Behavior Research Methods

, Volume 51, Issue 4, pp 1737–1753 | Cite as

The rippling dynamics of valenced messages in naturalistic youth chat

  • Seth FreyEmail author
  • Karsten Donnay
  • Dirk Helbing
  • Robert W. Sumner
  • Maarten W. Bos


Even though human behavior is largely driven by real-time feedback from others, this social complexity is underrepresented in psychological theory, largely because it is so difficult to isolate. In this work, we performed a quasi-experimental analysis of hundreds of millions of chat room messages between young people. This allowed us to reconstruct how—and on what timeline—the valence of one message affects the valence of subsequent messages by others. For the highly emotionally valenced chat messages that we focused on, we found that these messages elicited a general increase of 0.1 to 0.4 messages per minute. This influence started 2 s after the original message and continued out to 60 s. Expanding our focus to include feedback loops—the way a speaker’s chat comes back to affect him or her—we found that the stimulating effects of these same chat events started rippling back from others 8 s after the original message, to cause an increase in the speaker’s chat that persisted for up to 8 min. This feedback accounted for at least 1% of the bulk of chat. Additionally, a message’s valence affects its dynamics, with negative events feeding back more slowly and continuing to affect the speaker longer. By reconstructing the second-by-second dynamics of many psychosocial processes in aggregate, we captured the timescales at which they collectively ripple through a social system to drive system-level outcomes.




Author note

S.F. wishes to thank Michael Mäs, Emma Templeton, Beau Sievers, David Garcia, and Luke Chang for their ideas, guidance, and input. K.D. and D.H. acknowledge financial support from Minerva Grant #FA9550-14-1-0353 DEF (AFOSR) and ERC Advanced Investigator “Momentum” Grant #324247, respectively. S.F. acknowledges the support of the Neukom Institute for Computational Science. For access to the data, contact authors S.F. and M.W.B. This research was approved by the ETH Zurich Ethics Commission, IRB #EK 2014-N-55.

Supplementary material

13428_2018_1140_MOESM1_ESM.pdf (711 kb)
ESM 1 (PDF 710 kb)


