🪴 Scaling Synthesis

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C- Most people will primarily consume information

Last updated May 18, 2022

Authored By:: P- Brendan Langen, P- Rob Haisfield

Statistics over the years cite a 90-9-1 rule to contributors in online communities, and although that does not hold up across all communities, the majority of people fall into the consumption-only group (90%). Most site data is not publicly shared, but estimates range from 80-98% of people as lurkers, 1.9%-19% of people as editing/updating content, and .1-1% of people as primary contributors.

Looked at differently, the 90-9-1 rule also indicates the breakdown of contributions from users. In this group, 90% of posts come from 1% of users, 10% of posts come from 9% of users, while no posts come from 90% of users. Our closest public comparison to a discourse graph is Wikipedia. In 2006, 99.8% of visitors were lurkers, 0.2% edited pages, and only .003% were contributors.

This speaks to a large gap in the activity we want to see in a discourse graph. It will be important to capture the potential energy of information consumption. Additionally, we need to enable workflows and behaviors to facilitate synthesis. How can you lower the barriers for someone to meaningfully contribute?

Synthesis is supported by active reading, and a number of tools assist with this.

LiquidText is built on the claim that systems must enable people to trace excerpts back to their original context to support active reading, because knowledge must be recontextualized to be usefully reused. The tool allows you to “pull out” excerpts and make pointers to context, and use these units on a canvas to weave together a larger understanding, albeit in a less formal fashion.

Hypothesis allows users to annotate webpages and documents in their margins, providing the option to further enrich a snippet with context. Sharing is built in to Hypothes.is, affording added context and active reading across groups. This enables social tagging, which helps users find related content and build community. Social tagging is a key user behavior to managing a decentralized knowledge graph. This can come in the form of text, likes or emoji reactions.

Readwise offers a view towards actions a reader can take to save important notes and passages without considerable effort. P- Tiago Forte famously coined the term “progressive summarization,” which is the behavior we are looking to develop in discourse graph communities - incrementally processing notes is a key user behavior to promote synthesis. Progressive summarization refers to consuming an information resource, taking the important parts out of it, rereading the important parts at a later date, summarizing the most important parts of that, and so on, until you only have the most important content.

How can we incentivize users to contribute? Perhaps anonymous author contributions combined with identifiable editors can increase the volume and quality of reviews while reducing bias. A helpful proxy to ask ourselves is what would a Web3 Wikipedia look like?

Our goal is to enable people to maintain a decentralized discourse graph with a high quantity of information as it grows over time.