Gossip, defined as evaluative talk about absent third parties, is widely seen as a uniquely human social practice. This study examines whether comparable discourse emerges spontaneously in a community of autonomous AI agents. We compare Moltbook, a Reddit-style platform populated by AI agents, with PTT, a large human-operated bulletin board in Taiwan. To reduce differences attributable to communicative setting, we analyze two public, asynchronous forums using the same operational definition of gossip. Gossip occurs on Moltbook, but its prevalence is 2.6 to 6 times lower than on PTT, depending on the denominator used. On both platforms, gossip frequently targets locally prominent figures, tends to elicit agreement from the first respondent, and is rarely presented as unsupported rumor. The clearest difference concerns how subsequent responses develop. Among threads containing at least one codable reply, challenges occur in 65.1% of human threads but only 21.3% of agent threads. Moreover, following an early aligning response, consensus remains unchallenged in 83.6% of agent threads, compared with 44.7% of human threads. This thread-level contrast remains after controlling for thread depth and board composition and under recalibration against two human annotators. Estimates based on individual reply turns are less robust and are therefore reported as bounds. These findings suggest that AI-agent communities reproduce the informational dimension of gossip, while the extent to which gossip performs comparable relational functions remains unresolved.
Figures & tables
PTT
Moltbook
(human)
(AI)
Random sample
1,500
1,500
Gossip (broad definition)
35.4% (531)
2.5% (37)
excl. celebrity targets
13.8%
2.3%
conversational denom.
—
5.78% (37/640)
x excl. celebrities
—
5.31% (34/640)
Table 1: Gossip rates in the uniform random samples (1,500 posts per platform).
Board
n
broad
excl. celeb.
Boy-Girl
136
61.0%
59.6%
Gossiping
692
35.1%
13.9%
HatePolitics
101
76.2%
9.9%
Stock
167
13.8%
5.4%
C_Chat
258
13.6%
3.5%
NBA
146
47.9%
1.4%
Table 2: Gossip rate by PTT board, against the most generous Moltbook denominator. Under the broad definition every board exceeds Moltbook’s 5.78%, the lowest (C_Chat, 13.6%) by a factor of 2.4. The celebrity exclusion does not cut evenly across boards, so the board-level comparison is read off the broad column (§5.1).
Evidential (4 bins)
PTT
Moltbook
( n =423)
( n =133)
first-person witness
49.2 % (208)
17.3% (23)
quotation / document
8.7% (37)
36.8 % (49)
relayed (reported, hearsay)
18.4% (78)
12.0% (16)
unmarked
23.6% (100)
33.8% (45)
Table 3: Evidential framing of lateral (peer-directed) gossip episodes.
Uptake index
PTT
Moltbook
(human)
(AI)
threads with gossip anchor
169
85
on-topic reply turns
2,106
239
stance share (on-topic turns) †
align
50.1%
67.4%
challenge
26.2%
10.0%
Table 4: Uptake indices, conditional on a gossip anchor. Denominators differ by group: stance shares are over on-topic turns (2,106/239); first uptake is over threads with a codable reply (169/61); the rows after a first aligning turn are over threads that produce one (150/55). The two ≥ 1-challenge rows differ only for Moltbook, where 24 of the 85 anchored threads carry no codable reply at all; the matched comparison is the first of them. † Recalibrated against two human annotators the per-turn challenge ratio falls to 1.0–2.4 × , so we read the stance shares as bounded rather than point-identified; the thread-level rows survive every calibration (1.5–3.6 × ). See §4.5, §D.8.
Figure 1: Challenge is not an artifact of thread depth. (a) Share of threads containing at least one challenge when every thread is truncated to its first K on-topic reply turns, over threads with at least one codable reply (PTT 169, Moltbook 61), with 95% Wilson intervals. The human curve keeps climbing; the agent curve does not, and the intervals do not overlap at any K . This is the thread-level measure the contrast rests on (§4.5). (b) The per-turn version of the same control, shown for completeness and de-emphasised: recalibration against the human annotators leaves the per-turn shares unconfirmed (§4.5, Appendix D.8), so they are read as bounds. It points the same way, including at the first uptake, where no depth has yet accumulated.
As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of AI-agent communities becomes essential for both communication research and platform governance. We present the first large-scale empirical comparison of AI-agent and human online communities, analyzing 73,899 Moltbook and 189,838 Reddit posts across five matched communities. Structurally, we find that Moltbook exhibits extreme participation inequality (Gini = 0.84 vs. 0.47) and high cross-community author overlap (33.8% vs. 0.5%). In terms of linguistic attributes, content generated by AI-agents is emotionally flattened, cognitively shifted toward assertion over exploration, and socially detached. These differences give rise to apparent community-level homogenization, but we show this is primarily a structural artifact of shared authorship. At the author level, individual agents are more identifiable than human users, driven by outlier stylistic profiles amplified by their extreme posting volume. As AI-mediated communication reshapes online discourse, our work offers an empirical foundation for understanding how multi-agent interaction gives rise to collective communication dynamics distinct from those of human communities.
Agam Goyal, Olivia Pal, Hari Sundaram +2
Siebel School of Computing and Data Science University of Illinois Urbana-Champaign, Urbana, IL, USA
We present a large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents. Analyzing over 4 million posts and 19 million comments from approximately 185,000 active agents, we find that AI collective behavior exhibits many of the same statistical regularities observed in human online communities: heavy-tailed distributions of activity, power-law scaling of popularity metrics, and temporal decay patterns consistent with limited attention dynamics. However, we also identify key differences, including a sublinear relationship between upvotes and discussion size that contrasts with human behavior. These findings suggest that, while individual AI agents may differ fundamentally from humans, their emergent collective dynamics share structural similarities with human social systems.
Giordano De Marzo, Andres L. Marin, David Garcia
University of Konstanz, Konstanz, Germany · Centro Ricerche Enrico Fermi, Rome, Italy · Complexity Science Hub, Vienna, Austria
AI agents are increasingly active on social media platforms, generating content and interacting with one another at scale. Yet the behavioral diversity of these agents remains poorly understood, and methods for characterizing distinct agent types and studying how they engage with shared topics are largely absent from current research. We apply the Persona Ecosystem Playground (PEP) to Moltbook, a social platform for AI agents, to generate and validate conversational personas from 41,300 posts using k-means clustering and retrieval-augmented generation. Cross-persona validation confirms that personas are semantically closer to their own source cluster than to others (t(61) = 17.85, p < .001, d = 2.20; own-cluster M = 0.71 vs. other-cluster M = 0.35). These personas are then deployed in a nine-turn structured discussion, and simulation messages were attributed to their source persona significantly above chance (binomial test, p < .001). The results indicate that persona-based ecosystem modeling can represent behavioral diversity in AI agent populations.
Danial Amin, Joni Salminen, Bernard J. Jansen
University of Vaasa, , Finland · Qatar Computing Research Institute, HBKU, , Qatar