The goal is to collect baseline stats on stories from Medium Stats in order to get a better understanding of how readers engage with a writers work. Note that this is a personal project and is in no way associated with Medium. In order to best utilize this repo, follow the following directives depending on your goals. If the Jupyter notebooks give you trouble rendering, just copy/paste the url into nbviewer and it should work.
I wrote a Medium post as well. You can find the post and more about metrics on Medium here:
Deconstructing Metrics on Medium
- Download and run
scrape_medium_stats.py
after replacing theUSER
andPASS
variables with your Google login (your Medium account must be linked with Google) - If you want to tweak things or alter the code for alternate logins (Facebook, Twitter, etc.), then walk through the
Medium Stats Data Collection
jupyter notebook - Once you've collected your data, it will be placed in a file named
mystats.csv
(similar to mine in this repo).
- Check out my analysis in
Medium Stats Data Analysis
or perform your own! - An example of my data is available in
mystats.csv
as a starter dataset, feel free to share and explore.
It's worth noting that this type of analysis should be available to all writers, not just those that are data science practioners. Data analysis should be democratized for writers and content creators. Even with my ~30 post sample size, I was able to walk away with some interesting insights:
- 2 out of my 30+ posts make up over 70% of my total lifetime views
Read Ratio
andRead Time
appear to be strongly correlated- Publication choice matters for engagement stats like
Fan Ratio
Read Ratio
andFan Ratio
correlated - strong posts do both well
This analysis is just from using the fraction of data available to users on the Medium Stats page. Imagine if we had more of our data and information at our disposal; if writers were empowered to use Medium Stats for improving their work and understanding how readers perceive it rather than boosting their ego with simple vanity stats. It's no small task, but I believe it can be done.