Who Posts, Who Gets Supported, and Where Do the Rewards Go?

An analysis of main-post activity, community participation, Steem Curator and Booming support, and finalized rewards — August 8–21, 2026

Over the years, I have often wondered how activity on Steem is actually distributed.

I regularly see some of the same users publishing several posts per day, sometimes across multiple communities and almost every day. At times, it can look almost like a full-time job.

There is nothing inherently wrong with that. There is nothing wrong with being highly active or earning rewards on Steem.

The question I wanted to investigate was different:

How are activity, support and rewards actually distributed on Steem?

Rather than relying on impressions, I decided to look at the blockchain data.

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The Study

The analysis covers August 8–21, 2026, representing 14 complete days (UTC).

During this period I identified:

26,242 main posts

3,610 unique authors

Only root/main posts are included in the primary posting analysis. Comments and replies are examined separately later.

I looked at:

• posting frequency and active days
• activity across communities
• Steem Curator support
• Booming support
• finalized author rewards
• direct self-comments and self-voting

The period was selected far enough in the past for the normal seven-day payout window to have ended. Therefore, the reward analysis uses finalized blockchain author_reward data, not pending payout estimates.

STEEM and vesting rewards are kept separate. For readability, VESTS are also shown in parts of the analysis as Estimated SP. Where STEEM + Estimated SP are combined, this is only a comparison measure for studying reward distribution — not USD and not liquid STEEM.

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Curator and Booming Are Different

One distinction became particularly important during the investigation.

Steem Curator and Booming should not be treated as the same support system.

Steem Curator accounts are used by curation teams working with different themes and responsibilities.

Booming works differently because communities nominate posts for Booming support.

For that reason, I track Curator and Booming separately and use Combined Support only when asking whether a post received at least one of the two.

A post receiving both is counted only once in the Combined category.

This distinction becomes important when we later examine why the same authors sometimes receive repeated support.

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What the Data Cannot Tell Us

Blockchain data can show what happened.

It can show how often somebody posted, which communities they used, who voted, and what the finalized rewards were.

It cannot tell us why somebody did it.

Therefore:

High posting frequency is not automatically reward farming. Repeated support is not automatically abuse. Large rewards are not automatically evidence that anything improper occurred.

The purpose of this investigation is to identify measurable patterns, not to assign motives to individual users or communities.

Likewise, when I refer to the reward of a supported post, this means the total finalized author reward of a post that received a Curator and/or Booming vote. It does not mean that Curator or Booming supplied the entire reward.

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Data & Tools

The analysis is based on publicly available Steem blockchain data.

Part of the data was obtained and verified using SteemWorld.org / Steem Blockchain Data Services (SDS).

Special thanks to @steemchiller for developing and maintaining tools that make detailed analysis of the Steem blockchain possible.

SteemWorld.org — Built with ♥ by @steemchiller

With the methodology established, I started with the simplest question:

Who is actually producing Steem's 26,242 main posts?

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Who Is Posting?

During the 14-day period, 3,610 authors published 26,242 main posts.

But activity was far from evenly distributed.

Posts during 14 days	Authors
1	903
2–5	1,155
6–15	1,349
16–30	139
31–60	37
61+	27

More than 2,000 authors published five posts or fewer, while only 64 accounts published more than 30 posts.

So there is clearly a relatively small group operating at a much higher posting frequency.

But does that group dominate Steem?

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Posting Concentration

Ranking authors by number of main posts gives:

Most Active Authors	Posts	Share of All Posts
Top 10	1,648	6.28%
Top 20	2,533	9.65%
Top 50	4,112	15.67%
Top 100	5,607	21.37%

The Top 100 represented only 2.77% of all authors, yet produced 21.37% of the posts.

That is significant concentration, but it also means:

78.63% of all posts came from authors outside the Top 100.

So the data does not support the idea that a handful of accounts produce almost everything on Steem.

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The Most Active Accounts

Author	Posts	Active Days	Avg./Active Day
@web2.support	450	14	32.14
@steemburnup	168	14	12.00
@sweecee	151	11	13.73
@statsexpert	149	14	10.64
@haejin	143	13	11.00
@tworld	139	14	9.93
@abandi	112	14	8.00
@playpoker	112	14	8.00
@playhighcard	112	14	8.00
@playdice	112	14	8.00

Some of these are game, service, synchronization or system-related accounts rather than ordinary content publishers.

To avoid overstating their effect, I performed a secondary analysis excluding only 15 clearly identifiable system/game accounts.

Together they produced:

1,486 posts — 5.66% of all posts

After this conservative exclusion, 24,756 posts from 3,595 accounts remained.

