Designing a Real Popularity Algorithm for Virtual Land Parcels in Second Life

In a dynamic virtual world like Second Life, parcel popularity is a critical metric - one that influences user engagement, traffic flow, content discovery, and monetization. However, accurately measuring the true popularity of a land parcel is far more complex than simply counting visits. Fake activity, idle avatars, and manipulation through bots can easily distort surface-level metrics.

To address this, we propose a more refined algorithm that calculates true parcel popularity based on verifiable engagement signals, while actively filtering out artificial behaviors. This paper outlines the input variables, heuristic filters, scoring methodology, and potential for machine learning integration.

We consider six core variables associated with each parcel. These are chosen for their indicative value and resistance to manipulation:

1. Number of Picks - pick_score = log2(pick_count + 1)

   The number of user profile picks pointing to the parcel. Since profile picks are limited in number and require intentional user action, they serve as a high-trust indicator.

2. Advertisement Presence - ad_score = 1 if classified_ad_present else 0

   Whether the parcel is actively being advertised through the in-world classifieds system.

3. Event Registration - event_score = 1 if event_present else 0

   Whether an official Second Life event is scheduled on the parcel.

4. Visitor Time (Adjusted)

   The total time avatars spend on the parcel, adjusted through behavioral analysis to exclude artificial or idle presence.

5. Unique Visitors - unique_visitors_score = log2(unique_avatars + 1)

   The number of distinct avatars visiting the parcel during the measured period.

6. Interaction Events

   In-world interaction signals, such as object touches, scripted communication, or sensor-detected movement.

Filtering Artificial Presence

The Problem:

Some parcel owners inflate metrics by deploying scripted bots or idle avatars that remain stationary for extended periods.

The Solution:

Apply behavioral filtering heuristics to adjust or discount suspicious visitor time:

Suspicion Criteria:

Indicator - Description
Excessive duration - Average session > 90 minutes
Lack of movement - No walking, turning, or teleport
Low interaction - No object touches or chat
Repetitive pattern - Same login time each day
Same visitor - One avatar reappears daily with no activity

When one or more criteria are met, the visitor's time is either heavily weighted down or excluded entirely.

Final Popularity Score Calculation

We propose the following scoring function:

popularity_score =
    (log2(pick_count + 1) * 2.0) +
    (ad_score * 1.0) +
    (event_score * 1.5) +
    (adjusted_time_score * 2.5) +
    (unique_visitors_score * 2.0) +
    (interaction_score * 1.5)

Each component is normalized to ensure fair scaling. The adjusted_time_score is derived only from filtered session durations. Interaction score can be defined based on event frequency per hour.

Example:

14 profile picks, advertisment created, event created, total visitor time 880 minutes, filtered visitor time 510 minutes, 9 unique visitors, 4.2 interaction score

pick log2(14 + 1) = log2(15) ≈ 3.91 → 3.91 × 2.0 = 7.82
adjusted_time_score = 8.5 (510 min = 8.5 hours) × 2.5 = 21.25
unique_visitors = log2(9 + 1) = log2(10) ≈ 3.32 → 3.32 × 2.0 = 6.64
interaction_score = 4.2 × 1.5 = 6.3

popularity_score = 7.82  (pick) + 1.0   (ad) + 1.5   (event) + 21.25 (adjusted time) + 6.64  (unique visitors) + 6.3   (interactions) = ** 44.51 **

Machine Learning Integration (Optional)

As data accumulates, a supervised machine learning model could be trained to:

• Distinguish real vs. artificial sessions

• Predict parcel popularity based on historical patterns

• Recommend optimization strategies to landowners

Initial models could include decision trees, logistic regression, or shallow neural networks trained on labeled activity patterns.

Conclusion

True popularity in a virtual environment like Second Life cannot be measured solely by traffic or time-on-parcel. By incorporating verified engagement signals - such as profile picks, event status, unique visitor diversity, and interactive behavior - we can construct a realistic, manipulation-resistant popularity index that better serves users, creators, and the platform itself.

This algorithmic framework provides both immediate utility and room for future evolution through data-driven refinement.