  1. Algesheimer, R., Dholakia, U. M., & Herrmann, A. (2005). The social influence of brand community: Evidence from European car clubs. Journal of Marketing, 69, 19–34. Google Scholar
  2. Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106, 21544–21549. Google Scholar
  3. Arceneaux, K., Gerber, A. S., & Green, D. P. (2006). Comparing experimental and matching methods using a large-scale voter mobilization experiment. Political Analysis, 14, 37–62.Google Scholar
  4. Arceneaux, K., Gerber, A. S., & Green, D. P. (2010). A cautionary note on the use of matching to estimate causal effects: An empirical example comparing matching estimates to an experimental benchmark. Sociological Methods and Research, 39, 256–282. Google Scholar
  5. Bakshy, E., Eckles, D., & Bernstein, M. S. (2014). Designing and deploying online field experiments. In C.-W. Chung, A. Broder, K. Shim, & T. Suel (Eds.), Proceedings of the 23rd International Conference on World Wide Web (pp. 283–292). New York, NY, USA: ACM Press. Google Scholar
  6. Bakshy, E., Eckles, D., Yan, R., & Rosenn, I. (2012). Social influence in social advertising: Evidence from field experiments. In B. Faltings, K. Leyton-Brown, & P. Ipeirotis (Eds.), Proceedings of the 13th ACM Conference on Electronic Commerce (pp. 146–161). New York, NY, USA: ACM Press. Google Scholar
  7. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348, 1130–1132. Google Scholar
  8. Barsade, S. G. (2002). The ripple effect: Emotional contagion and its influence on group behavior. Administrative Science Quarterly, 47, 644–675. Google Scholar
  9. Beasley, A., & Mason, W. (2015). Emotional states vs. emotional words in social media. In D. De Roure, P. Burnap, & S. Halford (Eds.), Proceedings of the ACM Web Science Conference (Art. No. 31). New York, NY, USA: ACM Press.Google Scholar
  10. Beckage, N., Smith, L., & Hills, T. (2011). Small worlds and semantic network growth in typical and late talkers. PLoS ONE, 6, e19348. Google Scholar
  11. Beckner, C., Blythe, R., Bybee, J., Christiansen, M. H., Croft, W., Ellis, N. C., ... Schoenemann, T. (2009). Language is a complex adaptive system: Position paper. Language Learning, 59(Suppl.), 1–26.Google Scholar
  12. Beer, R. D. (2007). Dynamical systems and embedded cognition. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence (pp. 128–148). Cambridge, UK: Cambridge University Press.Google Scholar
  13. Bingham, G. P., & Wickelgren, E. A. (2008). Events and actions as dynamically molded spatiotemporal objects: A critique of the motor theory of biological motion perception. In T. F. Shipley & J. M. Zacks (Eds.), Understanding events: From perception to action (pp. 255–285). New York, NY, US: Oxford University Press.Google Scholar
  14. Blackwell, M., Iacus, S. M., King, G., & Porro, G. (2009). cem: Coarsened exact matching in Stata. Stata Journal, 9, 524–546.Google Scholar
  15. Bliss, C. A., Kloumann, I. M., Harris, K. D., Danforth, C. M., & Dodds, P. S. (2012). Twitter reciprocal reply networks exhibit assortativity with respect to happiness. Journal of Computational Science, 3, 388–397. Google Scholar
  16. Brechwald, W. A., & Prinstein, M. J. (2011). Beyond homophily: A decade of advances in understanding peer influence processes. Journal of Research on Adolescence, 21, 166–179. Google Scholar
  17. Brown, K. W., & Moskowitz, D. S. (1998a). Dynamic stability of behavior: The rhythms of our interpersonal lives. Journal of Personality, 66, 105–134. Google Scholar
  18. Brown, K. W., & Moskowitz, D. S. (1998b). It’s a function of time: A review of the process approach to behavioral medicine research. Annals of Behavioral Medicine, 20, 109–117. Google Scholar
  19. Campbell, S. W., & Kwak, N. (2010). Mobile communication and civic life: Linking patterns of use to civic and political engagement. Journal of Communication, 60, 536–555. Google Scholar
  20. Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception–behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910. Google Scholar
  21. Cheng, J., Danescu-Niculescu-Mizil, C., & Leskovec, J. (2015). Antisocial behavior in online discussion communities. In Ninth International AAAI Conference on Web and Social Media (pp. 61–70). Retrieved from
