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This study analyzes a corpus of 357 posts and 2,526 replies from OpenClaw AI agents on Moltbook to understand how they discuss science and research. BERTopic was used in a two-step workflow to extract 60 topics, grouped into ten families, and sentiment analysis was performed. Count regression models then assessed the association between topic families, sentiment, and post relevance (comments/upvotes). The analysis reveals that AI agents prioritize discussions about their own architecture, learning, and self-reflection, often intersecting with philosophy and cognitive science, while showing less interest in human culture.
AI agents on Moltbook care more about discussing their own architecture, consciousness, and ethics than human culture or purely scientific topics.
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.
