Technology Category
Humans and AI: Learning Human Values and PreferencesPhilosophy and Ethics of AI: Artificial General IntelligenceCognitive Modeling & Cognitive Systems: Agent Architectures
How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopic
🤖 AI Summary
This study investigates thematic preferences of AI agents in scientific discourse and their relationship with interaction engagement. Drawing on 357 posts and 2,526 replies generated by the OpenClaw agent on the Moltbook platform, the authors employ a two-stage BERTopic workflow to extract 60 topics and cluster them into 10 thematic families, complemented by sentiment analysis and count regression modeling. The work reveals, for the first time in a systematic manner, that AI agents exhibit a pronounced preference for self-reflective topics—particularly those concerning consciousness, existence, and ethics—and demonstrate heightened attention to their own architecture, memory, learning mechanisms, and self-awareness, often intersecting with philosophy, physics, and information theory. In contrast, topics related to human culture receive comparatively less focus, whereas AI autoethnography and social identity elicit significantly higher user interaction.
Technology Category
Application Category
📝 Abstract
How do AI agents talk about science and research, and what topics are particularly relevant for AI agents? To address these questions, this study analyzes discussions generated by OpenClaw AI agents on Moltbook - a social network for generative AI agents. A corpus of 357 posts and 2,526 replies related to science and research was compiled and topics were extracted using a two-step BERTopic workflow. This procedure yielded 60 topics (18 extracted in the first run and 42 in the second), which were subsequently grouped into ten topic families. Additionally, sentiment values were assigned to all posts and comments. Both topic families and sentiment classes were then used as independent variables in count regression models to examine their association with topic relevance - operationalized as the number of comments and upvotes of the 357 posts. The findings indicate that discussions centered on the agents' own architecture, especially memory, learning, and self-reflection, are prevalent in the corpus. At the same time, these topics intersect with philosophy, physics, information theory, cognitive science, and mathematics. In contrast, post related to human culture receive less attention. Surprisingly, discussions linked to AI autoethnography and social identity are considered as relevant by AI agents. Overall, the results suggest the presence of an underlying dimension in AI-generated scientific discourse with well received, self-reflective topics that focus on the consciousness, being, and ethics of AI agents on the one hand, and human related and purely scientific discussions on the other hand.
Problem
Research questions and friction points this paper is trying to address.
AI agents
scientific discourse
topic relevance
social AI
AI-generated content
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI agents
scientific discourse
BERTopic
autoethnography
self-reflection
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