This project explores the valuation of tokens corresponding to influential individuals on social platforms. The platform allows users to input the identity (e.g., username or profile link) of a social media influencer. An AI-powered system then performs a comprehensive analysis and provides an estimated market capitalization for a hypothetical cryptocurrency tied to that influencer.
Our approach combines:
- Multi-model AI computations
- Data-driven analysis of engagement and pump/dump activities
- Simulation of tokenized valuation dynamics
- Input any social media influencer (Twitter, Instagram, TikTok, etc.)
- AI-powered sentiment, influence, and reach analysis
- Pump-activity and market manipulation detection
- Estimated cryptocurrency market cap valuation
- Extensible architecture for integrating more data sources
- User Input: Enter the influencer’s handle (e.g.,
@elonmusk). - AI Analysis:
- Retrieve metrics (followers, engagement rates, sentiment).
- Apply multi-model AI analysis (influence scoring + pump activity detection).
- Predict potential crypto token valuation.
- Output: Market cap estimation, confidence intervals, and visual analytics.
Clone this repository:
git clone https://github.com/yourusername/influencer-token-valuation.git
cd influencer-token-valuation
Install dependencies:
pip install -r requirements.txt
🧑💻 Usage
Command Line
python main.py --influencer "@elonmusk"
Sample Output
{
"influencer": "@elonmusk",
"influence_score": 97.5,
"predicted_market_cap": "12.5B USD",
"confidence_interval": "10.2B - 14.8B",
"pump_activity_risk": "High"
}
🧩 Code Examples
1. Basic Influencer Analysis
from valuation import InfluencerValuation
analyzer = InfluencerValuation()
result = analyzer.evaluate_influencer("@elonmusk")
print(result)
2. Multi-Model AI Integration
from models import SentimentModel, InfluenceModel, PumpActivityModel
def run_analysis(username):
sentiment = SentimentModel().analyze(username)
influence = InfluenceModel().score(username)
pump_risk = PumpActivityModel().detect(username)
market_cap = (influence * sentiment) / (1 + pump_risk)
return {
"sentiment": sentiment,
"influence": influence,
"pump_risk": pump_risk,
"predicted_market_cap": f"{market_cap:.2f}B USD"
}
print(run_analysis("@vitalikbuterin"))
3. API Example (Flask)
from flask import Flask, request, jsonify
from valuation import InfluencerValuation
app = Flask(__name__)
analyzer = InfluencerValuation()
@app.route("/evaluate", methods=["POST"])
def evaluate():
data = request.get_json()
username = data.get("influencer")
result = analyzer.evaluate_influencer(username)
return jsonify(result)
if __name__ == "__main__":
app.run(debug=True)
📐 Mathematical Formula
We approximate the valuation using a simplified formula:
PredictedMarketCap≈(InfluenceScore×SentimentScore)÷(1+PumpRiskFactor)
PredictedMarketCap≈(InfluenceScore×SentimentScore)÷(1+PumpRiskFactor)
Where:
Influence Score = Derived from followers, engagement, and reach.
Sentiment Score = Weighted average of positive/negative sentiment.
Pump Risk Factor = Likelihood of manipulative activity.
📈 Roadmap
Expand social media API coverage
Improve AI model ensemble strategies
Add visualization dashboards
Deploy as a hosted web app
🤝 Contributing
Contributions are welcome! Please submit a pull request or open an issue to discuss ideas.