No persisted videos match this date range.

Days to Trend Analysis

Analysis of time between video publication and trending appearance

Generated 2026-11-12 21:07 UTC
Feature Limitation

This analysis requires trending date information which is not currently available in the YouTube API response. The Python notebook analysis used historical CSV data that included both publication date and trending date.

Workaround: This implementation shows the analysis structure and methodology. To enable this feature, trending date data would need to be collected and stored separately.

Analysis Methodology
What is "Days to Trend"?

"Days to Trend" measures the time elapsed between when a video is published and when it appears on YouTube's trending list. This metric helps understand:

  • How quickly content gains traction
  • The viral potential of different content types
  • Optimal publication timing for trending success
  • Content lifecycle patterns
Python Notebook Analysis

The Python EDA notebook performed comprehensive days-to-trend analysis including:

  • Distribution histograms of days to trend
  • Statistical analysis (mean, median, mode)
  • Correlation with other metrics (views, likes)
  • Category-based comparisons
  • Time series analysis of trending patterns
Statistical Summary (Placeholder)
N/A

Average Days to Trend

Mean Days N/A
Median Days N/A
Minimum Days N/A
Maximum Days N/A
Standard Deviation N/A
Distribution Analysis (Placeholder)
Python Notebook Findings: Most videos trend within 0-7 days of publication, with a peak at 1-2 days. Some videos take weeks or months to trend, indicating different viral patterns.
Implementation Requirements
To Enable This Feature
  1. Extend VideoDto Model: Add TrendingDate property 
    public DateTime TrendingDate { get; set; }
  2. Update YouTube Service: Store trending date when fetching trends 
    // When fetching trending videos, record current date as trending date
video.TrendingDate = DateTime.UtcNow;
  3. Historical Data Collection: Maintain a database of trending videos with dates 
    // Store video trending events in a database
// Track when each video first appeared on trending lists
  4. Data Analysis: Calculate days between PublishedAt and TrendingDate 
    var daysToTrend = (video.TrendingDate - video.PublishedAt).TotalDays;
Python Notebook Implementation Reference

The notebook calculated days to trend using: 

# Python code from notebook
df['days_to_trend'] = (df['trending_date'] - df['publish_time']).dt.days
df['days_to_trend'].describe()
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