Detection of a core-periphery structure in bipartite user-content networks based on modularity and Stochastic Block...

Area of Science:

• Social Network Analysis
• Computational Social Science
• Information Science

Background:

• Understanding audience engagement with organizations on social media is crucial.
• Previous network models often fail to capture the complexity of real-world interactions.

Purpose of the Study:

• To model and analyze audience interaction patterns with Italian organizations on Twitter.
• To investigate the network structure, specifically the presence of a core-periphery organization.
• To examine how this structure relates to community formation within the networks.

Main Methods:

• Bipartite network modeling of user-tweet interactions (retweets, replies).
• Comparison with null models: bipartite configuration model, uniform bipartite graph, 'soft' configuration model.
• Degree Corrected Stochastic Block Model (DC-SBM) for core-periphery analysis.
• Bipartite modularity optimization for community structure analysis.

Main Results:

• Italian Twitter organization-audience networks exhibit unique structures, deviating from standard null models.
• A distinct bipartite core-periphery organization was identified using DC-SBM.
• Community structures primarily consist of star graphs centered on core users or influential tweets.

Conclusions:

• The study highlights the specific network dynamics of audience interaction with organizations on Twitter.
• The identified core-periphery structure and star-like communities offer new insights into online engagement patterns.
• Findings suggest a departure from traditional network models, emphasizing the importance of central actors and content.