There are many categories available on Steemit under which you can publish a post. Consistently in the top 10 categories is STEEMIT. Being such a popular category to post in, I felt it was important to document the data for this category in 2017.
Also when I started on Steemit in July, this was a very hot tag. I observed at the time that most of the posts on Trending were from the tag Steemit. What I did not realise at the time was the peak in new users the same time as I joined. It would make sense so that many people were looking to learn about Steemit and hence the popularity of the tag at the time.
Categories are set by the 'Tags' you select when publishing a post. The first 'Tag' that you select is the Category the post will be recorded in. Once a post has been published on the Steemit platform, tags can be changed but the category will no change.
The aim of this analysis is to
Get an overview and document of the posts made to STEEMIT in 2017
Look at the growth of this category over 2017
Look at the earnings of this category during 2017
Look at top authors of this category
Establish a trend based on previous 2017 tag analysis for the months of May and June for average earnings per Author. Investigate reasons for this trend.
The data and the query
I have connected to the Steemsql database held and managed by 
The SQL query used for this exploration is
Select *
FROM comments (NOLOCK)
where
( created >= CONVERT(datetime,'01/01/2017')
AND created< CONVERT(datetime,'01/01/2018')) and
Depth = 0
And category in ('steemit')
This query will pull all of the Posts made during 2017 in the category steemit. It will ignore any comments and will also ignore tags. Just to point out, the first tag that is selected on a post is the category in which a post is recorded.
After this I carried out further transformations on the data using M. First with this piece of code I removed any columns I did not want
= Table.SelectColumns(Source,{"author", "title", "body", "created", "children", "total_payout_value",
"net_votes", "pending_payout_value", "total_pending_payout_value"})
Then with this piece of code I changed the data type on the date field
= Table.TransformColumnTypes(#"Removed Other Columns",{{"created", type date}})
After this I loaded the data into the model and proceeded to model the data using DAX calculations.
The Analysis
In 2017 13KK posts were created in the STEEMIT category by 23.80K authors. These posts netted 2982K votes and 927K comments and also generated a total SBD pay-out value of 720.89K.
The average number of monthly posts to Life is 10,248 and the median is 10,979. From May to July there was a steady increase in the number of posts per month. This then declined slightly in September and October, increasing again in Dec. This trend is in line with previous tag analysis carried out for 2017
The visualisation above look at the SBD pay-outs. The bar chart shows the pay-outs over time. We can also see that 26% of posts earn between $1 and $50. Less than 3% of posts earned over $50. The balance of posts earned less than $1. This is also consistent with other 2017 tag analysis carried out. The table on the right shows the top preforming posts in STEEMIT for 2017.
Let’s take a look at some averages
23.8K authors produced on average 5.17 posts each to the STEEMIT category. The total of these 5.17 posts earned SBD 30.28 with the average post earning 5.86 SBD. This is higher than the reported averages on the categories ‘Blog’ ‘Life’ and ‘Cryptocurrency’. On average each post received 7.54 comments and 24.25 votes. The average vote value on posts in the STEEMIT category was 0.24 SBD
For comparison the averages on the BLOG category are shown below
We can also view these averages over the year of 2017, we can see some peaks in payments from May to June with a decline till December where we can see another rise. What is disappointing to see is the average number of comment per post declining, however this trend seems to be on all the tags analysed so far.
If we view the same information over months instead of days
We can see clearer now that the average SBD per post peaked in May and June, and then decreased again until another rise in December. It is also clear to see that the average number of comments left on a post also peaked in May and June and decreased from then with a small increase again in December.
It seems from this an previous analysis that May and June of 2017 have substantially larger earning. In May we seen an increase in the price of steemit from as low as $.15 in April to $1.42 in May. The price increased again in June to $2.80. The price then fell in July and remained bearish till December. This seems to be what is reflected in the charts above.
This leads to a question on December, as the price increased to over $7. So why is the average payment peak higher than that in May and June?
Looking at the chart below I have added the average number of votes per post. We can see that May has almost twice the average number of votes per post than Dec, but July and December are relatively consistent. There has to be another factor involved.
Further research brought me to Hardfork 19 which happened in June, and this changed how rewards and voting power were calculated.
Having a look at the authors that posted to the STEEMIT category, first I have sorted the data by the average number of comments per post. Comments are an awesome reflection on engagement within the post.
I then sorted the data by the average number of votes per post
And finally I have sorted the data by the total SBD pay out value on posts to cryptocurrency by author
The following cluster chart shows the Number of comments against the Post SBD pay out.
Conclusion
We can see clearly that posts on average in the STEEMIT category tend to do better in terms of votes, payouts and comments than posts made in all the categories reported so far. However the overall trends remain the same.
It would be interesting to see, had hardfork 19 not been implemented how the reward pool would be with the price of STEEM at all-time highs.
You can find a full analysis of the LIFE category here
https://utopian.io/utopian-io/@paulag/steemit-2017-round-up-analysing-the-life-category
You can find a full analysis of the BLOG category here
https://utopian.io/utopian-io/@paulag/steemit-2017-round-up-analyzing-the-blog-category
You can find a full analysis of the cryptocurrency category here
https://steemit.com/utopian-io/@paulag/steemit-2017-round-up-analyzing-the-cryptocurrency-tag
On saying that, this analysis does not take into consideration the quality of the post, however I do class comments as a good indicator of engagement. I am hopeful that the more quality the post, the more comments.
I am part of a Blockchain Business Intelligence community. We all post under the tag #BlockchainBI. If you have an analysis you would like carried out on Steemit or Blockchain data, please do contact me or any of the#BlockchainBI team and we will do our best to help you...
You can find #BlockchainBI on discord https://discordapp.com/invite/JN7Yv7j
Posted on Utopian.io - Rewarding Open Source Contributors