Dark personality traits have long been associated with antisocial and toxic online behaviors, yet their relationship with observable online activity remains unclear. We investigate the association between validated dark personality measures, self-reported experiences of online incivility, and linguistic and behavioral features extracted from real-world user activity. To this end, we developed a Web application that securely links responses to validated psychological questionnaires collected via Amazon Mechanical Turk with participants' Reddit activity. This yielded a dataset of nearly 57K comments (2.2M tokens) from 114 users, represented through a broad set of linguistic and behavioral features. Our analyses reveal a clear distinction between self-reported and observed behavior. Dark personality traits show consistent associations with self-reported engagement in uncivil interactions. However, no validated dark personality dimension significantly predicts text-derived toxicity or linguistic features. In contrast, self-reported experiences of engaging in or being targeted by toxic behavior are robustly reflected in users' language, exhibiting consistent associations with measures of negativity, moral framing, and emotional intensity. Taken together, these findings highlight a gap between stable personality traits and their manifestation in surface-level linguistic signals. While computational features effectively capture behavioral engagement in online incivility, they do not provide reliable proxies for underlying personality constructs within the present framework. Our results underscore the importance of grounding computational approaches in validated psychological measures and point to the need for richer, context-aware representations to better understand the relationship between personality and online behavior.

翻译：黑暗人格特质长期被认为与反社会及网络毒性行为相关，然而其与可观测网络行为之间的关联仍不明确。本研究探究了经验证的黑暗人格测量指标、自我报告的网络不文明行为经历，以及从真实用户活动中提取的语言与行为特征之间的关联。为此，我们开发了一款网络应用程序，通过Amazon Mechanical Turk收集的经验证心理问卷回应与参与者的Reddit活动数据进行安全关联。由此构建了包含114名用户近57,000条评论（220万词元）的数据集，并通过广泛的语言与行为特征进行表征。分析揭示出自报告行为与观测行为之间存在显著差异：黑暗人格特质表现出与自我报告的不文明互动参与度的一致性关联，但经验证的黑暗人格维度均未能显著预测基于文本的毒性或语言特征。相反，自我报告从事或遭遇毒性行为的经历在用户语言中体现显著，与消极性、道德框架及情感强度等指标呈稳定关联。综合而言，这些发现揭示了稳定人格特质与其表层语言信号表现之间的鸿沟。虽然计算特征能有效捕捉网络不文明行为的行为参与度，但在当前框架下无法为潜在人格构念提供可靠代理指标。本研究结果强调了基于验证心理测量方法构建计算模型的重要性，同时表明需发展更丰富的情境化表征以深入理解人格与网络行为之间的关系。