Posting concentration then fell to:

Group	All Activity	After Exclusion
Top 10	6.28%	5.57%
Top 20	9.65%	7.90%
Top 50	15.67%	12.74%
Top 100	21.37%	18.18%

System/game activity therefore affects the headline numbers, but it does not explain the broader pattern of concentrated high-frequency posting.

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Sustained Activity

Frequency becomes more meaningful when we ask whether it continues day after day.

Across all authors:

• 366 were active on all 14 days
• 633 were active on at least 12 days
• 893 were active on at least 10 days

After the conservative system/game exclusion:

Daily Posting Level	At Least Once	10+ Days	All 14 Days
2+ posts/day	795	87	30
3+ posts/day	278	33	13
4+ posts/day	133	19	8
5+ posts/day	72	6	4

So there are authors maintaining several main posts per day for almost the entire period.

The highest number recorded for a single author in one calendar day was:

39 main posts

This shows that extremely high activity exists, but frequency alone tells us nothing about whether those posts receive support or meaningful rewards.

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Moving Between Communities

I also examined how authors moved between recorded Steem communities.

During the 14 days:

544 authors posted in at least two different communities

297 authors posted in multiple communities on the same day

There were 1,081 author/day instances where the same author published in more than one recorded community during a single day.

A technical limitation should be noted: not every post contained an identifiable hive-xxxxx community value, so a missing community value should not automatically be interpreted as posting outside a community.

Still, the overall pattern is clear:

Steem has a broad author base, but within it is a much smaller group publishing frequently, consistently and sometimes across several communities.

The next question is therefore more important than posting frequency itself:

Does this higher level of activity translate into more Steem Curator or Booming support?

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Steem Curator and Booming Support

Of the 26,242 main posts, the support distribution was:

Support	Posts	Share
Steem Curator	2,214	8.44%
Booming	1,750	6.67%
Both	188	0.72%
Combined	3,776	14.39%
Neither	22,466	85.61%

A total of 648 authors received Curator and/or Booming support on at least one main post.

The overlap between the systems was relatively small: 2,026 posts received Curator only, 1,562 Booming only, and just 188 received both.

So Curator and Booming clearly do not support exactly the same content.

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Does Posting More Produce More Support?

To test this, I defined a high-frequency group after the conservative system/game exclusion:

• active on at least 10 of the 14 days
• average of at least 3 main posts per active day

This produced 34 authors with 2,586 posts.

Their Combined Curator/Booming support rate was:

14.81%

For all other authors in the non-system dataset it was:

15.30%

This is an important result.

High posting frequency alone did not increase the probability of receiving Curator/Booming support.

The interesting pattern appears within a much smaller subset of highly active authors.

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High Activity Combined With Repeated Support

I then looked for authors who were active on at least 10 days, averaged at least three posts per active day, and received support on at least 10 posts.

Eight authors stood out with Combined support rates above 60%:

Author	Posts	Communities*	Curator	Booming	Combined	Rate	Support Days
@narocky71	45	6	20	23	38	84.4%	14/14
@ride1	45	5	6	31	37	82.2%	13/14
@jamal7	48	8	10	26	36	75.0%	14/14
@jannat7	46	8	14	19	33	71.7%	13/14
@bristy1	42	6	5	26	30	71.4%	13/14
@bijoy1	66	12	12	34	45	68.2%	14/14
@limon88	42	8	6	22	28	66.7%	14/14
@tasonya	74	7	18	31	49	66.2%	14/14

*Recorded hive-xxxxx communities.

For six of these eight authors, support occurred on every day of the 14-day period. For the other two, it occurred on 13 of 14 days.

This shows a persistent support pattern, but it does not by itself tell us why the support occurred.

For Booming in particular, the communities turned out to be very important.

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The Community Connection

Because communities nominate posts for Booming, I linked Booming-supported posts back to the communities where they were published.

One example illustrates the effect particularly well.

@jannat7 published:

23 posts in hive-174315 → 5 received Booming support

but:

11 posts in hive-113640 → all 11 received Booming support

The same author therefore had very different outcomes depending on the community.

Other examples in hive-113640 included:

• @narocky71: 14 of 14 posts received Booming
• @jamal7: 19 of 20
• @tasonya: 14 of 16

This suggests that community nomination channels are an important part of understanding repeated Booming support.

Rather than simply asking:

“Why does Booming keep supporting the same author?”

a better question is:

“How are Booming opportunities distributed within the communities?”

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Booming Distribution by Community

The differences between communities were substantial.

