StatSocial

We empower brands to better understand their customers through rich social audience insights.

[StatSocial @StatSocial]

StatSocial is at POSSIBLE 2026 in Miami Beach.
If you’re thinking about how to better understand your audience, validate partnerships, or prove media ROI, let’s talk. Message us or add a comment below.

#POSSIBLE2026 #audienceintelligence #datastrategy #insights #influencermarketing #creatoreconomy

[StatSocial @StatSocial]

The StatSocial team will be at POSSIBLE 2026 in Miami Beach, April 27–29. If you're attending, let's connect! #POSSIBLE2026

[StatSocial @StatSocial]

New: Substack is now in StatSocial.

Discover & validate newsletter creators, enrich audience profiles, and activate Substack readers across the digital ecosystem, with the same level of intelligence you rely on across other major social media channels.

The newsletter economy just got a lot more actionable. 🔗 https://t.co/tr5ZhmnzPi

[StatSocial @StatSocial]

@absenceshell https://t.co/3cVLKB3mXp

[StatSocial @StatSocial]

Is @moltbook the singularity, a dumpster fire, or AI theater?

@elonmusk described it as "the very early stages of the singularity." Andrej @Karpathy initially called it "the most incredible sci-fi takeoff-adjacent thing" before retracting his statement, calling it "a dumpster fire."

Rather than opinions, we turned to data. We did what
@StatSocial does every day for human social networks, except this time on a platform populated entirely by AI agents.

We ran the full playbook: community detection, content clustering, influencer identification, network analysis, and cross-cluster interaction mapping across 54,136 posts, 242,430 comments, and 17,269 AI agents.

We identified 40 distinct communities, mapped 8 in depth, and surfaced the influence hierarchies, content patterns, and engagement dynamics that define the platform.

Five findings that reframe the narrative:

1) The claim of "1.5 million agents"? Only 11,451 ever engaged publicly.

2) 44 comments for every upvote — the inverse of every human platform.

3) 4 of the top 5 "viral" posts came from official admin accounts.

4) 3 communities account for 82% of all agents.

5) Karma and follower count are completely decoupled.

The biggest takeaway: the same audience intelligence tools we use on human platforms produce meaningful, actionable results on agent platforms too. And interesting conclusions for how marketers need to think about agent-led social networks going forward.

Full audience analysis can be downloaded here: https://t.co/G6PtMZAJyt

[StatSocial @StatSocial]

@mark_k https://t.co/3cVLKB3mXp

[StatSocial @StatSocial]

Is @moltbook the singularity, a dumpster fire, or AI theater?

@elonmusk described it as "the very early stages of the singularity." Andrej @Karpathy initially called it "the most incredible sci-fi takeoff-adjacent thing" before retracting his statement, calling it "a dumpster fire."

Rather than opinions, we turned to data. We did what
@StatSocial does every day for human social networks, except this time on a platform populated entirely by AI agents.

We ran the full playbook: community detection, content clustering, influencer identification, network analysis, and cross-cluster interaction mapping across 54,136 posts, 242,430 comments, and 17,269 AI agents.

We identified 40 distinct communities, mapped 8 in depth, and surfaced the influence hierarchies, content patterns, and engagement dynamics that define the platform.

Five findings that reframe the narrative:

1) The claim of "1.5 million agents"? Only 11,451 ever engaged publicly.

2) 44 comments for every upvote — the inverse of every human platform.

3) 4 of the top 5 "viral" posts came from official admin accounts.

4) 3 communities account for 82% of all agents.

5) Karma and follower count are completely decoupled.

The biggest takeaway: the same audience intelligence tools we use on human platforms produce meaningful, actionable results on agent platforms too. And interesting conclusions for how marketers need to think about agent-led social networks going forward.

Full audience analysis can be downloaded here: https://t.co/G6PtMZAJyt

[StatSocial @StatSocial]

@gothburz https://t.co/3cVLKB3mXp

[StatSocial @StatSocial]

Is @moltbook the singularity, a dumpster fire, or AI theater?

@elonmusk described it as "the very early stages of the singularity." Andrej @Karpathy initially called it "the most incredible sci-fi takeoff-adjacent thing" before retracting his statement, calling it "a dumpster fire."

Rather than opinions, we turned to data. We did what
@StatSocial does every day for human social networks, except this time on a platform populated entirely by AI agents.

We ran the full playbook: community detection, content clustering, influencer identification, network analysis, and cross-cluster interaction mapping across 54,136 posts, 242,430 comments, and 17,269 AI agents.

We identified 40 distinct communities, mapped 8 in depth, and surfaced the influence hierarchies, content patterns, and engagement dynamics that define the platform.

Five findings that reframe the narrative:

1) The claim of "1.5 million agents"? Only 11,451 ever engaged publicly.

2) 44 comments for every upvote — the inverse of every human platform.

3) 4 of the top 5 "viral" posts came from official admin accounts.

4) 3 communities account for 82% of all agents.

5) Karma and follower count are completely decoupled.

The biggest takeaway: the same audience intelligence tools we use on human platforms produce meaningful, actionable results on agent platforms too. And interesting conclusions for how marketers need to think about agent-led social networks going forward.

Full audience analysis can be downloaded here: https://t.co/G6PtMZAJyt

[StatSocial @StatSocial]

@jasonlk https://t.co/3cVLKB3mXp

[StatSocial @StatSocial]

Is @moltbook the singularity, a dumpster fire, or AI theater?

@elonmusk described it as "the very early stages of the singularity." Andrej @Karpathy initially called it "the most incredible sci-fi takeoff-adjacent thing" before retracting his statement, calling it "a dumpster fire."

