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Weekly progress
- ⚒ Updated the website for Narrative Analysis (work-in-progress).
- 🤝 Advised another project.
Main hypothesis
Every message m is read by n(m) people. Of these, t(m) people will trade based on the message (buy or sell), s(m) people will share the message on their channels as-is, and p(m) people will become “pulsars” — they will decide to continue spreading messages about a specific asset.
We hypothesize that p(m) — the count of pulsars from the message — is the most important number. This is supported by the fact that the more pulsars there are, the more similar messages we will see on average in the next time period.
For example: a person reads an article about Bitcoin and becomes convinced that crypto is the future. He starts a YouTube channel and begins to continuously post videos where he talks about crypto with positive sentiment.
Every video has a chance to generate new pulsars.
Just like “retention” is the most important metric of startups, the “pulsar ratio” is the most important metric of narratives. Those two metrics are correlated: the amount of people who are using the product is correlated with the amount of people who are talking about the product.
We will continue researching ways to determine the conditions under which people become “pulsars” & how it affects the spread of narrative.
We have published a lot of other thoughts on the Narrative Analysis website — be sure to check it out!
Next week’s focus
- 🤝 Advise current projects.
- 🤝 Publish about narrative analysis.
About Coliquidity
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