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

Nature Human Behaviourvolume 3pages92100 (2019) | Download Citation


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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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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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.

Author information


  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

    • Rui Fan
  2. Center for Complex Network Research, Northeastern University, Boston, MA, USA

    • Onur Varol
  3. Center for Complex Networks and Systems Research, Indiana University, Bloomington, IN, USA

    • Ali Varamesh
    • , Alexander Barron
    •  & Johan Bollen
  4. Wageningen University, Wageningen, the Netherlands

    • Ingrid A. van de Leemput
    • , Marten Scheffer
    •  & Johan Bollen
  5. Cognitive Science Program, Indiana University, Bloomington, IN, USA

    • Johan Bollen


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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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The authors declare no competing interests.

Corresponding authors

Correspondence to Rui Fan or Johan Bollen.

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