After having a look at the Vest Power Distribution among users; I got curious about the how this power is being utilised among the groups. Steemit is after all, a gift economy where one votes someone and gets some benefit in return.
Tools Used:
I am pulling data from the ever reliable steemsql.com. I have connected Google sheets to it for pulling up the data I need.
The Method:
The tables involved are TxVotes, Accounts. Accounts table is used find the user's class based on vesting power. The vote weight and count comes from the TxVotes table.
A quick rehash of the user classification based on Vesting Power:
| Vesting Balance | Coinage |
|---|---|
| Above 1000 MVests | Whale |
| Above 100 MVests | Orca |
| Above 10 MVests | Dolphin |
| Above 1 MVest | Minnow |
| Below 1 MVest | Plankton |
Since we are looking at how the vote power is being used, both upvotes and downvotes come into play. Upvotes are the ones that have positive weight in the TxVotes table and downvotes are the ones in negative. I have converted the results into pivot tables so that it is easy on the eyes.
Results
I had run these queries last week before I went off the grid for a few days; so the counts may not be exact but the results are pretty much the same. The rows are Voters and the Columns represent the Authors/Curators for all these tables.
Upvotes
The first query involved getting the count of upvotes among different user categories:
Next step was to find what percent of votes are being shared between these categories:
- The last column shows the percentage of votes made by the user category based on the total votes in the system.
- The last row shows the percentage of votes received by the user category based on the total votes in the system.
- It is interesting to note that majority of votes by the Orcas and Whales always goes to other categories.
- The reverse is not true though but it is premature to draw a conclusion yet.
- Planktons get fewer votes than they give.
- Orcas, Dolphins and Minnows receive more votes than they give with Dolphins being particularly lucky.
- Dolphins contribute only 13% to the overal voting but eat away 22% of the votes.
Looking at the vote counts may be misleading since voting weight also determines the payout of a vote.
So here is the spread of voting weight among the different categories:
- When it comes to votes among themselves Whales and Orcas vote wholeheartedly.
- Others don't use their full power to vote among themselves.
- Minnows in particular give only 71% upvote power to their own class votes.
- Incoming votes to Planktons come at 50% power making the voter half effective.
- Notice that the Whales always garner votes with 100% power but while giving votes outside their category they tend to get thrifty with the vote percentage. In fact on average; Whales vote with only 50% power as a whole.
Downvotes
Next, let us look at downvotes or flagging. Downvotes comprise of about 1% of the total votes cast in the system.
So how are these users flagging among themselves and on others?
Let us check the counts first:
Counts are not revealing much information as it is raw. Let us make them as percentages:
- The last column shows the percentage of downvotes made by the user category based on the total votes in the system.
- The last row shows the percentage of downvotes received by the user category based on the total votes in the system.
- Flagging amongst their own kind - Whales, Orcas and Minnows are very hesitant when it comes to flagging one of their own but Dolphin target 10% of their flags among themselves.
- It is a bloodshed among Planktons with flagging coming in at 60% of the total flags by Planktons.
- Another interesting point is that the Whales, Orcas, Dolphins and Minnows do not downvote much among themselves but when a Plankton is at the receiving end then these classes get trigger happy giving around 75% of their flags upon them.
- Whales, Minnows and Planktons give more flags to others than the ones they receive.
- Dolphins receive unusually high downvotes than the ones they give.
The flag percentages are there but at what cost do they come?
Let us see the voting weight percentage involved in the flaggings:
- In a Whale-Whale or Orca-Orca flagging war they always give their 100% power.
- Others tend to have lesser weight when flagging their own.
- Whales and Orcas get flagged with more weightage than when they flag others.
- The opposite is true for Minnows and Planktons.
Take Aways
Since a majority of users are in the lowermost level, let us see what we can infer from these numbers.
- The spread and weightage charts looks like the top users do behave according to the Elite Theory; hesitating in giving quality benefits to lower levels even if it does not cost a dime.
- The Planktons do get a lion's share of the upvotes from others but when you look at the voting weightage you can see that it comes at a huge discount than to other levels making those votes less effective.
- Things can get better if this voting weight improves. Or in other words if the quality of the vote increases rather than the quantity then the lower level users may benefit more from this system.
- The lower level users should be more considerate in giving weightage to the upper levels particularly the Planktons. Planktons have the numbers so a lesser weight will definitely affect others' rewards.
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dbdecoy
Good analysis, well done.