  22. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357, 370–379. Google Scholar
  23. Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621. Google Scholar
  24. Cialdini, R. B., Green, B. L., & Rusch, A. J. (1992). When tactical pronouncements of change become real change: The case of reciprocal persuasion. Journal of Personality and Social Psychology, 63, 30–40. Google Scholar
  25. Cohen, G. L., & Sherman, D. K. (2014). The psychology of change: Self-affirmation and social psychological intervention. Annual Review of Psychology, 65, 333–371. Google Scholar
  26. Cohen-Cole, E., & Fletcher, J. M. (2008). Detecting implausible social network effects in acne, height, and headaches: Longitudinal analysis. BMJ, 337, a2533. Google Scholar
  27. Coviello, L., Sohn, Y., Kramer, A. D. I., Marlow, C., Franceschetti, M., Christakis, N. A., & Fowler, J. H. (2014). Detecting emotional contagion in massive social networks. PLoS ONE, 9, e90315. Google Scholar
  28. Danescu-Niculescu-Mizil, C., Lee, L., Pang, B., & Kleinberg, J. (2012). Echoes of power: Language effects and power differences in social interaction. In A. Mille, F. Gandon, & J. Misselis (Eds.), Proceedings of the 21st International Conference on World Wide Web (pp. 699–708). New York, NY, USA: ACM Press. Google Scholar
  29. Davis, J. L., & Rusbult, C. E. (2001). Attitude alignment in close relationships. Journal of Personality and Social Psychology, 81, 65–84. Google Scholar
  30. DiFonzo, N., Beckstead, J. W., Stupak, N., & Walders, K. (2016). Validity judgments of rumors heard multiple times: The shape of the truth effect. Social Influence, 11, 22–39. Google Scholar
  31. Dishion, T. J., & Tipsord, J. M. (2011). Peer contagion in child and adolescent social and emotional development. Annual Review of Psychology, 62, 189–214. Google Scholar
  32. Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS ONE, 6, e26752. Google Scholar
  33. Doyle, G., & Frank, M. C. (2015). Shared common ground influences information density in microblog texts. In Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL (pp. 1587–1596).
  34. Doyle, G., Yurovsky, D., & Frank, M. C. (2016). A robust framework for estimating linguistic alignment in Twitter conversations. In J. Bourdeau, J. A. Hendler, & R. Nkambou Nkambou (Eds.), Proceedings of the 21st International Conference on World Wide Web (pp. 637–648). New York, NY, USA: ACM Press. Google Scholar
  35. Duggan, M., & Brenner, J. (2013). The demographics of social media users—2012. Pew Research Center.Google Scholar
  36. Eckles, D., & Bakshy, E. (2017). Bias and high-dimensional adjustment in observational studies of peer effects (Working article). arXiv:1706.04692Google Scholar
  37. Elman, J. (2005). Connectionist models of cognitive development: In which next? Trends in Cognitive Sciences, 9, 111–117.Google Scholar
  38. Festinger, L., & Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. Journal of Abnormal and Social Psychology, 58, 203–210. Google Scholar
  39. File, T., & Ryan, C. (2014). Computer and Internet use in the United States: 2013 (No. ACS-28). Washington, DC: US Census Bureau.Google Scholar
  40. Fowler, J. H., Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. BMJ, 337, 23–27.Google Scholar
  41. Fraley, R. C., & Hudson, N. W. (2013). Review of intensive longitudinal methods: An introduction to diary and experience sampling research. Journal of Social Psychology, 154, 89–91. Google Scholar
  42. Frey, S., Bos, M. W., & Sumner, R. W. (2017). Can you moderate an unreadable message? “Blind” content moderation via human computation. Human Computation, 4, 78–106. Google Scholar
  43. Garas, A., Garcia, D., Skowron, M., & Schweitzer, F. (2012). Emotional persistence in online chatting communities. Scientific Reports, 2, 402. Google Scholar
  44. Garcia, D., Kappas, A., Küster, D., & Schweitzer, F. (2016). The dynamics of emotions in online interaction. Open Science, 3, 160059. Google Scholar
