Is it possible to use the posting activity of comments to isolate which accounts are bots?
My last post, in this deep dive into account activity, focused on Posting data. We got some insights into what posting activity looked like for the last 3 months of 2017, and we also got a feel for what the mix of automated posting and manual posting was.
There were less automated posts than I was expecting but I am sure this is something we will see grow over time and is one to watch. I suspected that most of the automated posts are being generated on comments so today I will take a look at the breakdown of these.
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In this post I am going to focus my analysis on Comments (excluding Posts) to see what trends emerge.
If we slice the data by month and look at the distributions of posting activity by account we get the following boxplot.
So how many times did the most active accounts comment?
This graph shows the distribution of number of posts from the 100 most active individual accounts over this period.
If there is one, what is the number of comments a day that we could use to identify automated accounts?
I would propose anything higher than 15 comments per day (on average) uses some sort of automation. This is almost twice the rate that I posted at during the period so it's an indicator for some sort of automated commenting. This number may not be correct but it's a starting point for our analysis.
Using this criteria (15 comments per day on average) I next look at the splits of posts per day between automated and manual. Automated Comments have posted more than 450 times in a month. This produces a list of 1,175 accounts that regularly post more than 15 times a day.
This next series of graphs shows the split of number of comments per day based on this criteria to identify manual and automated comments.
The last graph shows the total post count and the visual the split but we can also plot the individual components separately to identify trends.
With this series I am analysing trends in the Steemit Account Activity to see what the most useful metrics are for identifying growth and activity on the platform. I have come across some interesting trends in the data which I hope to analyse regularly and which the community may find useful. There are a few more items I will look at in the coming days related to accounts but please let me know if there is anything in particular you would like to see. Thanks for reading.
I am taking a deep dive into the Accounts of Users in this series of posts. You may also be interest in:
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