post3Why does Web3 Publishing Matter?
Where does the ownership of one’s content reside in the world of online writing?Writers have been using publishing platforms for a long time, either to showcase their content or to generate income. Platforms like Medium, Substack or Hashnode pave the way for writers to get noticed on the vast web, by providing great SEO and easing the writing experience. But content can easily be copied, difficult to trace back and there’s no guarantee that these platforms will be here forever. Web3 publishin...
post3Mirror Analysis: Week 6 in Review (2024)
This entry is part of the series: Mirror Entries Analysis. Each week, Post3 utilizes data extraction and data analysis techniques to deliver insightful reports with information concerning authors, articles, revenue, chains, keywords, and more, derived from exploring Mirror data. For Week 6, we tackle the following questions:Who are the authors from whom people have collected the most?Which entries were the most collected?Which authors generated the most revenue?Which entries generated the mos...
post3Mirror Entries Analysis: Week 1 in Review (2024)
This entry is part of the series: Mirror Entries Analysis. Each week, Post3 utilizes data extraction and data analysis techniques to deliver insightful reports with information concerning authors, articles, revenue, chains, keywords, and more, derived from exploring Mirror data. For Week 1, we tackle the following questions:Who are the authors from whom people have collected the most?Which entries were the most collected?Which authors generated the most revenue?Which entries generated the mos...
The ultimate web3 newsletter for discovering cutting-edge projects, airdrops, and overall crypto content. Mint and get a dataset.
post3Why does Web3 Publishing Matter?
Where does the ownership of one’s content reside in the world of online writing?Writers have been using publishing platforms for a long time, either to showcase their content or to generate income. Platforms like Medium, Substack or Hashnode pave the way for writers to get noticed on the vast web, by providing great SEO and easing the writing experience. But content can easily be copied, difficult to trace back and there’s no guarantee that these platforms will be here forever. Web3 publishin...
post3Mirror Analysis: Week 6 in Review (2024)
This entry is part of the series: Mirror Entries Analysis. Each week, Post3 utilizes data extraction and data analysis techniques to deliver insightful reports with information concerning authors, articles, revenue, chains, keywords, and more, derived from exploring Mirror data. For Week 6, we tackle the following questions:Who are the authors from whom people have collected the most?Which entries were the most collected?Which authors generated the most revenue?Which entries generated the mos...
post3Mirror Entries Analysis: Week 1 in Review (2024)
This entry is part of the series: Mirror Entries Analysis. Each week, Post3 utilizes data extraction and data analysis techniques to deliver insightful reports with information concerning authors, articles, revenue, chains, keywords, and more, derived from exploring Mirror data. For Week 1, we tackle the following questions:Who are the authors from whom people have collected the most?Which entries were the most collected?Which authors generated the most revenue?Which entries generated the mos...
The ultimate web3 newsletter for discovering cutting-edge projects, airdrops, and overall crypto content. Mint and get a dataset.
📌 Mint this entry and subscribe to Post3 newsletter to get the dataset that powered this analysis delivered to your email inbox.
This entry is part of the series: Mirror Entries Analysis. Each week, Post3 utilizes data extraction and data analysis techniques to deliver insightful reports with information concerning authors, articles, revenue, chains, keywords, and more, derived from exploring Mirror data.
A filter is applied to the extracted data. For instance, entries with one-word titles and bodies with less than 55 words are not considered to minimize the noise and incentivize good writing practices. In addition, Large Language Models (LLMs) are utilized to obtain tags from each article.
On week 18 we tackle the following questions:
What are the general statistics?
How does the user activity change over the week?
Who are the authors from whom people have collected the most?
Which entries were the most collected?
Which authors/publications generated the most revenue?
Which entries generated the most revenue?
What was the networks/chains usage?
⚠️ Note: The number of collections/mints of some entries might have changed at the time I’m writing.
Let's begin to analyse 1076 posts collected from week 18.
