Viral Attention Cascade & Crowdsourcing Simulator

Simulate outrage diffusion dynamics, network echo-chambers, and power-law crowdsourcing curves

Network Attention Propagation Map Step 0
Unexposed
Outrage Active
Amplifier (Hub)
Satiated/Decayed
Node #0
Centrality (Degree): 0
State: Susceptible
Sec. Infections: 0
Contributed: $0
Scrub Step:
Active Active Nodes
0
0.0% Network Saturation
Peak Saturation Step
--
Max 0 infected nodes
Total Crowdsourced
$0
$0 avg per infected node
Power-Law Fit (α)
2.15
Scale-Free Threshold (α ~ 2.15)
Power-Law Degree Distribution
P(k) ~ k-2.15
Cumulative Crowdsourcing ($)
+0.0%/step
Network Topology Preset
Scale-Free (BA)
Small-World (WS)
Random (ER)
Cascade Parameters
Viral Reproduction Rate (R₀) 2.40

Avg secondary transmissions per active node.

Outrage Amplification Bias 0.65

Weighting multiplier for hub transmission probability.

Echo Chamber Clustering 0.78

Local triadic closure probability in network generation.

Attention Decay Half-Life Rate 0.12

Step-wise decay rate from Active Outrage to Decayed state.

Total Network Nodes 150
Dynamical Insights

In scale-free social networks, attention spikes follow a heavy-tailed power law: P(k) ~ k-α. Hubs absorb and amplify emotional outrage, accelerating peak node saturation typically around time steps 12–16, driving exponential crowdsourcing capital inflows.

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