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<p>Personality provides valuable insights into users’ emotions and behaviors, with applications in psychological counseling, personalized recommendations, and social analysis. However, most existing personality detection methods treat traits as independent variables and rely on simple aggregation, lacking psychological interpretability. To address this gap, we propose a Type Dynamics-driven Personality Detection Model (TPD), which integrates psychological theory with deep learning to capture the synergistic interactions among personality traits under social contexts. Specifically, TPD constructs a multi-level graph attention mechanism to jointly model semantic relations between user posts and interdependencies among traits, revealing the internal cognitive dynamics of personality expression. To further capture the influence of external environments on trait expression, TPD introduces a GRU-based attention mechanism that models how external stimuli shape trait manifestation over time. In addition, by identifying core posts representative of each trait and leveraging large language models (LLMs) for contextual explanation, TPD provides interpretable user-centric analyses of online environments. Extensive experiments conducted on two real-world datasets demonstrate superior performance over seven state-of-the-art methods for personality detection, establishing a psychologically grounded framework for social-media-based personality detection. Our code is available at <a href="https://github.com/lambdarw/TPD">https://github.com/lambdarw/TPD</a>.</p>

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Type dynamics theory-driven personality detection with LLM-enhanced social profiling

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Ruwen Zhang,
  • Bo Liu,
  • Xiaorong Hao,
  • Xinhui Huang,
  • Jiuxin Cao

摘要

Personality provides valuable insights into users’ emotions and behaviors, with applications in psychological counseling, personalized recommendations, and social analysis. However, most existing personality detection methods treat traits as independent variables and rely on simple aggregation, lacking psychological interpretability. To address this gap, we propose a Type Dynamics-driven Personality Detection Model (TPD), which integrates psychological theory with deep learning to capture the synergistic interactions among personality traits under social contexts. Specifically, TPD constructs a multi-level graph attention mechanism to jointly model semantic relations between user posts and interdependencies among traits, revealing the internal cognitive dynamics of personality expression. To further capture the influence of external environments on trait expression, TPD introduces a GRU-based attention mechanism that models how external stimuli shape trait manifestation over time. In addition, by identifying core posts representative of each trait and leveraging large language models (LLMs) for contextual explanation, TPD provides interpretable user-centric analyses of online environments. Extensive experiments conducted on two real-world datasets demonstrate superior performance over seven state-of-the-art methods for personality detection, establishing a psychologically grounded framework for social-media-based personality detection. Our code is available at https://github.com/lambdarw/TPD.