Binge Analytics: Content Trends and User Engagement on Netflix Using Social Media Analytical Tools and Machine Learning
Year: 2025
Type: article
Abstract: This research explores the application of Social Media Analytics (SMA) and machine learning techniques to identify content trends and predict user engagement patterns on Netflix. Using the Netflix Movies and TV Shows dataset sourced from Kaggle, the study employs Orange Data Mining software to perform K-Means clustering and Linear Regression analysis. The clustering process categorizes content into three distinct segments based on release year, genre, and format, providing insight into consumption preferences over time. Simultaneously, the regression model predicts show duration with a high degree of accuracy (R² = 0.813), revealing strong correlations between duration and features such as genre and release year. Visual analytics tools such as scatter plots and heatmaps enhance interpretability, allowing researchers to extract actionable insights without complex programming. The findings underscore the value of integrating SMA tools in entertainment analytics, particularly for content personalization, audience segmentation, and strategic content development. Future research could further enrich the model by incorporating user interaction data and network-based genre analysis.
Author: Sagwekar, Saloni
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893342846
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