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Temporal Segmentation of Social Media Timelines: A Comparative Evaluation of Clustering Algorithms
Conference paper

Temporal Segmentation of Social Media Timelines: A Comparative Evaluation of Clustering Algorithms

Elizabeth Sheffield Arey
International Conference on Social Networks Analysis, Management and Security (SNAMS ... ) (Online), (2025), pp 543-547
25 Nov 2025
DOI: https://doi.org/10.1109/SNAMS67467.2025.11391227

Abstract

Annotations Clustering Clustering algorithms Coherence Computational efficiency Human Behavior Patterns Human Information Processing Information processing Performance evaluation Runtime Security Social networking (online) Social Networks Semantics
This work evaluates the effectiveness of clustering algorithms for temporally segmenting social media timelines into distinct episodes of use. Using only post timestamps, MeanShift, K-Means, and DBSCAN clustering methods are compared against human annotations that leverage additional metadata such as post content, device type, and language. Performance is assessed through a multi-faceted evaluation including: (1) alignment with human-labeled segmentation points to measure accuracy; (2) runtime comparisons to assess algorithmic efficiency; and (3) analysis of intra- and inter-cluster token similarity to validate the semantic coherence and distinctiveness of the resulting segments. Results show that Mean-Shift clustering consistently produces segments that closely match human annotations, while maintaining computational efficiency and yielding high internal lexical consistency. These findings suggest that timestamp-based clustering provides a low-dimensional, reproducible method for detecting episodes of social media use. This segmentation supports downstream tasks such as author attribution and verification by providing a foundation for longitudinal analyses of online behavior and construction of sequential datasets within a single user account.

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