Community	Posts	Booming Posts	Recipients	Booming Rate	Top 5 Share
hive-188619	609	251	58	41.2%	21.5%
hive-113640	271	194	32	71.6%	42.8%
hive-180106	339	122	53	36.0%	34.4%
hive-109690	346	113	26	32.7%	46.9%
hive-179660	273	95	46	34.8%	33.7%
hive-109722	127	88	6	69.3%	88.6%
hive-141054	124	69	14	55.6%	82.6%
hive-174315	234	63	19	26.9%	61.9%

The contrast is clear.

In hive-188619, 58 authors received Booming support and the Top 5 accounted for only 21.5% of supported posts.

In hive-109722, only 6 authors received Booming support and the Top 5 accounted for 88.6%.

So communities appear to use their Booming opportunities very differently.

Some distribute them broadly, while others show much stronger concentration among recurring recipients.

This does not establish wrongdoing. The blockchain data records the resulting votes, not the complete decision-making process behind each community nomination.

It does, however, show that community-level distribution matters when evaluating Booming support.

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The Same Authors Across Communities

Some authors also appeared among significant Booming recipients in more than one community.

For example, @bijoy1 appeared among the Top 10 Booming recipients in four different communities during the period, while several other authors appeared in the Top 10 in two communities.

Again, an active author can legitimately contribute good content to several communities.

But it raises an important ecosystem question:

Are limited support opportunities reaching a broad range of authors, or are some highly active users accessing several separate community nomination channels?

The blockchain can show the distribution pattern.

It cannot determine whether that distribution is fair.

And to understand how economically important these patterns are, we now need to connect them to the finalized rewards.

Where Do the Rewards Actually Go?

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Posting frequency and support are only part of the picture. The next step was to examine the finalized author rewards.

Of the 26,242 main posts:

17,628 had an author_reward operation

15,951 had a positive finalized author reward

That means approximately 60.8% of all main posts generated a positive author reward.

Across the dataset, finalized author rewards totaled:

183,864.801 STEEM

319,039,619.837 VESTS ≈ 197,068 Estimated SP

As explained earlier, STEEM and Estimated SP are different assets. Where they are combined below, it is only as a comparison measure for analyzing distribution.

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Posting More Does Not Necessarily Mean Earning More

Some of the most active accounts illustrate this clearly:

Author	Posts	Approx. STEEM + Est. SP
@web2.support	450	~0
@steemburnup	168	~0
@sweecee	151	~0
@statsexpert	149	~51
@haejin	143	~7,778
@tworld	139	~78
@abandi	112	~0
@playpoker	112	~196
@playhighcard	112	~196
@playdice	112	~324
@netscape	111	~31

Several accounts published more than 100 main posts but earned very little, while @haejin combined very high posting frequency with substantial rewards.

So:

More posts do not automatically mean more rewards.

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Rewards Are More Concentrated Than Posting

When authors are ranked separately by finalized rewards, concentration becomes much stronger:

Group	Share of Posts*	Share of Rewards**
Top 10	6.28%	20.75%
Top 20	9.65%	31.67%
Top 50	15.67%	49.01%
Top 100	21.37%	61.78%

*Ranked by number of posts.
**Ranked separately by reward comparison value.

These are not necessarily the same accounts, so this is not an account-for-account comparison.

What it demonstrates is that:

Content production was much more broadly distributed than rewards.

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The Largest Reward Recipients

The Top 10 authors ranked by the STEEM + Estimated SP comparison measure were:

Author	Posts	STEEM	Est. SP	Approx. Total
@rme	14	7,704.7	~7,702	~15,407
@trafalgar	12	0	~8,650	~8,650
@haejin	143	3,889.8	~3,889	~7,778
@puss.coin	14	3,842.0	~3,841	~7,683
@cjsdns	14	3,722.6	~3,722	~7,444
@abbc-reports	14	3,548.1	~3,547	~7,095
@sa-reports	14	3,461.0	~3,460	~6,921
@boc-reports	14	3,393.7	~3,393	~6,786
@tfc-reports	14	3,318.2	~3,317	~6,635
@happycapital	26	2,323.2	~2,323	~4,646

Interestingly:

None of these Top 10 reward recipients had a matched Curator/Booming-supported root post in this dataset.

This is important because it shows that Curator and Booming do not explain all reward concentration.

Steem is a stake-based blockchain, and large stakeholders can exercise substantial voting power independently of these support systems.

For that reason, stake-supported activity and Curator/Booming-supported activity should not automatically be treated as the same phenomenon.

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What Is the Economic Effect of Support?

