Kartal

Project Objective:

Tool that predicts the viral potential of topics based on real-time data. It analyzes social media mentions, engagement growth rates, and sentiment to generate a Viral Score. The platform helps users identify high-potential topics quickly, enabling data-driven content decisions.

Key Features:

1. Topic Analysis:

   • Users input a topic or choose trending suggestions from platforms like Twitter, Reddit, and Google Trends.

2. Viral Score Prediction:

   • Predicts a Viral Score based on mentions, sentiment, and engagement growth.

3. Trend Visualization:

   • Interactive graphs show engagement trends, sentiment breakdown, and platform-specific insights.

4. Real-Time Insights:

   • Provides live updates on trending topics and their growth trajectory.

Design Process:

1. Problem Definition:

   • Identify inefficiencies in tracking viral topics manually.

   • Define user needs: simplicity, speed, and actionable insights.

2. Data Pipeline:

   • Integrate APIs (e.g., Twitter, Reddit) for real-time data collection.

   • Use machine learning to analyze features like mentions, sentiment, and growth rate.

3. Model Development:

   • Train a regression ML model using historical data to predict Viral Scores.

4. User Interface:

   • Design an intuitive UI with:

     • Input fields for topic suggestions.

     • Dashboard showing Viral Scores, trends, and insights.

   • Use a clean turquoise, silver, and white color palette for a professional feel.

5. Testing & Feedback:

   • Validate predictions with real-world data and refine based on user feedback.

Kartal provides an efficient, user-friendly platform to forecast topic virality, empowering users to act on emerging trends with confidence.