📔 Session A (Lecture)¶
Here are the main topics covered in your “Polarization II” lecture:
Echo Chambers & Network Formation: How homophily (like-seeks-like) creates clustering that leads to self-reinforcing bubbles with limited cross-group contact
Filter Bubbles & Algorithms: The synergy between user-driven selective exposure and algorithm-driven personalization that creates parallel information universes
Evidence & Modeling: Real-world data from Twitter and Facebook paired with agent-based models (ABM) showing how network structure and algorithmic filtering produce polarization
Misinformation Advantage: Why false news spreads 6× faster than truth, driven by novelty, emotion, and confirmation bias in polarized clusters
The Vicious Cycle: How echo chambers → misinformation → biased assimilation → hardened attitudes → deepened polarization form a self-reinforcing spiral
Intervention #1: Breaking Echo Chambers: Cross-cutting communication that works (gradual, civil, credible) versus approaches that backfire (hostile exposure)
Intervention #2: Algorithmic Design: Platform changes like diversified feeds, downranking extremes, and user control tools that can limit fake news spread
Intervention #3: Inoculation & Trusted Voices: Prebunking tactics, accuracy nudges, counter-attitudinal validators, and media literacy as resistance-building strategies
NetLogo Modeling Opportunities: Teaching applications for simulating echo chamber formation, misinformation cascades, and testing interventions
🔆 Polarization Part II (Slides)
📔 Session B (SRG)¶
Discussion of readings.
📖 Chueca Del Cerro, C. (2024). The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model. Applied Network Science, 9(1).
📖 Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.
📖 Dandekar, P., Goel, A., & Lee, D. T. (2013). Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences, 110(15), 5791–5796.
- Chueca Del Cerro, C. (2024). The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model. Applied Network Science, 9(1). 10.1007/s41109-024-00679-3
- Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. 10.1126/science.aap9559
- Dandekar, P., Goel, A., & Lee, D. T. (2013). Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences, 110(15), 5791–5796. 10.1073/pnas.1217220110