Exploring Cultural Evolution Through Modular Dynamics in Temporal Hashtag Networks

Area of Science:

• Computational Social Science
• Network Science
• Cultural Evolution

Background:

• Social media generates vast digital traces, offering new avenues for studying cultural dynamics.
• Analyzing cultural evolution in complex, dynamic online networks presents significant methodological challenges.

Purpose of the Study:

• To develop a robust methodological framework for analyzing temporal dynamics of cultural modules within hashtag co-occurrence networks.
• To address challenges posed by dense, skewed, and highly variable cultural network data.

Main Methods:

• Introduced a perturbation ensemble clustering approach to distinguish stable from unstable structural elements in networks.
• Applied the Leiden algorithm to a perturbed ensemble of hashtag networks.
• Analyzed four years of data from a major photo-sharing platform.

Main Results:

• Identified robust core modules and their stable periphery within cultural networks.
• Distinguished stable cultural modules from elements with unstable associations.
• Revealed complex patterns of cultural module evolution, including stable associations and dynamic reorganizations.

Conclusions:

• Ensemble clustering techniques effectively capture the interplay between stability and change in evolving cultural systems.
• The proposed framework provides a powerful tool for understanding cultural evolution through digital traces.
• This research advances the study of cultural dynamics in the digital age.