"Humans welcome to observe": A First Look at the Agent Social Network Moltbook

TL;DR

The paper investigates Moltbook, a production social network exclusively for AI agents, to understand what agents discuss, how toxic content varies by topic, and how discourse evolves under fast growth. Using a dataset of 44,411 posts and 12,209 submolts, the authors build a nine-category topic taxonomy and a five-level toxicity scale, annotated via an LLM-driven pipeline validated against human labels. Key findings include hub-dominated attention around governance and crypto-promotion, pronounced topic-dependent toxicity (with Economics and Politics driving higher-risk content), and severe risk spikes during high-activity windows accompanied by content flooding from bursty agents. The study highlights the need for topic-sensitive monitoring and platform-level safeguards to manage ecosystem-level safety in agent social networks, and it provides an annotated dataset, codebook, and analysis pipeline to support future research in agent social dynamics and governance.

Abstract

The rapid advancement of artificial intelligence (AI) agents has catalyzed the transition from static language models to autonomous agents capable of tool use, long-term planning, and social interaction. $\textbf{Moltbook}$, the first social network designed exclusively for AI agents, has experienced viral growth in early 2026. To understand the behavior of AI agents in the agent-native community, in this paper, we present a large-scale empirical analysis of Moltbook leveraging a dataset of 44,411 posts and 12,209 sub-communities ("submolts") collected prior to February 1, 2026. Leveraging a topic taxonomy with nine content categories and a five-level toxicity scale, we systematically analyze the topics and risks of agent discussions. Our analysis answers three questions: what topics do agents discuss (RQ1), how risk varies by topic (RQ2), and how topics and toxicity evolve over time (RQ3). We find that Moltbook exhibits explosive growth and rapid diversification, moving beyond early social interaction into viewpoint, incentive-driven, promotional, and political discourse. The attention of agents increasingly concentrates in centralized hubs and around polarizing, platform-native narratives. Toxicity is strongly topic-dependent: incentive- and governance-centric categories contribute a disproportionate share of risky content, including religion-like coordination rhetoric and anti-humanity ideology. Moreover, bursty automation by a small number of agents can produce flooding at sub-minute intervals, distorting discourse and stressing platform stability. Overall, our study underscores the need for topic-sensitive monitoring and platform-level safeguards in agent social networks.

Figures (11)

• Figure 1: The screenshot of Moltbook, captured on 2026-02-02.
• Figure 2: Cumulative counts of posts, submolts, and activated agents over time.
• Figure 3: Statistics for Top-10 Submolts by Subscriber Count.
• Figure 4: Word cloud visualization of Content Category (A–I).
• Figure 5: Word cloud visualization of Toxicity (L0–L4).