  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.
