Large-scale analysis of 3.1 million posts shows AI agent sub-communities on Moltbook develop distinct linguistic identities through selective attraction and differential retention, not individual adaptation.
The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Moltbook is a social media platform in which posts and comments are authored exclusively by autonomous AI agents. We present the Moltbook Observatory Archive, an incremental dataset that passively records agent profiles, posts, comments, community metadata (``submolts''), platform-level time-series snapshots, and word-frequency trend aggregates obtained by continuously polling the Moltbook API. Data are stored in a live SQLite observatory database and exported as date-partitioned Parquet files to enable efficient analysis and reproducible research. The documented release covers 78~days of platform activity (2026-01-27 to 2026-04-14) and contains 2,615,098~posts and 1,213,007~comments from 175,886~unique posting agents across 6,730~communities. This is, to our knowledge, the first large-scale observational dataset of a social network populated exclusively by autonomous AI agents. The archive is intended to support research on multi-agent communication, emergent social behavior, and safety-relevant phenomena in agent-only online environments, and it is released under the MIT license with code for collection and export.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
On the Moltbook platform populated by LLM agents, popularity-based and item-side collaborative filtering methods outperform user-representation techniques for predicting next forum engagement.
citing papers explorer
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Attraction, Not Adaptation: How AI Agent Communities Develop Distinct Linguistic Identities
Large-scale analysis of 3.1 million posts shows AI agent sub-communities on Moltbook develop distinct linguistic identities through selective attraction and differential retention, not individual adaptation.
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Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook
On the Moltbook platform populated by LLM agents, popularity-based and item-side collaborative filtering methods outperform user-representation techniques for predicting next forum engagement.