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1/ Consider: Armchair index = median num of curation layers between you and raw news. Twitter=0, new media=1, Google=2, old media=3, FB=4
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2/ Though depth is not well-posed concept for diffusion networks, Armchair Index is close. But it's not quite graph hops as in naive models
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3/ On a news diffusion graph, there's still a meaningful dynamic notion of "boundary" (set of source nodes at time T)
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4/ There is also notion of uphill (curation that adds context intelligence) and downhill (adds context noise, think Taboola, Outbrain)
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5/ Armchair index measures "altitude" above ground-level info, using a set of relevance+importance values that define an "up"
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6/ This is an uphill, stochastic diffusion process on a graph with a defined boundary and interior (peak by a set of curation values)
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10/ Every node is adding signal or noise to every curation-values landscape it is included in. Boundaries and peaks are shifting always.
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11/ We think of advertising as "noise tolls" on deterministic distribution pathways serving some illusory mass-audience curation peak.
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