Fake News Propagation Simulation

Computational and mathematical approaches to modeling and predicting how misinformation cascades through social networks and populations. Propagation simulations aim to: (1) understand mechanisms driving spread (who believes and shares, why, how rapidly), (2) forecast future spread patterns, (3) test intervention strategies before deployment.

Approaches range from traditional epidemic models (SIR: Susceptible-Infected-Recovered) adapted to information spread, to point-process models (Hawkes processes) capturing temporal patterns, to agent-based simulations with realistic user behavior and social network structure. Recent work incorporates large language models to capture semantic richness of opinions and reasoning processes.

Key papers

• From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News — LLM-based agent simulation with personality, memory, and reasoning mechanisms
• Propagation Models — broader survey of modeling approaches

Related topics

• Agent-Based Modeling — simulation methodology
• Opinion Dynamics — individual belief evolution in propagation
• Personality Traits and Fake News Susceptibility — how individual susceptibility varies
• Intervention Strategies for Misinformation — counter-interventions to slow or prevent spread