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5.3 Polarization Part II

Heckman Library 406C


📔 Session A (Lecture)

Here are the main topics covered in your “Polarization II” lecture:

  1. Echo Chambers & Network Formation: How homophily (like-seeks-like) creates clustering that leads to self-reinforcing bubbles with limited cross-group contact

  2. Filter Bubbles & Algorithms: The synergy between user-driven selective exposure and algorithm-driven personalization that creates parallel information universes

  3. Evidence & Modeling: Real-world data from Twitter and Facebook paired with agent-based models (ABM) showing how network structure and algorithmic filtering produce polarization

  4. Misinformation Advantage: Why false news spreads 6× faster than truth, driven by novelty, emotion, and confirmation bias in polarized clusters

  5. The Vicious Cycle: How echo chambers → misinformation → biased assimilation → hardened attitudes → deepened polarization form a self-reinforcing spiral

  6. Intervention #1: Breaking Echo Chambers: Cross-cutting communication that works (gradual, civil, credible) versus approaches that backfire (hostile exposure)

  7. Intervention #2: Algorithmic Design: Platform changes like diversified feeds, downranking extremes, and user control tools that can limit fake news spread

  8. Intervention #3: Inoculation & Trusted Voices: Prebunking tactics, accuracy nudges, counter-attitudinal validators, and media literacy as resistance-building strategies

  9. 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.

  1. 📖 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).

  2. 📖 Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.

  3. 📖 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.


References
  1. 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
  2. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151. 10.1126/science.aap9559
  3. 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