Mirror Analysis: Week 17 in Review (2024)

April 30th, 2024

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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 17 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 786 posts collected from week 17.

1 - What are the general statistics?

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 | 937.0    | 1.2      | 0.0      | 10.0     |
| Revenue     | 905.6    | 1.2      | 0.0      | 9.3      |

2 - How does the user activity change over the week?

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.

Bar chart showing the number of entries, collections and revenue per day

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3 - Who are the authors/publications from whom people have collected the most?

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.

Bar chart showing the authors with the most collections

Below is the list of the authors/publications with the most collections:

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4 - Which entries were the most collected?

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:

Bar chart showing the entries with the most collections

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5 - Which authors/publications generated the most revenue?

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.

Bar chart showing the authors that generated the most revenue

Below is the list of authors/publications with the most revenue:

6 - Which entries generated 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:

Bar chart showing the entries that generated the most revenue

7 - What was the networks/chains usage?

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.

Pie chart comparing the usage of the L2 chains

On week 17, Optimism dominates with 94.8% of network usage. In the second position, we have Zora with 1.8%. The third most used network is Polygon with 1.6%. Followed by Base with 0.9% and finally Linea with 0.9%.

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Accessing week 17 entries dataset

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.
revenue: collections times the price in USD.

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Verification

This entry has been permanently stored onchain and signed by its creator.

Arweave Transaction

N1JTkywVDuo6mMN…wuYm8xcaeBYMLoc

Author Address

0x29a283761a3a5e4…2a250e045973DA8

Content Digest

l14J7-HZr690fyi…FKf5VawdYEm1_Ic