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.

Step 2 - Normalizing Engagement Scores

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.

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.