Textual personality detection aims to identify personality characteristics by analyzing user-generated content toward social media platforms. Numerous psychological literature highlighted 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, without effectively combining 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 for textual personality detection. In DEN, a Long-term Personality Encoding is devised to effectively model long-term stable personality traits. Short-term Personality Encoding is presented to capture short-term dynamic personality states. The Bi-directional Interaction component facilitates the integration of both personality aspects, allowing for a comprehensive representation of the user's personality. Experimental results on two personality detection datasets demonstrate the effectiveness of the DEN model and the benefits of considering both the dynamic and stable nature of personality characteristics for textual personality detection.

翻译：文本人格检测旨在通过分析用户在社交媒体平台上生成的内容来识别其人格特征。大量心理学文献强调，人格既包含长期稳定的特质，也包含短期动态的状态。然而，现有研究往往仅关注长期或短期的人格表征，未能有效结合这两个方面。这种局限性阻碍了对个体人格的全面理解，因为稳定特质和动态状态都至关重要。为弥补这一空白，我们提出了一种双增强网络（DEN），用于联合建模用户的长期与短期人格进行文本人格检测。在DEN中，我们设计了长期人格编码模块，以有效建模长期稳定的人格特质；同时提出了短期人格编码模块，用于捕获短期动态的人格状态。双向交互组件促进了这两个人格方面的整合，使得用户人格的综合表征成为可能。在两个人格检测数据集上的实验结果表明，DEN模型的有效性，以及在文本人格检测中同时考虑人格特性的动态与稳定性质的益处。