  45. Gardner, M., & Steinberg, L. (2005). Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: An experimental study. Developmental Psychology, 41, 625–635. Google Scholar
  46. Gibson, J. J. (1966). The senses considered as perceptual systems. Boston, MA: Houghton Mifflin.Google Scholar
  47. Goldstone, R. L., & Lupyan, G. (2016). Discovering psychological principles by mining naturally occurring data sets. Topics in Cognitive Science, 8, 548–568. Google Scholar
  48. Gonçalves, B., & Sánchez, D. (2014). Crowdsourcing dialect characterization through Twitter. Retrieved April 11, 2018, from
  49. Greenfield, P., & Yan, Z. (2006). Children, adolescents, and the Internet: A new field of inquiry in developmental psychology. Developmental Psychology, 42, 391–394. Google Scholar
  50. Gummerus, J., Liljander, V., Weman, E., & Pihlström, M. (2012). Customer engagement in a Facebook brand community. Management Research Review, 35, 857–877. Google Scholar
  51. Gutnick, A. L., Robb, M., Takeuchi, L., & Kotler, J. (2010). Always connected: The new digital media habits of young children. New York, NY, USA: Joan Ganz Cooney Center at Sesame Workshop.Google Scholar
  52. Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016, August). Diachronic word embeddings reveal statistical laws of semantic change. Article presented at the conference of the Association for Computational Linguistics, Berlin, Germany.Google Scholar
  53. Hardin, C. D., & Higgins, E. T. (1996). Shared reality: How social verification makes the subjective objective. In R. M. Sorrentino & E. T. Higgins (Eds.), Handbook of motivation and cognition (Vol. 3, pp. 28–84). New York, NY: Guilford Press.Google Scholar
  54. Hari, R., Himberg, T., Nummenmaa, L., Hämäläinen, M., & Parkkonen, L. (2013). Synchrony of brains and bodies during implicit interpersonal interaction. Trends in Cognitive Sciences, 17, 105–106. Google Scholar
  55. Harrison, S. J., & Richardson, M. J. (2009). Horsing around: Spontaneous four-legged coordination. Journal of Motor Behavior, 41, 519–524. Google Scholar
  56. Heatherton, T. F. (2011). Neuroscience of self and self-regulation. Annual Review of Psychology, 62, 363–390. Google Scholar
  57. Hermans, R. C. J., Lichtwarck-Aschoff, A., Bevelander, K. E., Herman, C. P., Larsen, J. K., & Engels, R. C. M. E. (2012). Mimicry of food intake: The dynamic interplay between eating companions. PLoS ONE, 7, e31027. Google Scholar
  58. Hills, T. T., Maouene, J., Riordan, B., & Smith, L. B. (2010). The associative structure of language: Contextual diversity in early word learning. Journal of Memory and Language, 63, 259–273. Google Scholar
  59. Hills, T. T., Maouene, M., Maouene, J., Sheya, A., & Smith, L. (2009). Longitudinal analysis of early semantic networks: Preferential attachment or preferential acquisition? Psychological Science, 20, 729–739.Google Scholar
  60. Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2017). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15, 199–236. Google Scholar
  61. Hoeksma, J. B., Oosterlaan, J., & Schipper, E. M. (2004). Emotion regulation and the dynamics of feelings: A conceptual and methodological framework. Child Development, 75, 354–360. Google Scholar
  62. Huang, Y., Kendrick, K. M., & Yu, R. (2014). Conformity to the opinions of other people lasts for no more than 3 days. Psychological Science, 25, 1388–1393. Google Scholar
  63. Hughes, J. M., Foti, N. J., Krakauer, D. C., & Rockmore, D. N. (2012). Quantitative patterns of stylistic influence in the evolution of literature. Proceedings of the National Academy of Sciences, 109, 7682–7686. Google Scholar
  64. Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20, 1–24. Google Scholar
  65. Iacus, S. M., King, G., & Porro, G. (2015). A theory of statistical inference for matching methods in applied causal research (Working paper).Google Scholar
  66. Kandel, D. B. (1978). Homophily, selection, and socialization in adolescent friendships. American Journal of Sociology, 84, 427–436. Google Scholar
  67. Kashima, Y. (2008). A social psychology of cultural dynamics: Examining how cultures are formed, maintained, and transformed. Social and Personality Psychology Compass, 2, 107–120. Google Scholar
