· Complex Networks · Information Diffusion
Mechanisms of the Information Cocoon Effect in Social Networks
Uses a weighted directed network to model information reception, processing, diffusion, and opinion feedback, combining diffusion dynamics with structural analysis to study anomalous amplification, key-node identification, and the formation and intervention of information cocoons.
Problem
Node influence, interests, recommendation feedback, and community structure jointly reshape diffusion paths: local signals can be anomalously amplified into a “scream effect,” while persistent opinion reinforcement can form information cocoons. A unified model must therefore capture diffusion dynamics, network structure, and feedback-driven opinion change.
Mathematical and algorithmic approach
The model builds a weighted directed network from node influence, edge weights, and interest thresholds; SEIR dynamics and impulse functions describe diffusion scale and anomalous amplification, while LeaderRank and k-core identify influential nodes and structural intervention points. The information-cocoon extension introduces opinion values, edge-weight feedback, and an error-backpropagation-inspired update rule to model the chain of information reception, processing, and transmission.
My contribution
Served as project lead across both stages, responsible for network modeling, diffusion and feedback mechanisms, simulation, structural analysis, and manuscript preparation.
Results and outputs
The work produced the paper “Research of the propagation mechanism of scream effect and its simulation”; the university Undergraduate Research Development Program project “Mechanisms of the Information Cocoon Effect in Social Networks” concluded with an Excellent rating. The research was supported by the 2023 China Undergraduate Mathematical Contest in Modeling Post-Competition Research Program.
Workflow
- 01
Network representation
Represent the social network through influence, edge weights, and interest thresholds.
- 02
Diffusion dynamics
Combine SEIR and impulse mechanisms to simulate diffusion and anomalous amplification.
- 03
Opinion feedback and cocoon evolution
Model the evolution of information cocoons through opinion values, edge weights, and feedback updates.
- 04
Structural diagnosis
Use LeaderRank and k-core to identify key nodes and evaluate interventions.