After having a look at the [Vest Power Distribution](https://steemit.com/steemit/@dbdecoy/steemit-vest-power-population-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:
![Upvote_total.png](https://s26.postimg.org/s5ofjq521/Upvote_total.png)

Next step was to find what percent of votes are being shared between these categories:
![Upvote_spread.png](https://s26.postimg.org/q2e0c2595/Upvote_spread.png)

* 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:
![Upvote_wt_spread.png](https://s26.postimg.org/hzfu0qknt/Upvote_wt_spread.png)

* 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:
![Dnvote_total.png](https://s26.postimg.org/ufcjuhdzt/Dnvote_total.png)

Counts are not revealing much information as it is raw. Let us make them as percentages:
![Dnvote_spread.png](https://s26.postimg.org/5orgueo0p/Dnvote_spread.png)

* 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:
![Dnvote_wt_spread.png](https://s26.postimg.org/939najjfd/Dnvote_wt_spread.png)

* 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.

___Interested in helping the community clean up the spam content in Steemit?___
You can check the new daily reports on [Comment spammers](https://steemit.com/steemit/@dbdecoy/steemit-comment-spam-report-20-09-2017) and [Introduce tag spammers](https://steemit.com/steemit/@dbdecoy/steemit-intro-tag-abusers-daily-report-20-09-2017) on [my page](https://steemit.com/@dbdecoy). You can warn the offenders or flag them or report them to steemcleaners.

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___dbdecoy___

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