Weighted Point Calculation

To ensure fairness, accuracy, and prevent abuse, liq.life employs a weighted point system and advanced algorithms to calculate influencer scores. Here’s an overview of how the weighting system functions, including a simplified code snippet:

Weighted Engagement System

Each engagement metric on X (Views, Likes, Reposts, Replies) has a distinct weight. These weights ensure posts with higher quality interactions earn greater rewards.

Engagement Weights Recap:

• Views: 1.0

• Likes: 3.0

• Reposts: 5.0

• Replies: 2.0

Simplified Code Example (Python):

Here's a basic implementation example to clarify the calculation:

# Example engagement data from user's post
engagement_metrics = {
    'views': 1000,
    'likes': 100,
    'reposts': 20,
    'replies': 50
}

# Assigned engagement weights
weights = {
    'views': 1.0,
    'likes': 3.0,
    'reposts': 5.0,
    'replies': 2.0
}

# Calculate total points
def calculate_points(metrics, weights):
    total_points = 0
    for metric in metrics:
        total_points += metrics[metric] * weights[metric]
    return total_points

total_points = calculate_points(engagement_metrics, weights)

print("Total Points Earned:", total_points)

Output:

Total Points Earned: 1500.0

Why Weighted Points?

• Prevents Spam and Abuse: Weighted points favor authentic engagement over mere impressions.

• Fair Reward Distribution: Higher quality content naturally receives better rewards, creating healthier competition.

• Flexible & Scalable: Easily adjustable weights allow continuous improvement of the system based on real data and user behavior.

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Next, we’ll explain clearly how LP rewards distribution works, covering the specifics of the liquidity provider shares and transaction fee allocations.