### Step 1 - Scraping Data
- Utilizes the CivitAI API with cursor-based pagination to fetch image data over a two-year period.
- Saves progress in `cursors.txt` to resume scraping from the last retrieved point, avoiding redundant requests.
- Stores data in timestamped directories, organizing results into manageable batches of 50,000 images per session.
- Handles API constraints efficiently, with planned improvements for retrying failed requests.
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# Step 2  - Normalizing Engagement Scores
### Penalty (Time Penalty)
- **Purpose**: To reduce the influence of older posts by applying a decay based on how long the content has been on the platform.
- **Formula**: \( \text{timePenalty} = \frac{1}{1 + \log(\text{daysOnPlatform} + \text{offset})} \)
  - **Logarithmic Decay**: Older posts (higher `daysOnPlatform`) have smaller penalty values.
  - **Offset**: Ensures stability and avoids division by zero or undefined log values.
- **Effect**: 
  - Recent posts get higher scores.
  - Older posts are de-emphasized in engagement calculation.

### Normalizing (Reactions Normalization)
- **Purpose**: To scale raw social reaction values into a standardized range (0–1), enabling comparisons.
- **Method**: Min-Max Scaling
  - Formula: \( \text{normalizedReactions} = \frac{\text{value} - \text{min}}{\text{max} - \text{min}} \)
  - Adjusts all reaction values relative to the minimum and maximum in the dataset.
- **Effect**:
  - Converts raw reaction values into a uniform scale.
  - Ensures different ranges of reactions are treated fairly in further calculations.