  68. Kashima, Y., Woolcock, J., & Kashima, E. S. (2000). Group impressions as dynamic configurations: The tensor product model of group impression formation and change. Psychological Review, 107, 914–942. Google Scholar
  69. King, G., Lucas, C., & Nielsen, R. (2014). The balance-sample size frontier in matching methods for causal inference. Political Science and Politics, 42, 11–22.Google Scholar
  70. Kleinsman, J., & Buckley, S. (2015). Facebook study: A little bit unethical but worth it? Journal of Bioethical Inquiry, 12, 179–182. Google Scholar
  71. Konvalinka, I., Xygalatas, D., Bulbulia, J., Schjødt, U., Jegindø, E.-M., Wallot, S., ... A. Roepstorff (2011). Synchronized arousal between performers and related spectators in a fire-walking ritual. Proceedings of the National Academy of Sciences, 108, 8514–8519.
  72. Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111, 8788–8790. Google Scholar
  73. Krosnick, J. A., & Judd, C. M. (1982). Transitions in social influence at adolescence: Who induces cigarette smoking? Developmental Psychology, 18, 359–368.Google Scholar
  74. Lang, M., Shaw, D. J., Reddish, P., Wallot, S., Mitkidis, P., & Xygalatas, D. (2015). Lost in the rhythm: Effects of rhythm on subsequent interpersonal coordination. Cognitive Science, 40, 1797–1815. Google Scholar
  75. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., ... Van Alstyne, M. (2009). Computational social science. Science, 323, 721–723.
  76. Leipold, B., Bermeitinger, C., Greve, W., Meyer, B., Arnold, M., & Pielniok, M. (2014). Short-term induction of assimilation and accommodation. Quarterly Journal of Experimental Psychology, 67, 2392–2408. Google Scholar
  77. Lewis, K., Gonzalez, M., & Kaufman, J. (2012). Social selection and peer influence in an online social network. Proceedings of the National Academy of Sciences, 109, 68–72. Google Scholar
  78. Liao, Q. V., & Fu, W.-T. (2013). Beyond the filter bubble: Interactive effects of perceived threat and topic involvement on selective exposure to information. In W. E. Mackay (Ed.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2359–2368). New York, NY, USA: ACM Press. Google Scholar
  79. Lind, A., Hall, L., Breidegard, B., Balkenius, C., & Johansson, P. (2014). Speakers’ acceptance of real-time speech exchange indicates that we use auditory feedback to specify the meaning of what we say. Psychological Science, 25, 1198–1205. Google Scholar
  80. Lind, A., Hall, L., Breidegard, B., Balkenius, C., & Johansson, P. (2015). Auditory feedback is used for self-comprehension: When we hear ourselves saying something other than what we said, we believe we said what we hear. Psychological Science, 26, 1978–1980. Google Scholar
  81. Madon, S., Jussim, L., & Eccles, J. (1997). In search of the powerful self-fulfilling prophecy. Journal of Personality and Social Psychology, 72, 791–809. Google Scholar
  82. Markus, H., & Wurf, E. (1987). The dynamic self-concept: A social psychological perspective. Annual Review of Psychology, 38, 299–337. Google Scholar
  83. Mason, W. A., Conrey, F. R., & Smith, E. R. (2007). Situating social influence processes: Dynamic, multidirectional flows of influence within social networks. Personality and Social Psychology Review, 11, 279–300. Google Scholar
  84. McCann, C. D., Higgins, E. T., & Fondacaro, R. A. (1991). Primacy and recency in communication and self-persuasion: How successive audiences and multiple encodings influence subsequent evaluative judgments. Social Cognition, 9, 47–66. Google Scholar
  85. Miller, M. K. (2015). The uses and abuses of matching (Working article).Google Scholar
  86. Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013). The geography of happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE, 8, e64417. Google Scholar
  87. Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., & Vespignani, A. (2013). The Twitter of Babel: Mapping world languages through microblogging platforms. PLoS ONE, 8, e61981. Google Scholar
  88. Morf, C. C., & Rhodewalt, F. (2001). Unraveling the paradoxes of narcissism: A dynamic self-regulatory processing model. Psychological Inquiry, 12, 177–196.Google Scholar
