This is a time series analysis of the Steem ecosystem for Quarter 4 of 2017. Specifically this examines Steem block chain Posts and includes transactions committed from entry points such as Steemit, Utopian, Busy, Steepshot etc.
The Steemit social platform was launched on July 4th of 2016 as the first of these entry points; limiting the dataset to approximately 18 months or six quarters.
The data source for this analysis is Steemdata.com via the MongoDB API. For this analysis we're examining the Posts Collection and two specific objects, author and created (illustrated in the screenshot below).
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 1.0 | 1.0 | 4.0 | 19.98 | 12.0 | 18607.0 |
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 1.0 | 1.0 | 3.0 | 17.1 | 12.0 | 8842.0 |
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 1.0 | 1.0 | 4.0 | 13.81 | 11.0 | 2700.0 |
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. |
|---|---|---|---|---|---|
| 1.0 | 1.0 | 3.0 | 14.5 | 12.0 | 1351.0 |
| Steemian | Post Count | |
|---|---|---|
| 1 |
 @jack.the.ripper
|
18607 |
| 2 |
 @drumz
|
14887 |
| 3 |
 @manuel20
|
14524 |
| 4 |
 @cryptomonitor
|
13878 |
| 5 |
 @ryox
|
13829 |
| 6 |
 @monitorcap
|
11310 |
| 7 |
 @trafficmonitor
|
10923 |
| 8 |
 @holunder
|
10857 |
| 9 |
 @markboss
|
10738 |
| 10 |
 @vertical
|
8813 |
@jack.the.ripper. Others like @vertical at Number 10 have made it onto this elite list in less than 60 days of joining. All appear to be getting widely "followed" but are themselves following few if any other users.
Comparing this Q4 list of elite user accounts to previous three quarters, we see significant growth in Post volume. Top performers from previous quarters appear to be getting crowded out by a new breed of very high frequency robot accounts.
Robots are clearly present in prior quarters but exhibit more modest posting rates.
Top poster in Q1 (
These relatively few but highly prodigious robot accounts have confounding effects when examining the activity of human users. The affect is illustrated by the plot below. The excessively long-tail makes the average or typical activity almost indistinguishable. The mean being indicated by the red dotted line.
The plot does however serve to emphasise the scale to which bot activity is crowding out human contributers.
We observe in these four Probability Densitiy Distribution charts above, growth in the mean number of posts by ~5% over the year. From the summaries above this is likely a consequence of robot activity influencing the overall population mean.
We also observe marginal growth in the number of accounts posting twice per quarter. The declining tail of the chart highlights the relatively few humans able to sustain high frequency posting.
We can also consider Posting patterns by Week of the Year and from the two charts below we observe sustained year on year growth in Post volume (note Y-axis scales).
With the relatively limited historical data (~18 months) no obvious seasonable affects can be adjusted for and these charts serve to illustrate only the sustained growth over the period.
Posting patterns by day of the week indicate mid week remains the most popular time to Post.
However, the relatively lower weekend traffic may be a consequence of of robot-maintenance being performed over the weekends; suspending their high frequency posting.
These four charts indicate post volume by hour of day, unadjusted for time zones.
We observe to two important phenomena. The daily average (red dotted line) continues to rise and the early morning dip around 05:00am begins to flatten out. The trend being towards a flattening of the curve. This high frequency, around the clock posting pattern is again indicative of rising levels of robotic automation. Robots don't sleep.
Growth in user account and Post volume continue to accelerate. The two cannot be correlated due confounding mentioned above. Separating humans and robot accounts into separate chorts will be necessary to better understand how these two types of user are developing.
The long tail of the density distribution charts and the very high average daily posts in Q4 indicate automated robot accounts continue to proliferate. We can expect to see this cohort gainning increasing visibility and influence.
Users with high levels of technical automation skills will dominate the Hot, Trending and Promoted sections in an increasingly aggressive battle for Upvotes. This arms race shows no signs of peaking. Finding high quality human generated content will continue to be challenging.
If this trend continues we can expect to see Steem becoming a social platform for robots, with human users participating on the fringes; most likely in highly niche categories. Earnings will be dominated by those with the most robotic fire power (highly technically proficient users).
As automation becomes more prevalent and competition increases, we can expect to see more widespread use of automated down voting of competing robots. This tactic is likely to be employed in a manner simmilar to "hedging" a complex derivatives trade.
There appear to be many parallels between the growth of Steem and the early days of the electronic financial markets in the 1990s.
