The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of...

Area of Science:

• Computational Social Science
• Sociology
• Information Science

Background:

• Filter bubbles and echo chambers are increasingly linked to societal shifts like Brexit and US political polarization.
• Understanding the mechanisms driving societal fragmentation is crucial in the digital age.

Purpose of the Study:

• To investigate the circumstances under which social media and recommender algorithms contribute to societal fragmentation into echo chambers.
• To propose and test a triple-filter-bubble framework encompassing individual, social, and technological filtering levels.

Main Methods:

• Agent-based modeling (ABM) was employed to simulate information filtering processes.
• Twelve distinct information filtering scenarios were analyzed to assess their impact on echo chamber formation.

Main Results:

• Echo chambers emerge even without social or technological filters, driven by cognitive mechanisms like confirmation bias under central information propagation.
• The addition of social and technological filtering mechanisms exacerbates societal polarization into more distinct and less interconnected echo chambers.

Conclusions:

• Cognitive biases play a significant role in echo chamber formation, independent of technological influences.
• Social and technological filters amplify societal fragmentation, highlighting the complex interplay between individual psychology and digital environments.