Overview

Flipside is a platform that let users to get paid to learn about protocols and chains, and level up to become a crypto-SQL superstar. There, users can do several specific tasks (called bounties) and be paid in tokens, while learn about different projects like Sushiswap, Polygon, Thorchain, Solana, Algorand among others.

However, there are some users trying to "farm" Flipside rewards. But, are we able to find these users?

Methods

The idea is to look for participants that collect multiple times for the same bounty or hunt and then transfer all of their token incentives to the same address. For this analysis, only Ethereum and Solana chain bounties and hunts have been taken into account from 2022.

Inclusion criteria employed:

• Destination wallets with no more than 100k transactions received to avoid some pools, platforms, etc.
• More than 5 analysts sending to the same destination wallet
• Average transfers per analyst higher than 1 in the same destination wallet
• Number of same bounties received by analysts from Flipside higher than 3
• Total received by destination wallet **<**average same bounties * analysts involved * max amount received from Flipside

Ethereum chain

Looking at the Ethereum chain, we have found more than 30 possible bad actors. In the analysis, we can see the bad actors involved and the average of duplicated bounty by each destination wallet as well as the total amount (USD) received and the average transfers done per bad actior by each wallet destination.

In the table, we can find the following metrics:

• the number of other possible wallets involved
• the common destination wallet
• the average same bounties
• the number of transfers
• the total amount
• last tx date

Conclusions

• As it can be seen in the first chart, we can see how there were 11 destination wallets involved where the major of the potential bad actors send their benefits. We can see ho the major of them have between 6 and 20 wallets that are sending tokens to them but there are one with more than 70.
• There are two others with more than 4k USD in benefits.
• For two of the destination wallets, there are one basic account sending the major of the capital, but for the rest are more heterogeneous divided.
• If we bucked the potential bad actors per the amount farmed, we can see how the major of them are between 25 and 100 USD farmed (44.2%), but more than 30% of them farmed between 100 and 500 USD. Less than 4% farmed more than 1k USD, representing 2 accounts.
• The accounts that farmed more volume, have also done more bounties and have done more transfers in average.
• The major of the volume is being farmed by the first 3 groups, having similar volume at around 3.5k USD in total.

As it can be seen in the first chart, we can see how there were 11 destination wallets involved where the major of the potential bad actors send their benefits. We can see ho the major of them have between 6 and 20 wallets that are sending tokens to them but there are one with more than 70.

If we extract all the benefits sent in USD, we can see how even having the major of the accounts involved, it is not the wallet destination with the major benefits. There are two others with more than 4k USD in benefits.

In this chart on the left, we can see the amount sent per wallet on each destination. We can see how for two of them there are one basic account sending the major of the capital, but for the rest are more heterogeneous divided.

If we bucked the potential bad actors per the amount farmed, we can see how the major of them are between 25 and 100 USD farmed (44.2%), but more than 30% of them farmed between 100 and 500 USD. Less than 4% farmed more than 1k USD, representing 2 accounts.

Looking at the second image, we can clearly see that the accounts that farmed more volume, have also done more bounties and have done more transfers in average.

Finally, looking at the total volume farmed per each group, we can see that the major of the volume is being farmed by the first 3 groups, having similar volume at around 3.5k USD in total.