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

Nature Human Behaviourvolume 3pages92100 (2019) | Download Citation

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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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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References

  1. 1.

    Shariff, A. F. & Tracy, J. L. What are emotion expressions for? Curr. Dir. Psychol. Sci. 20, 395–399 (2011).

  2. 2.

    Torre, J. B. & Lieberman., M. D. Putting feelings into words: affect labeling as implicit emotion regulation. Emot. Rev. 10, 116–124 (2018).

  3. 3.

    Lieberman, M. D., Inagaki, T. K., Tabibnia, G. & Crockett, M. J. Subjective responses to emotional stimuli during labeling, reappraisal, and distraction. Emotion 11, 468–480 (2011).

  4. 4.

    Constantinou, E., Van Den Houte, M., Bogaerts, K., Van Diest, I. & Van den Bergh, O. Can words heal? Using affect labeling to reduce the effects of unpleasant cues on symptom reporting. Front. Psychol. 5, 807 (2014).

  5. 5.

    Taylor, S. F., Phan, K. L., Decker, L. R. & Liberzon, I. Subjective rating of emotionally salient stimuli modulates neural activity. NeuroImage 18, 650–659 (2003).

  6. 6.

    Thomassin, K., Morelen, D. & Suveg, C. Motion reporting using electronic diaries reduces anxiety symptoms in girls with emotion dysregulation. J. Contemp. Psychother. 42, 207–213 (2012).

  7. 7.

    Kircanski, K., Lieberman, M. D. & Craske, M. G. Feelings into words. Psychol. Sci. 23, 1086–1091 (2012).

  8. 8.

    Niles, A. N., Craske, M. G., Lieberman, M. D. & Hur., C. Affect labeling enhances exposure effectiveness for public speaking anxiety. Behav. Res. Ther. 68, 27–36 (2015).

  9. 9.

    Niles, A. N., Haltom, K. E. B., Lieberman, M. D., Hur, C. & Stanton, A. L. Writing content predicts benefit from written expressive disclosure: evidence for repeated exposure and self-affirmation. Cogn. Emot. 30, 258–274 (2016).

  10. 10.

    Lieberman, M. D. et al. Putting feelings into words: affect labeling disrupts amygdala activity to affective stimuli. Psychol. Sci. 18, 421–428 (2007).

  11. 11.

    Mauss, I. B. & Robinson, M. D. Measures of emotion: a review. Cogn. Emot. 23, 209–237 (2009).

  12. 12.

    Kahneman, D. & Krueger, A. B. Developments in the measurement of subjective well-being. J. Econ. Perspect. 20, 3–24 (2006).

  13. 13.

    Probst, T., Pryss, R., Langguth, B. & Schlee, W.Emotion dynamics and tinnitus: daily life data from the “trackyourtinnitus” application.Sci. Rep. 6, 31166 (2016).

  14. 14.

    Phan, K. L., Wager, T., Taylor, S. F. & Liberzon, I. Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and FMRI. NeuroImage 16, 331–348 (2002).

  15. 15.

    Ochsner, K. N., Bunge, S. A., Gross, J. J. & Gabrieli., J. D. E. Rethinking feelings: an FMRI study of the cognitive regulation of emotion. J. Cogn. Neurosci. 14, 1215–1229 (2002).

  16. 16.

    Fossati, P. et al. In search of the emotional self: an FMRI study using positive and negative emotional words. Am. J. Psychiatry 160, 1938–1945 (2003).

  17. 17.

    Andreassi, J. L. Psychophysiology: Human Behavior and Physiological Response (Psychology Press, London, 2013).

  18. 18.

    Nummenmaa, L., Glerean, E., Hari, R. & Hietanen, J. K. Bodily maps of emotions. Proc. Natl Acad. Sci. USA 111, 646–651 (2014).

  19. 19.

    McRae, K., Ochsner, K. N., Mauss, I. B., Gabrieli, J. J. D. & Gross, J. J. Gender differences in emotion regulation: an FMRI study of cognitive reappraisal. Group Process. Intergroup Relat. 11, 143–162 (2008).

  20. 20.

    Koelsch, S., Fritz, T., Müller, K. & Friederici, A. D. Investigating emotion with music: an FMRI study. Hum. Brain Mapp. 27, 239–250 (2006).

  21. 21.

    Prasad, D. K., Liu, S., Chen, S.-H. A. & Quek, C. Sentiment analysis using EEG activities for suicidology. Expert Syst. Appl. 103, 206–217 (2018).

  22. 22.

    Pennebaker, J. W. Emotion, Disclosure and Health (American Psychological Association Books, Washington DC, 1995).

  23. 23.

    Kennedy-Moore, E. & Watson, J. C. How and when does emotional expression help? Rev. Gen. Psychol. 5, 187–212 (2001).

  24. 24.

