mlsanityData to 7 Oct 2026

Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection

arXiv 2212.01515 ↗First posted 3 Dec 2022cs.CLcited by 14

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Abstract (arXiv)

Predicting personality traits based on online posts has emerged as an important task in many fields such as social network analysis. One of the challenges of this task is assembling information from various posts into an overall profile for each user. While many previous solutions simply concatenate the posts into a long document and then encode the document by sequential or hierarchical models, they introduce unwarranted orders for the posts, which may mislead the models. In this paper, we propose a dynamic deep graph convolutional network (D-DGCN) to overcome the above limitation. Specifically, we design a learn-to-connect approach that adopts a dynamic multi-hop structure instead of a deterministic structure, and combine it with a DGCN module to automatically learn the connections between posts. The modules of post encoder, learn-to-connect, and DGCN are jointly trained in an end-to-end manner. Experimental results on the Kaggle and Pandora datasets show the superior performance of D-DGCN to state-of-the-art baselines. Our code is available at https://github.com/djz233/D-DGCN.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 3 Dec 2022.

Builds on

26 of 40 references.

References · 26

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14 more
  1. 2018Graph Convolutional Networks for Text Classificationcited by 345
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Citing papers per year: 2024: 6; 2025: 7; 2026: 1.

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  1. 2024LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Modelcited by 21
  2. 2024Identifying Multiple Personalities in Large Language Models with External Evaluationcited by 10
  3. 2024Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectivescited by 6
  4. 2024EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detectioncited by 6
  5. 2025Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detectioncited by 6
  6. 2025LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detectioncited by 5
  7. 2024Integrating Multi-view Analysis: Multi-view Mixture-of-Expert for Textual Personality Detectioncited by 3
  8. 2025EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modellingcited by 2
  9. 2025Prompting-in-a-Series: Psychology-Informed Contents and Embeddings for Personality Recognition With Decoder-Only Modelscited by 2
  10. 2025HIPPD: Brain-Inspired Hierarchical Information Processing for Personality Detectioncited by 1
  11. 2024Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networkscited by 0
  12. 2025Cognitive Alignment in Personality Reasoning: Leveraging Prototype Theory for MBTI Inferencecited by 0
2 more
  1. 2025Ask, Answer, and Detect: Role-Playing LLMs for Personality Detection with Question-Conditioned Mixture-of-Expertscited by 0
  2. 2026Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognitioncited by 0