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

arXiv: https://arxiv.org/abs/2404.15067 · HTML: https://mlsanity.com/paper/2404.15067/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Improving Dialog Systems for Negotiation with Personality Modeling](https://mlsanity.com/paper/2010.09954/) (2020)
- [Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings](https://mlsanity.com/paper/1911.09304/) (2019)
- [PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection](https://mlsanity.com/paper/2310.20256/) (2023)
- [Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models](https://mlsanity.com/paper/1907.06333/) (2019)
- [A Survey on Personality-Aware Recommendation Systems](https://mlsanity.com/paper/2101.12153/) (2021)
- [Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection](https://mlsanity.com/paper/2212.01515/) (2022)
- [Psycholinguistic Tripartite Graph Network for Personality Detection](https://mlsanity.com/paper/2106.04963/) (2021)
- [On Text-based Personality Computing: Challenges and Future Directions](https://mlsanity.com/paper/2212.06711/) (2022)

## Built on by (most cited first, 6 of 6)

- [LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection](https://mlsanity.com/paper/2504.02146/) (2025)
- [Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detection](https://mlsanity.com/paper/2408.08551/) (2024)
- [Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition](https://mlsanity.com/paper/2502.04960/) (2025)
- [EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling](https://mlsanity.com/paper/2509.02450/) (2025)
- [HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detection](https://mlsanity.com/paper/2510.09893/) (2025)
- [Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition](https://mlsanity.com/paper/2607.08374/) (2026)

## Abstract (arXiv)

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.