Two days ago, I posted How Much Do Whales Influences on Rewards?. After posting it and having discussion with others, I found this study can provide more deeper understanding of curation patterns. As a follow-up study, this post is focusing on typology of curations: grouping curators by certain patterns.
Method
I used k-means clustering to classify the top 100 curators (by influence) into four groups. The sample's total influence is 99.5%, which means almost all of curation is done by the top 100 curators.
Data and Variables
The data is from Steem blockchain from 2016/8/16 to 2016/9/10.
- Influence: Percentage of payout a curator used
- Power: Percentage of Steem Power a curator owns
- IPR: Ratio between influence and power (Utilization of Steem Power)
- N: Number of votes a curator casted during the study period
- Range: Number of unique writers a curator voted for
- Mean: Average number of votes per writer
- Max: Maximum number of votes on a writer repeatedly
- StDev: Standard deviation among a curator's votes on each writer
The following table presents some descriptive statistics about the four groups.
| Group | Sum_Influence | Avr_Influence | Sum_Power | Avr_Power | Avr_IPR | Avr_N | Avr_Range | Avr_Mean | Avr_Max | Avr_StDev |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 15.86 | 0.51 | 30.33 | 0.98 | 0.52 | 120.51 | 123.71 | 0.97 | 5.97 | 0.90 |
| 2 | 46.99 | 1.20 | 18.11 | 0.46 | 2.59 | 582.55 | 329.72 | 1.77 | 24.09 | 2.65 |
| 3 | 31.83 | 1.38 | 10.82 | 0.47 | 2.94 | 802.70 | 260.96 | 3.08 | 61.93 | 6.58 |
| 4 | 5.27 | 0.75 | 1.35 | 0.19 | 3.90 | 1410.57 | 164.57 | 8.57 | 70.00 | 12.08 |
| Total | 99.95 | - | 60.61 | - | 1.65 | 547.91 | 238.48 | 2.51 | 30.39 | 3.67 |
1. Lazy Lions
Curator in this group show low utilization of Steem Power on average (0.52), but they still have a great potential to become influential curators since they have more power than the other groups (0.98). The list of curators is as follow.
2. Hidden Gem Explorers
This group consists of the biggest part of the total influence (46.99%) and they have the highest range and the second lowest Max and StDev. This implies that their votes are dispersed to various authors.
3. Village Builders
While this group has a significant influence on payout, they votes for small groups of writers repeatedly, probably due to build their own communities (they have very high Max and StDev). A good example is and
who support the Chinese community. They have relatively high possibility to be developed into their own DCGs suggested by
4. Superhumans
They are possibly bots. They have very high N on average (1410.57) and the highest IPR (3.90). They may have very high curation rewards profitability.
Conclusion
Curation is a very important task in Steemit. More understanding of curation will provide constructive foundations for further growth of Steemit.