Analysis of Voting Habits of steemit accounts with highest amount of Delegated SP- Steemit Data Challenge

Hey Steemians,

I have just recently come across a community of data analysts,data scientists and data enthusiasts; #Bisteemit,this is a community I am really excited to identify with. I am really passionate about all things data analysis even though my current data skills are kind of elementary. In steemit with the help of these kind of communities, I see an opportunity to develop my skills whilst enjoying being part of a community where there is abundance of love,appreciation and self fulfillment and yeah maybe I could make some steem too hehe.

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Steemit Data Challenge

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This post is my submission to the first steemit Data Challenge. An analysis was done by @paulag on Delegation of Sp. In her post she higlighted key stats about delegation,like accounts that have delegated the most amount of SP,Steemit accounts with highest amount of sp delegated to them and Steemit accounts with the highest count of steemit accounts(delegatee) delegating SP to them. You can check out the post here
Now to the challenge, the challenge as I understand it is to analyse voting habits of accounts with highest amount of delegated SP. This is visualization by @paulag showing accounts with highest number of delegated SP as at 14th of August 2017 when she made this post

For this analysis, I am going to focus on the top 15 steemit accounts in the visualization. @booster and @randowhale are upvote bots that can be used to boost posts, I use these services a couple of times myself.

Top 15 Steemit Accounts with Delegated SP
@kpine
@booster
@htliao
@linuslee0216
@nicolemoker
@surpassinggoogle
@sweetsssj
@tumutanzi
@ausbitbank
@canadian-coconut
@good-karma
@teamsteem
@randowhale
@ats-david
@theprophet0

To get the votes data,I needed to acess the steemit sql database maintained by @arcange and I used his tutorial here to try to get the data into Excel,this was not straight forward hehe,spent like 3 hours just to get the right SQL query to import voting data for only these 15 steemit accounts but it is all worth it for me in the end.I was able to import data from 1st August 2017 00:00 UTC to 18th August 2017 12:31 UTC

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Most Active Voters

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Steemit ID	Number of Votes
@good-karma	20180
@randowhale	10836
@booster	4134
@teamsteem	2498
@sweetsssj	1523
@canadian-coconut	1481
@htliao	1371
@ausbitbank	1219
@surpassinggoogle	1051
@linuslee0216	842
@ats-david	813
@kpine	795
@tumutanzi	685
@nicolemoker	553
@theprophet0	73
@Grand Total	48054

There was a total of 48054 votes(includes upvotes and downvotes) cast by these steemit accounts during this period and you can see from the table @good-karma had the most upvotes by far. I am aware there are a lot of services for automating upvotes,I cannot think of anyway right now to distinguish between an auto vote and a manual vote.See visualization of number of votes cast below:

Hard for me not to notice @theprophet0 had the fewest number of votes during this period, wasup man!

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Vote Weight

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So these guys have a lot of SP but what is the average vote weight they apply when giving an Upvote, I mean a whales upvote wont mean much if it is done at 0.1% vote weight hehe. Please see the table below :

Steemit ID	Average Vote Weight
@theprophet0	81.08%
@tumutanzi	36.55%
@kpine	33.54%
@nicolemoker	29.84%
@ats-david	24.94%
@htliao	24.43%
@linuslee0216	23.90%
@ausbitbank	15.02%
@surpassinggoogle	14.68%
@canadian-coconut	12.93%
@sweetsssj	12.50%
@teamsteem	7.81%
@booster	3.57%
@randowhale	3.50%
@good-karma	0.54%

I can probably see why @theprophet0 has very few votes compared to others. His average vote weight is 81.08% which implies he goes all in when he spots a post he likes

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Dont mess with these guys

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With great power comes great responsibility. Accounts with a large amount of SP are custodians of this platform because any post they flag might be sent into oblivion, of course this is power that should not be abused

Steemit Account	Number of Votes	Number of Downvotes	%
@ats-david	813	125	15.38%
@canadian-coconut	1481	70	4.73%
@ausbitbank	1219	28	2.30%
@teamsteem	2498	36	1.44%
@htliao	1371	12	0.88%
@tumutanzi	685	2	0.29%
@booster	4134	6	0.15%
@good-karma	20180	8	0.04%
@randowhale	10836	0	0.00%
@sweetsssj	1523	0	0.00%
@surpassinggoogle	1051	0	0.00%
@linuslee0216	842	0	0.00%
@kpine	795	0	0.00%
@nicolemoker	553	0	0.00%
@theprophet0	73	0	0.00%

You can see from the table @ats-david is most likely to downvote. Strange to see @booster did a couple of downvotes too.

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There are a dozen other analysis I could run on these accounts but will stop here, If there is any angle you would like me to look at, you can leave me comment.

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Thanks for reading my post.Let me know your view on this post in the comments, I read and appreciate every comment.Each post I make takes considerable time and effort,please support me by upvoting and resteeming if you enjoyed it.Keep an eye on my blog for my next post. You can also follow me @datageek

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