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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15706324 |
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| _version_ | 1866901989487542272 |
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| author | Kulkarni, Siddharath |
| author_facet | Kulkarni, Siddharath |
| contents | <p>This study investigates viewer engagement on YouTube using a comprehensive dataset <br>containing over 575,000 video and channel entries. Key features such as likes/views, <br>dislikes/views, views per subscriber, and channel activity are used to model and predict total <br>view count and engagement behavior. The objective is to uncover patterns that contribute to <br>content virality and user retention. Using linear regression via Scikit-learn, we identify <br>significant predictors of channel view performance and evaluate model strength through R² <br>and error metrics. Findings show that engagement ratios like dislikes/views and likes/views <br>significantly impact visibility, suggesting that even controversial content can drive traffic. <br>The research also employs visual analytics (scatter plots, heatmaps) to reinforce findings. <br>Results suggest that digital visibility can be engineered through engagement optimization <br>rather than content volume alone.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15706324 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Clicks and Views - Analyzing YouTube Engagement Patterns Using Regression Modeling Kulkarni, Siddharath <p>This study investigates viewer engagement on YouTube using a comprehensive dataset <br>containing over 575,000 video and channel entries. Key features such as likes/views, <br>dislikes/views, views per subscriber, and channel activity are used to model and predict total <br>view count and engagement behavior. The objective is to uncover patterns that contribute to <br>content virality and user retention. Using linear regression via Scikit-learn, we identify <br>significant predictors of channel view performance and evaluate model strength through R² <br>and error metrics. Findings show that engagement ratios like dislikes/views and likes/views <br>significantly impact visibility, suggesting that even controversial content can drive traffic. <br>The research also employs visual analytics (scatter plots, heatmaps) to reinforce findings. <br>Results suggest that digital visibility can be engineered through engagement optimization <br>rather than content volume alone.</p> |
| title | Clicks and Views - Analyzing YouTube Engagement Patterns Using Regression Modeling |
| url | https://doi.org/10.5281/zenodo.15706324 |