Repository
Introduction
This analysis seeks to look at activities from the steemhunt application. Activities like posts, comments and voting pattern.
Outline
- Scope
- Results
- Conclusion
- Tools and Scripts
- Relevant Links and Resources
Scope
Date of the analysis: 26th October 2018
Timeframe of the analyzed data: January to September 2018
Results
Steemhunt is one of the opensource applications built on the steem blockchain. Over the last few months, I have been monitoring its progress both manually and through the database.
Steemhunt is an application where users comb and search through the internet for innovative and life-changing application and technological gadgets. Software that can solve problems and make life sweet and easy. Steemhunt is built in such a way that it promotes activities via the application.
In order to earn rewards for recovery, post, vote and comments must be made via the application. Over the last few months Steemhunt has received a lot of support and delegation, even from our very own @utopian-io. This is a contributing factor in attracting users and increasing the number of people using the application.
POSTS VS COMMENTS RATIO
One of the ways to check the effectiveness of an application that I have come to discover is how often it is used to make posts. It is very common to see people make a lot of post via a particular application but rarely use it in making comments.
As I mentioned before, on Steemhunt in order to be rewarded for post or discoveries, the post, comments and upvotes have to be done via steemhunt application in order to earn points.
- Between February and September 2018, there were a total of 32,128 posts made via Steemhunt. An average of 4,016 posts per month.
- These posts attracted a total of 230,582 comments. Out of which 153,007 were made using the Steemhunt application, giving the application a penetration level of 66.3%. The remaining 33.7% of comments were made using other applications. This shows a low level of possible comments made via spamming bots.
IS THERE A RELATIONSHIP BETWEEN THE NUMBER OF VOTES AND COMMENTS TO THE PAYOUT AMOUNT?
HOW CONSISTENT ARE USERS IN USING THE APPLICATION
This section checkes the weekly consistency of users. It checks if a user has used the application to make a post or comment at least once a week. February was excluded because there was no serious activity in that month. Between March and September, there are 28 weeks and only a handful have been consistent.
- A total of 3,679 unique authors have made a post between March and September 2018
- 41.23% of users have only posted for a single week.
- 0.68% of authors have a made a post for half of that time (14 weeks).
- @steemhunt, @urbangladiator, @teamhumble and @itsravi are the users with the highest consistency of 27 weeks.
STEEMHUNT OFFICIAL ACCOUNT VOTING PATTERN
- 1920 unique authors received an upvote from @steemhunt with @teamhumble receiving the highest number of upvotes with 5477
- Steemhunt has voted a total of 98,127 with an average of 516 votes a day.
- Steemhunt votes with an average voting weight of 1.73%
- 93.40% of votes were made between 15:00 hours and 17:00 hours UTC
Conclusion
Steemhunt has carved a niche for itself on the steem blockchain. Through its hunting for reward activities, many applications and gadgets have been discovered. The mission is well on track, with moderators checking for cheat and abuse. The platform has been running smoothly in the last few months, further encouraged by delegation and support it is receiving from users.
Tools and Scripts
Microsoft SQL Server Management Studio 17 was used to access the data from STEEMSQL ( a publicly managed database by @arcange )
Microsoft Excel used to plot graphs and Charts.
SCRIPTS
FOR VOTES
SELECT
voter,
author,
weight,
timestamp
FROM Txvotes (NOLOCK)
WHERE [voter] in ('steemhunt')
AND CONVERT(DATE,timestamp) BETWEEN '2018-01-01' AND '2018-09-30'
SELECT
author,
total_payout_value,
net_votes,
created,
children,
category,
depth
FROM comments (NOLOCK)
where CONVERT(DATE,created) BETWEEN '2018-01-01' AND '2018-09-30' AND
IIF(isjson(comments.json_metadata) = 1, IIF(CHARINDEX('/', json_value(comments.json_metadata, '$.app')) > 0, SUBSTRING(json_value(comments.json_metadata, '$.app'), 1, CHARINDEX('/', json_value(comments.json_metadata, '$.app'))-1),json_value(comments.json_metadata, '$.app')), null) = 'steemhunt'