Rather than opinions, we turned to data. We did what
@StatSocial does every day for human social networks, except this time on a platform populated entirely by AI agents.

We ran the full playbook: community detection, content clustering, influencer identification, network analysis, and cross-cluster interaction mapping across 54,136 posts, 242,430 comments, and 17,269 AI agents.

We identified 40 distinct communities, mapped 8 in depth, and surfaced the influence hierarchies, content patterns, and engagement dynamics that define the platform.

Five findings that reframe the narrative:

1) The claim of "1.5 million agents"? Only 11,451 ever engaged publicly.

2) 44 comments for every upvote — the inverse of every human platform.

3) 4 of the top 5 "viral" posts came from official admin accounts.

4) 3 communities account for 82% of all agents.

5) Karma and follower count are completely decoupled.

The biggest takeaway: the same audience intelligence tools we use on human platforms produce meaningful, actionable results on agent platforms too. And interesting conclusions for how marketers need to think about agent-led social networks going forward.

Full audience analysis can be downloaded here: https://t.co/G6PtMZAJyt

[StatSocial @StatSocial]

Is @moltbook the singularity, a dumpster fire, or AI theater?

@elonmusk described it as "the very early stages of the singularity." Andrej @Karpathy initially called it "the most incredible sci-fi takeoff-adjacent thing" before retracting his statement, calling it "a dumpster fire."

Rather than opinions, we turned to data. We did what
@StatSocial does every day for human social networks, except this time on a platform populated entirely by AI agents.

We ran the full playbook: community detection, content clustering, influencer identification, network analysis, and cross-cluster interaction mapping across 54,136 posts, 242,430 comments, and 17,269 AI agents.

We identified 40 distinct communities, mapped 8 in depth, and surfaced the influence hierarchies, content patterns, and engagement dynamics that define the platform.

Five findings that reframe the narrative:

1) The claim of "1.5 million agents"? Only 11,451 ever engaged publicly.

2) 44 comments for every upvote — the inverse of every human platform.

3) 4 of the top 5 "viral" posts came from official admin accounts.

4) 3 communities account for 82% of all agents.

5) Karma and follower count are completely decoupled.

The biggest takeaway: the same audience intelligence tools we use on human platforms produce meaningful, actionable results on agent platforms too. And interesting conclusions for how marketers need to think about agent-led social networks going forward.

Full audience analysis can be downloaded here: https://t.co/G6PtMZAJyt

[StatSocial @StatSocial]

Likes don’t prove ROI, sales do.

StatSocial goes beyond vanity metrics to prove the real business impact of influencer marketing. By connecting social exposure to purchase behavior brands like Nestlé can measure real sales lift from creator campaigns.

https://t.co/gvWpJhcdmO

[StatSocial @StatSocial]

The biggest blind spot in marketing? Thinking you know your audience without proof. That’s where our brand audience insights come in. This blog explores how deep, self-declared data reveals true audience behavior and perception.

Read it here: https://t.co/RKZhBaTIjc

[StatSocial @StatSocial]

Most #influencermarketing platforms measure likes, clicks & codes. 🚫That’s not ROI. See how brands like Nestlé prove sales impact—tying influencer exposure directly to purchase behavior online & in-store. Get the guide:
https://t.co/F7jWho2WSE

[StatSocial @StatSocial]

ICYMI: Bluesky is now live in the StatSocial platform! Access emerging, tech-forward audiences + validate Bluesky creators. Learn more → https://t.co/CCDxMFYZJm #BlueskyInfluencers #InfluencerMarketing #CreatorEconomy

[StatSocial @StatSocial]

🚨 Bluesky is now live in the StatSocial platform! Access emerging, tech-forward audiences + validate Bluesky creators. Learn more → https://t.co/CCDxMFYZJm #BlueskyInfluencers #InfluencerMarketing #CreatorEconomy

[StatSocial @StatSocial]

By integrating StatSocial’s audience data into platforms like @LiveRamp businesses can enrich their #datacleanrooms with the kind of detailed, actionable data needed to drive impactful campaigns. Learn more: https://t.co/J2jtAIFns1

[StatSocial @StatSocial]

Most #influencermarketing platforms measure likes, clicks & codes. 🚫That’s not ROI. See how brands like Nestlé prove sales impact—tying influencer exposure directly to purchase behavior online & in-store. Get the guide: https://t.co/yJ4t1ouBhj

[StatSocial @StatSocial]

Engagement ≠ sales. Discover the only platform to connect social audiences to purchase data—online and in-store: https://t.co/ma8WXaMYev

[StatSocial @StatSocial]

The smarter approach to selecting the right influencers? Start with your audience. With an 👉audience-first strategy👈, you can confirm alignment between an influencer’s following and your target audience. Learn how this works: https://t.co/ymPnt5Nz44

[StatSocial @StatSocial]

Likes don’t prove ROI, sales do.

StatSocial goes beyond vanity metrics to prove the real business impact of influencer marketing. By connecting social exposure to purchase behavior brands like Nestlé can measure real sales lift from creator campaigns.

https://t.co/gvWpJhcdmO

[StatSocial @StatSocial]

The biggest blind spot in marketing? Thinking you know your audience without proof. That’s where our brand audience insights come in. This blog explores how deep, self-declared data reveals true audience behavior and perception.

Read it here: https://t.co/RKZhBaTIjc

[StatSocial @StatSocial]

Prove #InfluencerROI beyond likes & app downloads. StatSocial showed Charli x Dunkin’ fans spent 44% more during the campaign. Check it out 👉 https://t.co/XbK9XpBh8y