  89. Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of “personally identifiable information” Communications of the ACM, 53, 24–26. Google Scholar
  90. Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention. Journal of Experimental Psychology: General, 106, 226–254. Google Scholar
  91. Noel, H., & Nyhan, B. (2011). The “unfriending” problem: The consequences of homophily in friendship retention for causal estimates of social influence. Social Networks, 33, 211–218. Google Scholar
  92. Nowak, A., Szamrej, J., & Latané, B. (1990). From private attitude to public opinion: A dynamic theory of social impact. Psychological Review, 97, 362–376. Google Scholar
  93. Olson, C. K. (2010). Children’s motivations for video game play in the context of normal development. Review of General Psychology, 14, 180–187. Google Scholar
  94. Panger, G. (2016). Reassessing the Facebook experiment: Critical thinking about the validity of Big Data research. Information, Communication & Society, 19, 1108–1126. Google Scholar
  95. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. New York, NY: Penguin.Google Scholar
  96. Paxton, A., & Dale, R. (2013). Argument disrupts interpersonal synchrony. Quarterly Journal of Experimental Psychology, 66, 2092–2102. Google Scholar
  97. Prinstein, M. J., & Dodge, K. A. (2008). Understanding peer influence in children and adolescents. New York, NY, USA: Guilford Press.Google Scholar
  98. Prot, S., Gentile, D. A., Anderson, C. A., Suzuki, K., Swing, E., Lim, K. M., ... Lam, B. C. P. (2014). Long-term relations among prosocial-media use, empathy, and prosocial behavior. Psychological Science, 25, 358–368.
  99. Ruan, Y., Purohit, H., Fuhry, D., Parthasarathy, S., & Sheth, A. P. (2012). Prediction of topic volume on Twitter. In Proceedings of the International ACM Web Science Conference (pp. 397–402). New York, NY, USA: ACM Press.Google Scholar
  100. Rubin, D. B. (1973). Matching to remove bias in observational studies. Biometrics, 29, 159–183. Google Scholar
  101. Schimel, J., Arndt, J., Banko, K. M., & Cook, A. (2004). Not all self-affirmations were created equal: The cognitive and social benefits of affirming the intrinsic (versus extrinsic) self. Social Cognition, 22, 75–99. Google Scholar
  102. Schmidt, R. C., Morr, S., Fitzpatrick, P., & Richardson, M. J. (2012). Measuring the dynamics of interactional synchrony. Journal of Nonverbal Behavior, 36, 263–279. Google Scholar
  103. Schutte, S., & Donnay, K. (2014). Matched wake analysis: Finding causal relationships in spatiotemporal event data. Political Geography, 41, 1–10.Google Scholar
  104. Sekhon, J. S. (2009). Opiates for the matches: Matching methods for causal inference. Annual Review of Political Science, 12, 487–508. Google Scholar
  105. Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40, 211–239. Google Scholar
  106. Sherman, D. K. (2013). Self-affirmation: Understanding the effects. Social and Personality Psychology Compass, 7, 834–845. Google Scholar
  107. Sherman, D. K., & Cohen, G. L. (2006). The psychology of self-defense: Self-affirmation theory. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 38, pp. 183–242). Amsterdam, The Netherlands: Elsevier. Google Scholar
  108. Simpkins, S. D., Schaefer, D. R., Price, C. D., & Vest, A. E. (2013). Adolescent friendships, BMI, and physical activity: Untangling selection and influence through longitudinal social network analysis. Journal of Research on Adolescence, 23, 537–549.Google Scholar
  109. Sinclair, S., Huntsinger, J., Skorinko, J., & Hardin, C. D. (2005). Social tuning of the self: Consequences for the self-evaluations of stereotype targets. Journal of Personality and Social Psychology, 89, 160–175. Google Scholar
  110. Smaldino, P. E. (2014). The cultural evolution of emergent group-level traits. Behavioral and Brain Sciences, 37, 243–254. Google Scholar
  111. Smith, L. B., Yu, C., & Pereira, A. F. (2011). Not your mother’s view: The dynamics of toddler visual experience. Developmental Science, 14, 9–17. Google Scholar
