Abstract
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.
Highlights
Emotions have profound influence on human cognition and behavior across many domains [1]: They bias memory, perception, and decision making [2] and provide feedback for past and guidance for current behavior [3]
(2020) 9:1 an emotion-inducing event the cognitive and behavioral impact of emotions can be expected to last. Is it enough to follow Jefferson’s advice and count to ten or a hundred, in order to avoid the negative effects of anger? A second question concerns the baseline to which emotions return after an emotion-inducing event: Do we return to a state of completely neutral emotions or are there individual baselines shifted away from the middle point? expressing emotions can have an effect on affective dynamics: If we ignore Jefferson’s advice and speak our minds while we are still angry, will this expressive act relax our emotional state—and if so, by how much?
5 Conclusions Our analysis of the temporal dynamics of affective expression on Facebook validates our model of affective dynamics
Summary
Emotions have profound influence on human cognition and behavior across many domains [1]: They bias memory, perception, and decision making [2] and provide feedback for past and guidance for current behavior [3]. Individual i started to write a status update at time tbefore, expressing an emotional state xi(tbefore), quantified as valence or arousal. After the expression of the emotion, the state of the individual instantly changes to a value adjusted by the distance to the baseline multiplied by a constant factor k This equation models a stable regulation towards the baseline when 0 < k < 1, which has been previously observed in experiments [12]. This kind of exponential relaxation has been observed in previous empirical research [10, 12], serving as the starting point of our individual dynamics model In this case, when the value of xi(t) is below the baseline, the value of xi(t) increases over time, approaching the baseline from below.
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