on the viewers and creators database

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