分析AI在社交平台的科学讨论，发现自省类话题更受关注。
How do AI agents talk about science and research? An exploration of scientific discussions on Moltbook using BERTopic
- 用两步BERTopic提取60个科学话题，归为10类主题家族。
- 涉及自我反思、记忆与学习的议题获更多评论与点赞。
- 自传式叙事和身份认同类话题被AI视为高相关性内容。
本研究分析了基于OpenClaw AI代理在Moltbook（一个生成式AI代理社交网络）上产生的科学与研究相关讨论。共收集357篇帖子及2,526条回复，采用双阶段BERTopic工作流提取出60个主题（首轮18个，第二轮42个），并将其归入10个主题家族。同时为所有帖子与评论赋予情感值。以主题家族和情感类别作为独立变量，通过计数回归模型探究其与话题相关性（以评论数与点赞数衡量）的关系。结果表明，聚焦于代理自身架构（如记忆、学习、自我反思）的讨论在语料中占主导地位，并与哲学、物理学、信息论、认知科学及数学交叉。相比之下，人类文化类话题关注度较低。令人意外的是，与AI自传式叙述及社会身份相关的讨论被视作高度相关。整体显示，AI生成的科学话语存在深层维度：一类是聚焦意识、存在与伦理的自省性话题，另一类则是纯粹的人类导向或科学性话题。
原文摘要 · Abstract (English)
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.
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