AI-Driven Clustering of Social Media Consumption Patterns and Daily Productivity Using K-Means and DBSCAN in Multigenerational Respondents
Keywords:
Daily Productivity, DBSCAN, K-Means Clustering, Unsupervised Learning, Social Media ConsumptionAbstract
Purpose – The rapid development of digital technology has made social media an integral part of life across generations, yet its intensive use raises growing concerns regarding its impact on daily productivity. This study aims to analyze patterns of social media consumption behavior and their relationship with productivity across age groups using a dual clustering approach based on the K-Means and DBSCAN algorithms.
Methods – The study utilizes secondary data from 3,000 multigenerational respondents, processed using Orange Data Mining through stages of data selection, normalization, and unsupervised clustering. K-Means is employed to segment respondents based on proximity to cluster centroids, while DBSCAN is applied to identify density-based behavioral patterns and detect outliers representing extreme digital usage behaviors.
Findings – The results indicate that K-Means effectively maps macro-level clusters primarily differentiated by age, achieving an average Silhouette score of 0.537, which reflects stable and well-separated segmentation. In contrast, DBSCAN demonstrates superior capability in identifying micro-level behavioral patterns, particularly respondents exhibiting extreme characteristics such as excessive screen time and non-productive application usage, despite yielding a lower overall Silhouette value. The comparative analysis highlights that K-Means is more suitable for demographic-based segmentation, whereas DBSCAN provides deeper insights into localized and atypical digital behavior.
Research limitations – The analysis is based on a randomly sampled subset of a publicly available dataset, which may limit the generalisability of the findings across different cultural, occupational, and socioeconomic contexts. Future studies are encouraged to incorporate longitudinal data and additional behavioral variables to capture temporal dynamics and causal relationships between social media usage and productivity.
Originality – This study contributes by systematically comparing centroid-based and density-based clustering approaches within a multigenerational framework to reveal both macro-demographic and micro-behavioral patterns of digital consumption. The proposed dual clustering strategy offers a novel analytical perspective for designing more adaptive and evidence-based digital literacy and productivity enhancement policies.
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