    Ford, B. Q., Lam, P., John, O. P. & Mauss, I. B. The psychological health benefits of accepting negative emotions and thoughts: aboratory, diary, and longitudinal evidence. J. Pers. Soc. Psychol. http://doi.org/10.1037/pspp0000157 (2017).

  25. 25.

    Thistlethwaite, D. L. & Campbell, D. T. Regression-discontinuity analysis: an alternative to the ex post facto experiment. J. Educ. Psychol. 51, 309–317 (1960).

  26. 26.

    Beasley, A. & Mason, W. Emotional states vs. emotional words in social media. In Proc. ACM Web Science Conference 31 (ACM, 2015).

  27. 27.

    Ziemer, K. S. & Korkmaz, G. Using text to predict psychological and physical health: a comparison of human raters and computerized text analysis. Comput. Hum. Behav. 76, 122–127 (2017).

  28. 28.

    Cowen, A. S. & Keltner, D. Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proc. Natl Acad. Sci. USA 114, E7900–E7909 (2017).

  29. 29.

    Bollen, J., Mao, H. & Zeng, X. Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011).

  30. 30.

    Hutto, C. J. & Gilbert, E. VADER: a parsimonious rule-based model for sentiment analysis of social media text. In Proc. Eighth International AAAI Conference on Weblogs and Social Media 216–225 (AAAI, 2014).

  31. 31.

    Bollen, J., Mao, H. & Pepe, A. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proc. Fifth International AAAI Conference on Weblogs and Social Media 450–453 (AAAI, 2011).

  32. 32.

    Yang, C. & Srinivasan, P. Life satisfaction and the pursuit of happiness on twitter. PLoS ONE 11, 1–30 (2016).

  33. 33.

    Warriner, A. B., Kuperman, V. & Brysbaert, M. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav. Res. Methods 45, 1191–1207 (2013).

  34. 34.

    Darwin, C. The Expression of the Emotions in Man and Animals (John Murray, London, 1872).

  35. 35.

    Russell, J. A circumplex model of affect.J. Pers. Soc. Psychol. 39, 1161–1178 (1980).

  36. 36.

    Russell, J. A. & Mehrabian, A. Evidence for a three-factor theory of emotions. J. Res. Pers. 11, 273–294 (1977).

  37. 37.

    Mehrabian, A. Basic Dimensions for a General Psychological Theory: Implications for Personality, Social, Environmental, and Developmental Studies (Oelgeschlager, Gunn & Hain, Cambridge, 1980).

  38. 38.

    Plutchik, R. & Conte, H. R. Circumplex Models of Personality and Emotions (American Psychological Association, Washington DC, 1997).

  39. 39.

    Ekman, P. Handbook of Cognition and Emotion (eds Dalgleish, T. & Power, M.) Ch. 3 (John Wiley and Sons, Chichester, 1999).

  40. 40.

    Lang, P. J., Bradley, M. M. & Cuthbert, B. N. International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual Technical Report A-8 (Univ. Florida, 2008).

  41. 41.

    Golder, S. A. & Macy, M. W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 1878–1881 (2011).

  42. 42.

    Ribeiro, F. N., Araújo, M., Gonçalves, P., Gonçalves, M. A. & Benevenuto, F. SentiBench—a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5, 23 (2016).

  43. 43.

    Dodds, P. S. et al. Human language reveals a universal positivity bias. Proc. Natl Acad. Sci. USA 112, 2389–2394 (2015).

  44. 44.

    Grigg, O. A., Farewell, V. T. & Spiegelhalter, D. J. Use of risk-adjusted CUSUM and RSPRT charts for monitoring in medical contexts. Stat. Methods Med. Res. 12, 147–170 (2003).

  45. 45.

    Kring, A. M. & Gordon, A. H. Sex differences in emotion: expression, experience, and physiology. J. Pers. Soc. Psychol. 74, 686–703 (1998).

  46. 46.

    McDuff, D., Kodra, E., Kaliouby, Rel & LaFrance, M. A large-scale analysis of sex differences in facial expressions. PLoS ONE 12, 1–11 (2017).

  47. 47.

    Li, J., Ritter, A. & Hovy, E. Weakly supervised user profile extraction from Twitter. In Proc. 52nd Annual Meeting of the Association for Computational Linguistics 165–174 (ACL, 2014).

  48. 48.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

  49. 49.

    Niculescu-Mizil, A. & Caruana, R. Predicting good probabilities with supervised learning. In Proc. 22nd International Conference on Machine Learning 625–632 (ACM, 2005).

  50. 50.

    Pennington, J., Socher, R. & Manning, C. D. Glove: global vectors for word representation. In Proc. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1532–1543 (ACL, 2014).

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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.

Author information

Affiliations

  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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Contributions

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.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Rui Fan or Johan Bollen.

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DOI

https://doi.org/10.1038/s41562-018-0490-5

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