arXiv 2404.150672025-09-29cs.CL

面向社交媒体增强文本人格检测：整合长期与短期视角

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

School of Computer Science and Technology, Dalian University of Technology, Dalian, China（计算机科学与技术学院，大连理工大学，大连，中国）；Criminal Investigation Police University of China, Shenyang, China（中国刑事侦查警察大学，沈阳，中国）；School of Foreign Languages, Dalian University of Technology, Dalian, China（外语学院，大连理工大学，大连，中国）

Haohao Zhu, Xiaokun Zhang, Junyu Lu, Youlin Wu, Zewen Bai, Changrong Min, Liang Yang, Bo Xu, Dongyu Zhang, Hongfei Lin

备注：: This work has been submitted to the IEEE for possible publication

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AI总结：

针对文本人格检测中长期特质与短期状态分离的问题，提出双重增强网络 DEN，通过长期、短期编码及双向交互获得完整人格表征，并在两个数据集上验证有效。

摘要：

文本人格检测旨在通过分析社交媒体平台上用户生成的内容来识别人格特征。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，现有研究往往只关注长期或短期人格表征中的某一方面，忽视了对二者的整合。这一局限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。为弥合这一差距，我们提出一种双重增强网络（Dual Enhanced Network，DEN），以联合建模用户的长期和短期人格特质。在 DEN 中，长期人格编码模块通过分析心理实体使用中的一致模式，对稳定的长期人格特质进行建模；短期人格编码模块通过实时建模单条帖子的上下文信息，捕捉动态的短期人格状态；双向交互模块整合人格的两个方面，形成连贯且全面的用户人格表征。在两个人格检测数据集上的实验结果证明了 DEN 模型的有效性，并强调了在文本人格检测中同时考虑人格稳定与动态方面的重要性。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。