Koninikax / YouTube-trends

YouTube Data Collection and Analysis

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YouTube-trends

YouTube Data Collection and Analysis

I've collected data about the trending videos on YouTube to analyze and find what makes a video trend on YouTube.

For data collection first, I used an API from Google Cloud Console.

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The histograms show that the distributions of view counts, like counts, and comment counts are right-skewed, with most videos having lower counts and a few videos having very high counts.

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The heatmap confirms strong positive correlations between views, likes, and comments.

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The bar chart shows that the Gaming, Entertainment, Sports, and Music categories have the highest number of trending videos.

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Music and People & Blogs categories have the highest average view counts, likes, and comments. Film & Animation also shows high engagement, especially in view counts and like counts.

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The scatter plot shows a slight negative correlation between video length and view count, indicating shorter videos tend to have higher view counts. Videos in the 0-5 minute range have the highest average view counts, likes, and comments. Engagement decreases as video length increases.

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The scatter plot shows a very weak relationship between the number of tags and view count, suggesting that the number of tags has minimal impact on a video’s view count.

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The distribution shows that most videos are published between 14:00 and 20:00 hours (2 PM – 8 PM), indicating this may be an optimal time for uploading videos. There is a very weak negative relationship between publish hour and view count, suggesting that the hour of publication has minimal impact on engagement metrics.

Conclusion

My conclusion based on the data, for a vid to trend on YouTube:

  1. Encourage viewers to like and comment on videos to boost engagement metrics.

  2. Aim to create shorter videos (under 5 minutes) for higher engagement, especially for categories like Music and Entertainment.

  3. Schedule video uploads around peak times (2 PM – 8 PM) to maximize initial views and engagement.

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YouTube Data Collection and Analysis


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