Curator/Booming-supported posts represented:

14.39% of all main posts

but were associated with approximately:

26.50% of the total reward comparison value

Average comparison reward per post was approximately:

Supported: 26.73

Unsupported: 12.46

Supported posts therefore averaged roughly:

2.1× the comparison reward of unsupported posts.

This is an association, not proof of causation.

A supported post may receive many other votes, and the fact that it was supported does not mean Curator/Booming supplied its entire reward.

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Curator and Booming Also Look Different Economically

Support Type	Share of Posts	Share of Rewards	Avg. Reward/Post
Curator only	7.72%	18.82%	~35.38
Booming only	5.95%	5.40%	~13.17
Both	0.72%	2.28%	~46.22
Neither	85.61%	73.50%	~12.46

Booming-only posts were relatively close to unsupported posts in average comparison reward.

Curator-only posts represented 7.72% of content but 18.82% of comparison rewards.

Again, these figures describe the total author reward on supported posts, not the exact amount supplied by the Curator or Booming vote.

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The Repeatedly Supported Authors

For the eight highly active authors identified earlier, finalized rewards were:

Author	Posts	STEEM	Est. SP	Support Rate
@tasonya	74	1,144.8	~1,144.5	66.2%
@bijoy1	66	588.6	~588.5	68.2%
@narocky71	45	371.6	~371.4	84.4%
@jamal7	48	369.9	~369.9	75.0%
@limon88	42	360.5	~360.4	66.7%
@jannat7	46	346.1	~346.0	71.7%
@bristy1	42	312.1	~312.0	71.4%
@ride1	45	243.2	~243.2	82.2%

For several repeatedly supported authors, more than 90% of their finalized rewards came from posts that received at least one Curator and/or Booming vote.

Examples include:

@narocky71 — 97.4%
@fasoniya — 96.8%
@bdwomen — 96.1%
@ride1 — 95.7%
@ahp93 — 95.6%
@jannat7 — 94.9%
@jamal7 — 92.2%
@limon88 — 91.2%

This does not mean Curator/Booming supplied those percentages of the rewards.

It means those percentages were earned on posts that happened to receive such support.

Nevertheless, it shows that supported posts were economically very important for some authors during the study period.

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High Frequency Alone Was Not the Key

The broader high-frequency group produced 2,586 posts, but received support on only 14.81% of them — almost identical to the rest of the non-system dataset.

Their reward share was also far from dominant.

So the data does not support a simple strategy of:

“Post as much as possible and collect the rewards.”

The stronger pattern appears only when high activity, community participation and repeated support occur together.

Before drawing the final conclusions, I also examined one smaller reward-related behavior:

Self-Comments and Self-Voting

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I also examined direct comments authors made under their own main posts.

A self-comment is not unusual. Authors may add information, updates or links, and a self-vote does not by itself prove reward-seeking behavior.

During the 14-day period I identified:

1,628 direct self-comments from 150 authors

Of these:

196 were self-voted — about 12%

The analysis covers direct self-comments under the author's own root post, not normal replies to other users in a discussion.

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Finalized Rewards

Of the 1,628 direct self-comments:

• 258 generated a positive finalized author reward
• Total rewards: 686.492 STEEM + ~686.36 Estimated SP
• 179 rewarded comments were self-voted
• Self-voted rewarded comments generated 66.423 STEEM + ~66.48 Estimated SP

Although 69.4% of the rewarded direct self-comments were self-voted, they accounted for only about 9.7% of the liquid STEEM reward in this self-comment dataset.

The 69.4% figure applies only to the rewarded direct self-comments analyzed here — not to Steem comments in general.

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Frequency Is Not the Same as Economic Impact

Two accounts illustrate this well.

@thoth.test made 140 direct self-comments, self-voted all 140, and 138 generated a positive reward. Yet the total was only about:

0.224 STEEM + 0.28 Estimated SP

By comparison, @graphene-faucet made 44 direct self-comments, self-voted 42, and those 42 generated approximately:

65.518 STEEM + 65.52 Estimated SP

So counting self-votes alone can be misleading.

The meaningful measurements are:

frequency, self-voting rate, and actual finalized reward.

I also appeared in this dataset. @xpilar had 9 direct self-comments, one self-voted, and two rewarded.

That is another reason I do not consider the existence of a self-vote alone to be evidence of improper behavior.

The blockchain records actions, but it cannot establish the intention behind them.

Overall, direct self-comment voting does not appear to be a major explanation for the broader reward concentration found in this study.

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What Does All This Tell Us?

We can now return to the question that started this investigation.

