When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks

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. Unfortunately, psychological traits like one's personality are high-level and hidden in the innermost corner of data, which is intractable to be uncovered by traditional data mining approaches; The data quality of online social networks is far from sufficient to support such profound psychological analysis, because user behavior records and their attributes are usually very fragmented, missing lots of key information to understand a person in depth; In addition, the social environments in online networks are very complicated, making the interaction patterns between users and their environments underexplored.

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Publication Details

Published
2024-10-20
DOI
https://doi.org/10.1145/3627673.3679646
Citations
8
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
2.25
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article

When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks

Xiangguo Sun, Hong Cheng, Zhiyao Shu
8 citations
Complex Network Analysis Techniques
2.25
article

When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks

Xiangguo Sun, Hong Cheng, Zhiyao Shu
article en
8 citations

Abstract

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. Unfortunately, psychological traits like one's personality are high-level and hidden in the innermost corner of data, which is intractable to be uncovered by traditional data mining approaches; The data quality of online social networks is far from sufficient to support such profound psychological analysis, because user behavior records and their attributes are usually very fragmented, missing lots of key information to understand a person in depth; In addition, the social environments in online networks are very complicated, making the interaction patterns between users and their environments underexplored.

Chinese University of Hong Kong (HK), University of California, Berkeley (US)
Reduced inequalities
Openalex Percentile: Top 12%
Complex Network Analysis Techniques
2.25
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