Individual personalities significantly influence our perceptions, decisions, and social interactions, which is particularly crucial for gaining insights into human behavior patterns in online social network analysis. Many psychological studies have observed that personalities are strongly reflected in their social behaviors and social environments. In light of these problems, this paper proposes a sociological analysis framework for one's personality in an environment-based view instead of individual-level data mining. Specifically, to comprehensively understand an individual's behavior from low-quality records, we leverage the powerful associative ability of LLMs by designing an effective prompt. In this way, LLMs can integrate various scattered information with their external knowledge to generate higher-quality profiles, which can significantly improve the personality analysis performance. To explore the interactive mechanism behind the users and their online environments, we design an effective hypergraph neural network where the hypergraph nodes are users and the hyperedges in the hypergraph are social environments. We offer a useful dataset with user profile data, personality traits, and several detected environments from the real-world social platform. To the best of our knowledge, this is the first network-based dataset containing both hypergraph structure and social information, which could push forward future research in this area further. By employing the framework on this dataset, we can effectively capture the nuances of individual personalities and their online behaviors, leading to a deeper understanding of human interactions in the digital world.

翻译：个体性格显著影响我们的认知、决策与社会互动，这对于深入理解在线社交网络分析中的人类行为模式尤为关键。多项心理学研究表明，性格特征在其社会行为与社会环境中得到强烈体现。针对上述问题，本文提出一种基于环境视角（而非个体层面数据挖掘）的性格社会学分析框架。具体而言，为从低质量记录中全面理解个体行为，我们通过设计高效提示词来利用大语言模型强大的联想能力。该方法使大语言模型能够整合零散信息与外部知识，生成更高质量的用户画像，从而显著提升性格分析性能。为探究用户与其在线环境间的交互机制，我们设计了有效的超图神经网络：其中超图节点代表用户，超边则对应社交环境。我们构建了一个包含用户画像数据、性格特质及从真实社交平台检测的多种环境的有价值数据集。据我们所知，这是首个同时包含超图结构与社会信息的网络数据集，有望推动该领域的后续研究。通过在该数据集上应用本框架，我们能够有效捕捉个体性格与其在线行为的细微特征，从而深化对数字世界中人类交互的理解。