Posts are the bread and butter here in Steemit. Without content, there will be nothing that people can upvote and curate. Without posts, there will be nothing to attract search engines and generate organic sign-ups.
As my first post to analyze Steemit data, I would like to trim it down to the community I am most active in - the Philippine community.
My Approach
- Since this is a first, I'd like to make it simple and easy - I just want to be familiar with the data available and learn how to extract and present those data.
- I'm only interested in top-most posts; not comments or replies under the post.
- The thing that I'm curious right now is the trend for the number of posts.
- I also want to know who the top posters in our community are.
With these premises down, I present to you my data and my analysis.
1. From 2016 to 2017, what has been the trend of posts?
Let's look at the data.
As we can see here, from 2016 to 2017, there was an increase in the number of posts in May 2017, with posts peaking in July with 513,409 then slightly decreased on August with 499,739.
For September, we may see a slight decline in the number of posts considering that we are already in the middle of the month and there are 252,388. Multiplying the number by two (which is the number of weeks left in September) will probably give us around 500,000 posts for September.
2. What's the trend for Philippine posts?
Let's look now for the posts that contains the #philippines or #pilipinas tag.
We can see that the number of posts peaked last month with 3,810, up from 2,445 last July.
For September, there's a high probability that we'll exceed the posts for August since we already have 2,982 posts and we're just in the middle of the month.
Now that we've got that down, let's now look into the individuals or contributors of the said posts.
3. Who are the top contributors?
The persons who lead the pack are @allmonitors, @unhorsepower777, and @albertvhons with 360, 304, and 252 posts respectively.
Looking at the table below, here are the top 20 contributors divided by year.
| Author | 2016 | 2017 | Total | |
|---|---|---|---|---|
| 1 | @allmonitors | 25 | 335 | 360 |
| 2 | @unhorsepower777 | 6 | 298 | 304 |
| 3 | @albertvhons | 0 | 252 | 252 |
| 4 | @iamkunaning | 0 | 231 | 231 |
| 5 | @koshin | 0 | 178 | 178 |
| 6 | @juvyjabian | 57 | 104 | 161 |
| 7 | @immarojas | 22 | 129 | 151 |
| 8 | @lapilipinas | 105 | 40 | 145 |
| 9 | @crowe | 1 | 139 | 140 |
| 10 | @zararina | 0 | 127 | 127 |
| 11 | @jamiz | 0 | 122 | 122 |
| 12 | @yehey | 0 | 112 | 112 |
| 13 | @marylizacaindoy | 3 | 106 | 109 |
| 14 | @gwapology | 0 | 109 | 109 |
| 15 | @ashlyncurvey | 0 | 103 | 103 |
| 16 | @mers | 0 | 101 | 101 |
| 17 | @junvebbei | 0 | 100 | 100 |
| 18 | @lgfurmanczyk | 0 | 91 | 91 |
| 19 | @steemitph | 0 | 83 | 83 |
| 20 | @joshvel | 0 | 80 | 80 |
Conclusion
Steemit has a lot to offer. It's just a matter of finding and joining the right people. It's important to be with people who share your interest. And that's what @paulag did for me.
I used Microsoft SQL Server Management Studio to connect to steemsql and then used Microsoft Excel for the analysis.
I am part of a Steemit Business Intelligence community. We all post under the tag #BIsteemit. If you have an analysis you would like carried out on Steemit data, please do contact me or any of the #bisteemit team and we will do our best to help you...
You can find #bisteemit on discord - https://discordapp.com/invite/JN7Yv7j
Credits
Credits to @arcange for his support. All of the data taken in this report was from the superb steemsql managed by the same.
I also want to thank @stellabelle for mentioning @paulag in one of her posts and to the person who re-steemed that post.