Valuation of Social Media Influencers' Tokens with AI

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

🚀 Features

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

📊 Example Workflow

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.

⚙️ Installation

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.

Minisoco/CLOV

Valuation of Social Media Influencers' Tokens with AI

🚀 Features

📊 Example Workflow

⚙️ Installation