By analyzing weekly statistics, we can gain insights into user activity and identify any imbalances in collection and revenue distributions. Let's explore the total collected and earned revenue (in USD) along with the average (mean), middle value (median), and spread (standard deviation) of these metrics.
| Features | Total | Mean | Median | Std |
| ----------- | -------- | -------- | -------- | -------- |
| Collections | 2634.0 | 2.5 | 0.0 | 36.1 |
| Revenue | 2885.1 | 2.8 | 0.0 | 40.0 |
While there are several ways to measure the user activity on Mirror, one that gives us a better understanding of the writers' activity, is by observing the total of articles created per day of the week, along with the number of collections and revenue. By visualizing these three metrics in a single chart, we can identify potential correlations between them.
👉 Join Post3 Discord community here. Follow Post3 on Twitter (aka X) and Warpcaster
The number of times an article has been collected/minted serves as a valuable metric to understand an author's popularity on Mirror. The “Author“ is the publication/newsletter, some authors such as protocols and ecosystems have several contributors that write to their publications. Let’s take a look at the ones whose work has attracted more collectors.
Below is the list of the authors/publications with the most collections:
📌 Mint this entry and subscribe to Post3 newsletter to get the dataset that powered this analysis delivered to your email inbox. Try it out on Post3 Engine:
Some authors publish several times in a weekly period, which grants them more collections than others. Hence we need to take a look at entries individually, to see which ones performed better. These are the top entries:
👉 Join Post3 Discord community here. Follow Post3 on Twitter (aka X) and Warpcaster
Revenue serves as an indicator of one's ability to attract and retain people to mint their content. Here we’ll take a look at the authors that generated the most revenue from minted entries, and how it correlates with collections.
Below is the list of authors/publications with the most revenue:
Just as for collections, revenue must be studied individually. People may be loyal to their favourite authors, but in the end, they will mint what they really like or find useful. Studying entries individually is important for writers to understand what kind of content people are willing to mint, and at what price. Below, are the entries with the most revenue:
Understanding the usage of L2 chains for minting NFT articles, is key for writers to decide which network should they use. The following pie chart only compares the usage, other metrics should be taken into account, such as the type of articles that are being published in each chain and so on.
On week 18, Optimism dominates with 96.4% of network usage. In the second position, we have Zora with 1.1%. The third most used network is Base with 1.0%. Followed by Linea with 0.8% and finally Polygon with 0.8%.
👉 Join Post3 Discord community here. Follow Post3 on Twitter (aka X) and Warpcaster
Post3 encourages you to explore the dataset and uncover more gems or generate your own charts and insights. The datasets contain the following features:
platform: web3 publishing platform.
title: the title of the article.
description: a short description of the article.
body: the full content of the article.
link: the URL for the article.
arweave_link: the URL for the Arweave JSON content.
author: the author/publication.
contributor_link: the writer of the article.
date: the date when the article was first published.
tags: tags that define the article generated using LLMs.
collections: number of mints the article has at the time the data was extracted.
supply: the maximum number of mints an article can have.
price: the price of the article in ETH or MATIC depending on the currency feature.
price_usd: the price in USD.
currency: either MATIC or ETH, others may join in the future.
network: the L2 solution used to mint the article.
📌 Mint this entry and subscribe to Post3 newsletter to get the dataset that powered this analysis delivered to your email inbox. Try it out on Post3 Engine:
📌 Mint this entry and subscribe to Post3 newsletter to get the dataset that powered this analysis delivered to your email inbox.
This entry is part of the series: Mirror Entries Analysis. Each week, Post3 utilizes data extraction and data analysis techniques to deliver insightful reports with information concerning authors, articles, revenue, chains, keywords, and more, derived from exploring Mirror data.
A filter is applied to the extracted data. For instance, entries with one-word titles and bodies with less than 55 words are not considered to minimize the noise and incentivize good writing practices. In addition, Large Language Models (LLMs) are utilized to obtain tags from each article.
