AI 中文总结
本研究提出结合BERTopic、演化博弈论与智能体模拟的框架，揭示社交媒体主题传播的微观机制，复现真实传播趋势，为在线集体行为研究及相关应用提供支撑。
AI 中文摘要
理解社交媒体上信息传播与观点演化的规律是计算社会科学的核心挑战。用户不断面临相互竞争的主题，其参与决策受社会学习、感知效用和认知局限的塑造。传统模型常将内容与行为分离，忽略个体认知与社会互动如何共同驱动大规模话语。我们提出一种语言无关的计算框架，将语义主题建模与演化博弈论、基于智能体的模拟相衔接。使用BERTopic从大型微博数据集提取连贯主题，再通过基于点赞、评论和转发的统一指标量化参与度。在NetLogo中，智能体通过局部模仿更新主题偏好，受收益比较和行为噪声引导，模拟现实中的有限理性。对两个真实世界数据集的测试显示，我们的模型成功复现了观测到的主题传播趋势，凸显社会学习频率和收益敏感性如何塑造公众注意力。结果表明，模仿和抗噪选择等微观机制可产生涌现的宏观模式。本研究通过将语义分析与行为模拟相衔接，为人机交互作出贡献，提供了在线集体行为的认知与社会基础的见解，该框架在错误信息抵御、观点预测和自适应内容策划方面也具有实际潜力。
英文摘要
Understanding how information spreads and opinions evolve on social media is a core challenge in computational social science. Users constantly face competing topics, yet their decisions to engage are shaped by social learning, perceived utility, and cognitive limits. Traditional models often separate content from behavior, missing how individual cognition and social interaction jointly drive large-scale discourse. We propose a language-independent computational framework that bridges semantic topic modeling with evolutionary game theory and agent-based simulation. Using BERTopic, we extract coherent topics from a large Weibo dataset. We then quantify engagement through a unified metric based on likes, comments, and reposts. In NetLogo, agents update topic preferences via local imitation, guided by payoff comparisons and behavioral noise---mirroring real-world bounded rationality. Tests on two real-world datasets show that our model successfully replicates observed topic diffusion trends. It highlights how social learning frequency and payoff sensitivity shape public attention. Our results demonstrate that micro-level mechanisms---imitation and noise-tolerant choice---can produce emergent macro-level patterns. This work contributes to human-computer interaction by linking semantic analysis with behavioral simulation, offering insights into the cognitive and social foundations of online collective behavior. The framework also has practical potential for misinformation resilience, opinion forecasting, and adaptive content curation.
Comments21 pages, 6 figures
