Propagation Models

Statistical and computational approaches to modeling how information, misinformation, and cultural artifacts spread through networks over time. Propagation models aim to understand and predict cascade trajectories—the path information takes as it moves from person to person across social networks. Approaches range from stochastic point-process models (e.g., Hawkes processes, branching processes) to agent-based simulations and machine learning approaches.

Key papers

• From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News — LLM-based agent simulation with dual memory and personality-driven opinion evolution
• Zannettou et al. (2018) — On the Origins of Memes by Means of Fringe Web Communities: Uses Hawkes processes to quantify directed influence between Web communities in meme dissemination

Related topics

• Propagation-based fake news detection — using propagation patterns to detect misinformation
• Cascade Analysis — characterizing and predicting information cascades
• Social Media Analysis — empirical measurement of content diffusion

See also