I suspected that some of the same users were posting continuously across communities while repeatedly receiving support and rewards.

After analyzing 26,242 main posts, 3,610 authors, community activity, Curator and Booming support, finalized rewards and self-comments, the answer is more nuanced than I expected.

My original impression was partly correct — but the data also challenged some of my assumptions.

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Findings and Conclusion

The data both confirmed and challenged my original impression.

There is a relatively small group of users publishing at a very high frequency, often every day and sometimes across several communities.

There is also a smaller subset combining high activity with repeated Curator and/or Booming support, in some cases on 13 or 14 of the 14 days studied.

But the broader claim that “the same people post all day and take all the rewards” is not supported by the data.

78.63% of all main posts came from authors outside the Top 100 most active accounts.

And high-frequency authors as a group had a Curator/Booming support rate of 14.81%, compared with 15.30% for the rest of the non-system dataset.

Simply posting more did not result in a higher support rate.

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The Community Question

One of the most important discoveries was the role of communities in Booming support.

Because communities nominate posts for Booming, repeated Booming votes should not simply be interpreted as Booming independently choosing the same authors.

The community analysis showed very different distribution patterns.

In one community, the Top 5 recipients accounted for only 21.5% of Booming-supported posts.

In another, the Top 5 accounted for 88.6%.

This shifts the question from:

“Who is Booming supporting?”

toward:

“How broadly are communities distributing their nomination opportunities?”

Some authors also appeared among significant Booming recipients across multiple communities. That is not evidence of wrongdoing, but it raises a legitimate question about whether limited support opportunities are reaching a broad enough range of contributors.

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Rewards Tell a Different Story

Content production was relatively broad.

Rewards were much more concentrated.

The Top 100 authors ranked by posting activity produced 21.37% of all posts, while the Top 100 ranked separately by reward received 61.78% of the reward comparison value.

But Curator and Booming do not explain all of that concentration.

In fact, none of the Top 10 reward recipients had a matched Curator/Booming-supported root post in this dataset.

Stake is clearly a major part of the Steem reward economy.

At the same time, Curator/Booming-supported posts represented only 14.39% of all main posts, but were associated with 26.50% of the reward comparison value.

So access to support can be economically important, even though the data cannot tell us how much of each post's total reward was caused specifically by those votes.

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Was My Original Suspicion Correct?

My answer is:

Partly.

The data confirms that some users publish systematically across multiple communities and receive support repeatedly.

It also confirms that some communities concentrate a large proportion of their Booming-supported posts among recurring recipients.

But it does not show that a small group produces most Steem content.

It does not show that posting frequently automatically produces more support.

And it does not show that Curator/Booming is responsible for all reward concentration.

The reality is more complicated.

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The Bigger Question

For me, the most important question after completing this analysis is not whether somebody posts “too much” or earns “too much.”

It is:

How widely are opportunities distributed?

When curation and nomination resources are limited, are new authors regularly being discovered?

Do communities rotate support among contributors?

Can somebody new to Steem realistically become noticed and supported?

Or do support opportunities gradually circulate among people who already understand how to access several communities and support channels?

These are questions the blockchain data can help us ask.

But the numbers alone cannot decide what the Steem community considers fair.

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A 14-Day Snapshot

This analysis covers only August 8–21, 2026.

Fourteen days are enough to identify patterns, but not enough to claim that those patterns remain unchanged throughout the year.

It would be interesting to repeat the same analysis over several months and see whether the same authors, communities and support patterns continue.

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Why I Published This

My intention is not to attack authors, communities or curators.

I have been part of Steem for many years, and I want Steem to succeed.

That is also why I believe we should be able to look critically at our own ecosystem.

The blockchain is public. Instead of relying only on impressions, we can examine the data and discuss what it actually shows.

People may interpret these results differently.

That is fine.

A healthy community should be able to ask difficult questions without automatically treating the questions themselves as an attack.

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Final Thought

Earning rewards is not the problem.

Being highly active is not the problem.

Receiving repeated support is not automatically a problem either.

The important question is whether the ecosystem remains open enough that many people have a realistic opportunity to participate, contribute, be discovered and eventually receive support.

Because in the long run, Steem does not grow by circulating attention among the same people.

It grows when new people see a reason to become part of it.

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Data period: August 8–21, 2026

Main posts analyzed: 26,242

Unique authors: 3,610

Data & Tools: Steem blockchain data, SteemWorld.org and Steem Blockchain Data Services (SDS).

Special thanks to @steemchiller — SteemWorld.org, Built with ♥

xpilar.witness

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