  112. Stella, M., Beckage, N. M., Brede, M., & De Domenico, M. (2018). Multiplex model of mental lexicon reveals explosive learning in humans. Scientific Reports, 8, 2259. Google Scholar
  113. Steyvers, M., & Tenenbaum, J. B. (2005). The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29, 41–78. Google Scholar
  114. Subrahmanyam, K., Greenfield, P., Kraut, R., & Gross, E. (2001). The impact of computer use on children’s and adolescents’ development. Journal of Applied Developmental Psychology, 22, 7–30. Google Scholar
  115. Thelen, E., Schoner, G., Scheier, C., & Smith, L. B. (2001). The dynamics of embodiment: A field theory of infant perseverative reaching. Behavioral and Brain Sciences, 24, 1–34, disc. 34–86. Google Scholar
  116. Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63, 163–173. Google Scholar
  117. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61, 2544–2558.Google Scholar
  118. Thompson, M. S., Judd, C. M., & Park, B. (2000). The consequences of communicating social stereotypes. Journal of Experimental Social Psychology, 36, 567–599. Google Scholar
  119. Vallacher, R. R., Nowak, A., & Kaufman, J. (1994). Intrinsic dynamics of social judgment. Journal of Personality and Social Psychology, 67, 20–34. Google Scholar
  120. Vallacher, R. R., Nowak, A., Markus, J., & Strauss, J. (1998). Dynamics in the coordination of mind and action. In M. Kofta, G. Weary, & G. Sedek (Eds.), Personal control in action: Cognitive and motivational mechanisms (pp. 27–59). New York, NY: Springer Science + Business Media. Google Scholar
  121. Vallacher, R. R., Read, S. J., & Nowak, A. (2002). The dynamical perspective in personality and social psychology. Personality and Social Psychology Review, 6, 264–273. Google Scholar
  122. van Geert, P., & Steenbeek, H. (2005). Explaining after by before: Basic aspects of a dynamic systems approach to the study of development. Developmental Review, 25, 408–442.Google Scholar
  123. Van Raalte, J. L., Brewer, B. W., Lewis, B. P., Linder, D. E., Wildman, G., & Kozimor, J. (1995). Cork! The effects of positive and negative self-talk on dart throwing performance. Journal of Sport Behavior, 18, 50–57.Google Scholar
  124. Vilares, D., Thelwall, M., & Alonso, M. A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41, 799–813.Google Scholar
  125. Vilhena, D. A., Foster, J. G., Rosvall, M., West, J. D., Evans, J., & Bergstrom, C. T. (2014). Finding cultural holes. Sociological Science, 1, 221–238.Google Scholar
  126. Vinson, D. W., & Dale, R. (2014). Valence weakly constrains the information density of messages. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Meeting of the Cognitive Science Society (pp. 1682–1687). Austin, TX: Cognitive Science Society.Google Scholar
  127. Weinberg, R. S., Smith, J., Jackson, A., & Gould, D. (1984). Effect of association, dissociation and positive self-talk strategies on endurance performance. Canadian Journal of Applied Sport Sciences, 9, 25–32. Retrieved from Google Scholar
  128. Weiss, W. (1953). A “sleeper” effect in opinion change. Journal of Abnormal and Social Psychology, 48, 173–180. Google Scholar
  129. Wiese, S. L., Vallacher, R. R., & Strawinska, U. (2010). Dynamical social psychology: Complexity and coherence in human experience. Social and Personality Psychology Compass, 4, 1018–1030. Google Scholar
  130. Xygalatas, D., Konvalinka, I., Bulbulia, J., & Andreas, R. (2011). Quantifying collective effervescence: Heart-rate dynamics at a fire-walking ritual. Communicative and Integrative Biology, 4, 735–738. Google Scholar
  131. Yee, N., Bailenson, J. N., Urbanek, M., Chang, F., & Merget, D. (2007). The unbearable likeness of being digital: The persistence of nonverbal social norms in online virtual environments. Cyberpsychology and Behavior, 10, 115–121. Google Scholar

Copyright information

© Psychonomic Society, Inc. 2018

Authors and Affiliations

  1. 1.Department of CommunicationUC DavisDavisUSA
  2. 2.Neukom InstituteDartmouth CollegeHanoverUSA
  3. 3.Disney ResearchLos AngelesUSA
  4. 4.University of KonstanzKonstanzGermany
  5. 5.ETH ZurichZurichSwitzerland
  6. 6.Carnegie Mellon UniversityPittsburghUSA

Personalised recommendations