一种用于从社交媒体进行可解释的基于文本的人格评估的计算框架
A Computational Framework for Interpretable Text-Based Personality Assessment from Social Media
AI总结：
针对带人格标签的社交媒体文本数据集稀缺、人格心理学与NLP脱节导致模型有效性和可解释性不足的问题，本研究构建了两个大规模Reddit人格数据集，并提出可解释的SIMPA框架，通过匹配用户陈述与问卷条目实现媲美人类的评估，且具备跨领域通用性。
摘要：
人格指的是个体在行为、思维和感受方面的差异。随着数字足迹（尤其是来自社交媒体的数字足迹）的可获取性不断提升，自动化人格评估方法变得愈发重要。自然语言处理（NLP）能够分析非结构化文本数据以识别人格指标，但本研究聚焦的两大核心挑战仍然存在：大规模带人格标签的数据集稀缺，以及人格心理学与NLP之间的脱节，这限制了模型的有效性和可解释性。为应对这些挑战，本论文提出了两个从Reddit（一个以用户匿名性和讨论多样性著称的平台）收集的数据集——MBTI9k和PANDORA。其中PANDORA数据集包含来自10000多名用户的1700万条评论，整合了MBTI和大五人格模型以及人口统计信息，克服了数据规模、质量和标签覆盖度方面的局限。在这些数据集上开展的实验表明，人口统计变量会影响模型的有效性。为此，研究人员开发了SIMPA（Statement-to-Item Matching Personality Assessment，陈述-条目匹配人格评估）框架，这是一种可解释人格评估的计算框架，能够将用户生成的陈述与经过验证的问卷条目进行匹配。通过运用机器学习和语义相似度技术，SIMPA在保持高可解释性和效率的同时，能提供与人类评估水平相当的人格评估结果。尽管聚焦于人格评估，SIMPA的通用性可延伸至该领域之外。其模型无关的设计、分层线索检测机制以及可扩展性，使其适用于涉及复杂标签分类体系和目标概念可变线索关联的各类研究与实际应用场景。
英文摘要：
Personality refers to individual differences in behavior, thinking, and feeling. With the growing availability of digital footprints, especially from social media, automated methods for personality assessment have become increasingly important. Natural language processing (NLP) enables the analysis of unstructured text data to identify personality indicators. However, two main challenges remain central to this thesis: the scarcity of large, personality-labeled datasets and the disconnect between personality psychology and NLP, which restricts model validity and interpretability. To address these challenges, this thesis presents two datasets -- MBTI9k and PANDORA -- collected from Reddit, a platform known for user anonymity and diverse discussions. The PANDORA dataset contains 17 million comments from over 10,000 users and integrates the MBTI and Big Five personality models with demographic information, overcoming limitations in data size, quality, and label coverage. Experiments on these datasets show that demographic variables influence model validity. In response, the SIMPA (Statement-to-Item Matching Personality Assessment) framework was developed - a computational framework for interpretable personality assessment that matches user-generated statements with validated questionnaire items. By using machine learning and semantic similarity, SIMPA delivers personality assessments comparable to human evaluations while maintaining high interpretability and efficiency. Although focused on personality assessment, SIMPA's versatility extends beyond this domain. Its model-agnostic design, layered cue detection, and scalability make it suitable for various research and practical applications involving complex label taxonomies and variable cue associations with target concepts.
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。