Couple of comments about the data. The vests in Accounts table doesn't account for delegations. There are some high SP users who delegate away their power while other minnows might be leasing a lot of SP that would push them up classes. Also, some users vote a lot more at 100% and have a degraded Vote Power which will skew your results too. Then there are the bots too like randowhale and minnowbooster that show up everywhere and would ideally be excluded from the data.
Overall though, really good analytics.
Yes, you are right about delegations. I was looking only at how the groups vote each other based on accounts table. So, even if power is delegated for a user it expires, the user is just a boosted up entity, it's relationship with others might not really change. So delegations do not really matter here.
Bots - I have already said that I don't endorse a manual way of excluding them as it may result in showing bad data.
In the week from Sept 17 to 24 the major bots have the following votes :-
minnowsupport - 5638
minnowbooster - 4120
randowhale - 2735
booster - 1560
bellyrub - 1321
That's a total of 15374 votes and they would almost entirely manifest in your statistic of whales voting for minnows or plankton at low percentages.
I get it and I know it. I am trying to present the data as a whole, not doing sampling. So if you have a way to identify the bots programmatically then please do share as it will be useful for a lot of us.
They are different types of bots with different voting behaviours so it is tricky to identify them programatically. I personally have been been building a register of them with some vital stats so I would use a manual blacklist to exclude them from the data query myself, but if I come up with a better idea I'll let you know.
Keep up the good work.
Thanks for the heads-up. One of the major pitfalls in any data analysis is the manual exclusion part. Once we start doing that cleanup we tend to get more biased on the data to be excluded and the final result may get screwed up. So if we cannot exclude programmatically, it is better to leave it as is. Let me know if you get any breakthrough in finding the bots. Eagerly Waiting!
Hey @dbdecoy,
Its me again
here is the link to my 6th curation post where I mentioned your post. Hope you will like it :D
This post received a 2.62% upvote from @randowhale thanks to @dbdecoy! To learn more, check out @randowhale 101 - Everything You Need to Know!
Hi @dbdecoy,
This is an amazing and informative analysis.
One thing I didn't understand from your take away message though
What do you mean by this?
Also, I will be adding your post to my 6th curation episode. It is not posted yet, but will post the address for you when I am done. I will be happy if you would check it out and let me know what you think.
Many thanks for sharing :D
Lower level users are probably unaware of adjusting their vote percentage and always give 100% to everyone. If they get accustomed to being more prudent in the voting percentage, the higher level users may receive lesser rewards. Lower level users have the power of numbers so larger the users who vote with lesser power the higher the number of votes need by higher level members for better rewards.
Thank you for your reply @dbdecoy,
This is very interesting. But As far as I know, lower SP users can't adjust their vote percentage, unless they have 500SP at least I think. Or is there another way to do it?
These are great results. can you make graphs out of them. They will be easier to understand even :D
I was able to adjust my power through @esteemapp right from the beginning. Did not check my SP at that point. Graphs - I will try to do that.
Thanks for your reply and the info @dbdecoy
I look forward to reading more of your posts :D
@dbdecoy got you a $7.9 @minnowbooster upgoat, nice! (Image: pixabay.com)
Want a boost? Click here to read more!
Interesting data as always. Thanks.
Thanks, First to comment as always!
Wow, this is amazing analysis! The first graph looks great - it looks like powerful users are using a lot of their votes to support newer and less established users. But the second one, showing the differential in voting power weight, is interesting...
Perhaps the most established users get more 100% upvotes because they are the ones who create the most in-depth content? There are a lot of planktons and minnows that, frankly, make sub-par content. They are trying, and I support them with smaller upvotes, but I can only do so much for some of those posts.
On the other hand it could be a sign of nepotism. Hard for me to say.
As spidey knows "With more power comes responsibility"; but the responsibility does not reflect when voting peers or those whose are above oneself.
awesome post. I dont think everyone here on steemit appreciates the time and effort that go into these analysis. Good job.
and the winner of this weeks steemit business intelligence contest
https://steemit.com/bisteemit/@paulag/business-intelligence-steemit-weekly-contest-3-and-winner-week-2
Thanks @paulag
excellent and informative post my friend! I look forward to more of this kind of analysis, it certainly contains some eye-opening discoveries! Keep up the good work ;) Basil x
Thank you for your support!
This is very insightful!
Thanks for the resteem!
Very interesting. I look the writer did very hard work to collect this much of data. It is really appreciatable. Thanks to making me more aware about the steemit .
Really good analysis.
Does this presuppose that the groups post content with equal frequency?
Post frequency not taken into account.