As text-based computer-mediated communication (CMC) increasingly structures everyday interaction, a central question re-emerges with new urgency: How do users reconstruct nonverbal expression in environments where embodied cues are absent? This paper provides a systematic, theory-driven account of electronic nonverbal cues (eNVCs) - textual analogues of kinesics, vocalics, and paralinguistics - in public microblog communication. Across three complementary studies, we advance conceptual, empirical, and methodological contributions. Study 1 develops a unified taxonomy of eNVCs grounded in foundational nonverbal communication theory and introduces a scalable Python toolkit for their automated detection. Study 2, a within-subject survey experiment, offers controlled causal evidence that eNVCs substantially improve emotional decoding accuracy and lower perceived ambiguity, while also identifying boundary conditions, such as sarcasm, under which these benefits weaken or disappear. Study 3, through focus group discussions, reveals the interpretive strategies users employ when reasoning about digital prosody, including drawing meaning from the absence of expected cues and defaulting toward negative interpretations in ambiguous contexts. Together, these studies establish eNVCs as a coherent and measurable class of digital behaviors, refine theoretical accounts of cue richness and interpretive effort, and provide practical tools for affective computing, user modeling, and emotion-aware interface design. The eNVC detection toolkit is available as a Python and R package at https://github.com/kokiljaidka/envc.

翻译：随着基于文本的计算机中介通信日益主导日常互动，一个核心问题再次凸显：用户在缺乏具身线索的环境中如何重构非语言表达？本文针对公开微博通信中的电子非语言线索（eNVCs）——即肢体语言、声音特征和副语言在文本中的模拟——提供了系统性的理论驱动阐释。通过三项互补研究，我们推进了概念、实证和方法论层面的贡献。研究1基于基础非语言通信理论建立了eNVCs的统一分类体系，并引入了一套可扩展的Python工具包用于其自动化检测。研究2通过一项被试内调查实验，提供了受控因果证据，表明eNVCs显著提升情绪解码准确率并降低感知模糊性，同时识别出边界条件（如讽刺）下这些优势减弱或消失的情况。研究3通过焦点小组讨论揭示了用户推理数字韵律时所采用的解释策略，包括从预期线索的缺失中提取含义，以及在模糊情境下默认倾向负面解读。综合来看，这些研究将eNVCs确立为一类连贯且可量化的数字行为，完善了线索丰富性与解释努力的理论阐述，并为情感计算、用户建模和情绪感知界面设计提供了实用工具。eNVC检测工具包以Python和R包形式提供，地址为https://github.com/kokiljaidka/envc。