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The minute-scale dynamics of online emotions reveal the effects of affect labeling

Abstract

Putting one’s feelings into words (also called affect labeling) can attenuate positive and negative emotions. Here, we track the evolution of specific emotions for 74,487 Twitter users by analysing the emotional content of their tweets before and after they explicitly report experiencing a positive or negative emotion. Our results describe the evolution of emotions and their expression at the temporal resolution of one minute. The expression of positive emotions is preceded by a short, steep increase in positive valence and followed by short decay to normal levels. Negative emotions, however, build up more slowly and are followed by a sharp reversal to previous levels, consistent with previous studies demonstrating the attenuating effects of affect labeling. We estimate that positive and negative emotions last approximately 1.25 and 1.5 h, respectively, from onset to evanescence. A separate analysis for male and female individuals suggests the potential for gender-specific differences in emotional dynamics.

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Fig. 1: Measuring changing valence levels from language before and after affect labeling.
Fig. 2: Time series of observed valence values across all individuals.
Fig. 3: Curve-fitting results of the smoothed mean valence values.
Fig. 4: Time series of observed valence CIs versus null-model CIs at 10 min increments.
Fig. 5: Robustness analysis.
Fig. 6: Gender differentiated time series of mean valence values.
Fig. 7: Regression discontinuity analysis of male and female time series in negative and positive affect labeling groups.

Data availability

The Twitter content data that support the findings of this study are publicly available from Twitter, but cannot be distributed by the authors. The authors provide the Twitter identification codes of all tweets used in this analysis to allow for retrieval of their content from the Twitter application programming interface. All other data are available from the authors upon reasonable request.

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Acknowledgements

R.F. acknowledges the support from the NSFC (grant number 71501005). J.B. thanks the Defense Advanced Research Projects Agency (NGS2 2016 D17AC00005), the National Science Foundation (SMA-SBE: 1636636), the Economic Development Administration (EDA/ED17HDQ3120040), Wageningen University (the Netherlands) and the ISI Foundation (Turin, Italy) for support. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Special thanks go to F. Radicchi and C. Scheffer for insightful comments, and R. B. Correia, who kindly set up our Twitter timeline database based on B. Gonçalves’ Twitter data collection.

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R.F. and J.B. defined the research methodology, which O.V., I.A.v.d.L. and M.S. helped design. R.F. and J.B. collected the data. R.F., O.V. and J.B. conducted the analysis. A.V. designed and implemented the gender classifier. A.B. provided statistical advice. I.A.v.d.L. and M.S. conducted a literature review. R.F., O.V., I.A.v.d.L., M.S. and J.B. interpreted the results. R.F., O.V., A.B. and J.B. co-authored the manuscript text.

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Correspondence to Rui Fan or Johan Bollen.

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Fan, R., Varol, O., Varamesh, A. et al. The minute-scale dynamics of online emotions reveal the effects of affect labeling. Nat Hum Behav 3, 92–100 (2019). https://doi.org/10.1038/s41562-018-0490-5

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