On week 18 we tackle the following questions:
What are the general statistics?
How does the user activity change over the week?
Who are the authors from whom people have collected the most?
Which entries were the most collected?
Which authors/publications generated the most revenue?
Which entries generated the most revenue?
What was the networks/chains usage?
⚠️ Note: The number of collections/mints of some entries might have changed at the time I’m writing.
Let's begin to analyse 1076 posts collected from week 18.
By analyzing weekly statistics, we can gain insights into user activity and identify any imbalances in collection and revenue distributions. Let's explore the total collected and earned revenue (in USD) along with the average (mean), middle value (median), and spread (standard deviation) of these metrics.
| Features | Total | Mean | Median | Std |
| ----------- | -------- | -------- | -------- | -------- |
| Collections | 2634.0 | 2.5 | 0.0 | 36.1 |
| Revenue | 2885.1 | 2.8 | 0.0 | 40.0 |
While there are several ways to measure the user activity on Mirror, one that gives us a better understanding of the writers' activity, is by observing the total of articles created per day of the week, along with the number of collections and revenue. By visualizing these three metrics in a single chart, we can identify potential correlations between them.
👉 Join Post3 Discord community here. Follow Post3 on Twitter (aka X) and Warpcaster
The number of times an article has been collected/minted serves as a valuable metric to understand an author's popularity on Mirror. The “Author“ is the publication/newsletter, some authors such as protocols and ecosystems have several contributors that write to their publications. Let’s take a look at the ones whose work has attracted more collectors.
Below is the list of the authors/publications with the most collections:
📌 Mint this entry and subscribe to Post3 newsletter to get the dataset that powered this analysis delivered to your email inbox. Try it out on Post3 Engine:
Some authors publish several times in a weekly period, which grants them more collections than others. Hence we need to take a look at entries individually, to see which ones performed better. These are the top entries:
👉 Join Post3 Discord community here. Follow Post3 on Twitter (aka X) and Warpcaster
Revenue serves as an indicator of one's ability to attract and retain people to mint their content. Here we’ll take a look at the authors that generated the most revenue from minted entries, and how it correlates with collections.
Below is the list of authors/publications with the most revenue:
Just as for collections, revenue must be studied individually. People may be loyal to their favourite authors, but in the end, they will mint what they really like or find useful. Studying entries individually is important for writers to understand what kind of content people are willing to mint, and at what price. Below, are the entries with the most revenue:
Understanding the usage of L2 chains for minting NFT articles, is key for writers to decide which network should they use. The following pie chart only compares the usage, other metrics should be taken into account, such as the type of articles that are being published in each chain and so on.
On week 18, Optimism dominates with 96.4% of network usage. In the second position, we have Zora with 1.1%. The third most used network is Base with 1.0%. Followed by Linea with 0.8% and finally Polygon with 0.8%.
👉 Join Post3 Discord community here. Follow Post3 on Twitter (aka X) and Warpcaster
Post3 encourages you to explore the dataset and uncover more gems or generate your own charts and insights. The datasets contain the following features:
platform: web3 publishing platform.
title: the title of the article.
description: a short description of the article.
body: the full content of the article.
link: the URL for the article.
arweave_link: the URL for the Arweave JSON content.
author: the author/publication.
contributor_link: the writer of the article.
date: the date when the article was first published.
tags: tags that define the article generated using LLMs.
collections: number of mints the article has at the time the data was extracted.
supply: the maximum number of mints an article can have.
price: the price of the article in ETH or MATIC depending on the currency feature.
price_usd: the price in USD.
currency: either MATIC or ETH, others may join in the future.
network: the L2 solution used to mint the article.
📌 Mint this entry and subscribe to Post3 newsletter to get the dataset that powered this analysis delivered to your email inbox. Try it out on Post3 Engine:
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revenue: collections times the price in USD.
revenue: collections times the price in USD.
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