Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.

Area of Science:

• Social Network Analysis
• Computational Social Science
• Online Behavior Dynamics

Background:

• Coordinated online behavior is crucial for large-scale campaigns, yet current detection methods often overlook temporal dynamics.
• Static analyses fail to capture the fluid nature of user interactions and group formation in online environments.

Purpose of the Study:

• To perform a dynamic analysis of coordinated online behavior.
• To identify and characterize groups exhibiting coordinated behaviors over time.
• To highlight the limitations of static analysis in understanding temporal network dynamics.

Main Methods:

• Construction of a multiplex temporal network to model user interactions.
• Application of dynamic community detection algorithms to identify evolving groups.
• Analysis of community instability, user archetypes, and factors influencing group membership.

Main Results:

• Coordinated communities (CCs) exhibit significant temporal instability.
• Dynamic analysis reveals limitations of static approaches for unstable communities.
• Identification of distinct user archetypes and key factors driving user participation in CCs.

Conclusions:

• Dynamic analysis is essential for accurately studying coordinated online behavior.
• Understanding temporal dynamics is key to analyzing online debates and influence patterns.
• Future research can explore CC strategies and evolving online influence patterns.