Understanding the temporal dynamics of affect is crucial for our understanding human emotions in general. In this study, we empirically test a computational model of affective dynamics by analyzing a large-scale dataset of Facebook status updates using text analysis techniques. Our analyses support the central assumptions of our model: After stimulation, affective states, quantified as valence and arousal, exponentially return to an individual-specific baseline. On average, this baseline is at a slightly positive valence value and at a moderate arousal point below the midpoint. Furthermore, affective expression, in this case posting a status update on Facebook, immediately pushes arousal and valence towards the baseline by a proportional value. These results are robust to the choice of the text analysis technique and illustrate the fast timescale of affective dynamics through social media text. These outcomes are of high relevance for affective computing, the detection and modeling of collective emotions, the refinement of psychological research methodology, and the detection of abnormal, and potentially pathological, individual affect dynamics.

中文翻译：

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社交媒体上情感表达的个体动力学

理解情感的时间动态对于我们总体上理解人类的情感至关重要。在这项研究中，我们通过使用文本分析技术分析Facebook状态更新的大规模数据集，以经验方式测试情感动力学的计算模型。我们的分析支持我们模型的主要假设：刺激后，被量化为效价和唤醒的情感状态呈指数返回个人特定基线。平均而言，该基线处于略为正的化合价值，并且处于中点以下的中等唤醒点。此外，情感表达（在这种情况下在Facebook上发布状态更新）会立即按比例值将唤醒和效价推向基线。这些结果对于选择文本分析技术是有力的，并说明了通过社交媒体文本进行情感动态分析的快速时标。这些结果与情感计算，集体情感的检测和建模，心理研究方法的完善以及异常和潜在病理性的个人情感动态的检测具有高度相关性。