Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives

TL;DR

This paper introduces a Dual Enhanced Network that jointly models long-term stable and short-term dynamic personality traits from social media text, improving the accuracy of personality detection.

Contribution

It proposes a novel model that integrates both long-term and short-term personality representations, addressing a gap in existing research.

Findings

The DEN model outperforms existing methods on two datasets.

Integrating both personality aspects improves detection accuracy.

Both stable traits and dynamic states are crucial for comprehensive personality understanding.

Abstract

Textual personality detection aims to identify personality characteristics by analyzing user-generated content on social media platforms. Extensive psychological literature highlights that personality encompasses both long-term stable traits and short-term dynamic states. However, existing studies often concentrate only on either long-term or short-term personality representations, neglecting the integration of both aspects. This limitation hinders a comprehensive understanding of individuals' personalities, as both stable traits and dynamic states are vital. To bridge this gap, we propose a Dual Enhanced Network (DEN) to jointly model users' long-term and short-term personality traits. In DEN, the Long-term Personality Encoding module models stable long-term personality traits by analyzing consistent patterns in the usage of psychological entities. The Short-term Personality Encoding module captures dynamic short-term personality states by modeling the contextual information of individual posts in real-time. The Bi-directional Interaction module integrates both aspects of personality, creating a cohesive and comprehensive representation of the user's personality. Experimental results on two personality detection datasets demonstrate the effectiveness of the DEN model and underscore the importance of considering both stable and dynamic aspects of personality in textual personality detection.