ReVibe

Inspiration

We saw how TikTok’s Creator Fund often rewards raw views and watch time, but overlooks the true value creators bring to their communities. Many niche creators producing educational, helpful, or socially impactful content feel undercompensated compared to viral dance or meme accounts. This imbalance discourages diverse, meaningful content creation and erodes trust in the platform. We wanted to design a transparent system that values not just popularity, but quality and positive impact.

What it does

ReVibe introduces a daily Quality Index (Q) that scores every TikTok video based on three factors: engagement, audience sentiment, and societal impact. Videos are automatically clustered into niche categories using machine learning, so creators compete fairly within their content type. The daily creator fund is then split across categories based on how much each community improves in quality compared to yesterday. Within each category, only videos that show above-average improvement earn payouts, ensuring that rewards go to creators who are consistently elevating their work.

How we built it

We built ReVibe with a Python backend for data processing and machine learning, supported by GitHub for collaboration and version control. For the creator dashboard UI, we used Lynx, TikTok’s cross-platform framework, to design a clean, interactive interface that could be directly embedded into the TikTok app. Our sentiment analysis leveraged a fine-tuned RoBERTa transformer on TikTok comments, while societal value was assessed using zero-shot classification models (e.g. BART MNLI) combined with keyword heuristics. To categorize creators fairly, we applied SentenceTransformer embeddings and k-means clustering via scikit-learn.

Challenges we ran into

A major hurdle was tackling bias against niche creators: without careful clustering, educational or specialized content would always be overshadowed by viral trends. We also had to design the Quality Index formula so no single metric (like views or sentiment spam) could dominate results. Preventing gaming behavior was tricky—creators might try inflating views or spamming positive comments, so we added safeguards like bounded Z-scores, multiplier caps, and anomaly detection. Another challenge was the cold-start problem for new creators, which we addressed by setting fair baselines. Finally, balancing technical complexity with hackathon time limits required tough prioritization.

Accomplishments that we're proud of

We designed a transparent payout formula that creators can actually understand and track. We successfully built a prototype Quality Index that blends quantitative and qualitative factors into a fairer metric. Our clustering approach ensures niche creators aren’t competing directly with mainstream categories, leveling the playing field. We demonstrated how AI sentiment and societal value analysis can align platform incentives with community well-being. Most importantly, we created a working prototype that reimagines value distribution on TikTok in a way that benefits both creators and the platform.

What we learned

We learned how to integrate multiple AI/ML techniques—from embeddings and clustering to transformer-based sentiment and zero-shot classification. We discovered the importance of balancing quantitative and qualitative metrics, since focusing only on one leads to unintended incentives. We also deepened our understanding of fairness in algorithmic systems, especially when dealing with diverse creator communities. The time pressure pushed us to be resourceful and focus on core functionality rather than perfection. Most of all, we realized that transparency and trust are just as critical as technical performance when designing for creators.

What's next for ReVibe

We plan to refine the Quality Index with more robust signals, including video transcripts, engagement longevity, and advanced toxicity detection. We aim to integrate ReVibe into TikTok’s ecosystem through Lynx, enabling seamless adoption within the creator dashboard. We also want to run large-scale simulations with real TikTok datasets to validate fairness and stability under growth. Long-term, we envision expanding ReVibe beyond TikTok, making it a general framework for ethical, merit-based content monetization across social platforms. Ultimately, our goal is to set a new standard for rewarding creators based on impact and improvement, not just clicks.