Advertisement

To illuminate and motivate: a fuzzy-trace model of the spread of information online

  • David A. BroniatowskiEmail author
  • Valerie F. Reyna
S.I. : Social Cyber-Security
  • 98 Downloads

Abstract

We propose, and test, a model of online media platform users’ decisions to act on, and share, received information. Specifically, we focus on how mental representations of message content drive its spread. Our model is based on fuzzy-trace theory (FTT), a leading theory of decision under risk. Per FTT, online content is mentally represented in two ways: verbatim (objective, but decontextualized, facts), and gist (subjective, but meaningful, interpretation). Although encoded in parallel, gist tends to drive behaviors more strongly than verbatim representations for most individuals. Our model uses factors derived from FTT to make predictions regarding which content is more likely to be shared, namely: (a) different levels of mental representation, (b) the motivational content of a message, (c) difficulty of information processing (e.g., the ease with which a given message may be comprehended and, therefore, its gist extracted), and (d) social values.

Keywords

Gist Verbatim Vaccines Misinformation twitter 

Notes

Acknowledgement

Preparation of this manuscript was supported in part by the National Institute of General Medical Sciences R01GM114771 to the first author.

References

  1. Akaike H (1976) Canonical correlation analysis of time series and the use of an information criterion. In: Mathematics in science and engineering, vol 126, Elsevier, pp 27–96Google Scholar
  2. Bakshy E, Messing S, Adamic LA (2015) Exposure to ideologically diverse news and opinion on facebook. Science 348(6239):1130–1132Google Scholar
  3. Bansal S (2018) textstat:memo: python package to calculate readability statistics of a text object—paragraphs, sentences, articles.https://github.com/shivam5992/textstat. Accessed 16 June 2014
  4. Berger J, Milkman KL (2012) What makes online content viral? J Mark Res 49(2):192–205Google Scholar
  5. Betsch C, Brewer NT, Brocard P, Davies P, Gaissmaier W, Haase N, Leask J, Renkewitz F, Renner B, Reyna VF et al (2012) Opportunities and challenges of Web 2.0 for vaccination decisions. Vaccine 30(25):3727–3733Google Scholar
  6. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022Google Scholar
  7. Bradley MM, Lang PJ (1999) Affective norms for english words (anew). The NIMH Center for the Study of Emotion and Attention, University of Florida, GainesvilleGoogle Scholar
  8. Brewer NT, Chapman GB, Rothman AJ, Leask J, Kempe A (2017) Increasing vaccination: putting psychological science into action. Psychol Sci Public Interest 18(3):149–207Google Scholar
  9. Broniatowski D, Reyna V (2018) A formal model of fuzzy-trace theory. Decision 5(4):205–252Google Scholar
  10. Broniatowski DA, Klein EY, Reyna VF (2015) Germs are germs, and why not take a risk? Patients’ expectations for prescribing antibiotics in an Inner-City Emergency Department. Med Decis Mak 35(1):60–67Google Scholar
  11. Broniatowski DA, Hilyard KM, Dredze M (2016) Effective vaccine communication during the disneyland measles outbreak. Vaccine 34(28):3225–3228 7Google Scholar
  12. Broniatowski DA, Jamison AM, Qi S, AlKulaib L, Chen T, Benton A, Quinn SC, Dredze M (2018) Weaponized health communication: twitter bots and Russian trolls amplify the vaccine debate. Am J Public Health 108(10):1378–1384Google Scholar
  13. Busemeyer JR, Wang YM (2000) Model comparisons and model selections based on generalization criterion methodology. J Math Psychol 44(1):171–189Google Scholar
  14. Buttenheim AM, Asch DA (2016) Leveraging behavioral insights to promote vaccine acceptance: one year after disneyland. JAMA Pediatr 170(7):635–636Google Scholar
  15. Cacioppo JT, Petty RE, Feng Kao C (1984) The efficient assessment of need for cognition. J Personal Assess 48(3):306–307Google Scholar
  16. Cacioppo JT, Feinstein JA, Jarvis WBG (1996) Dispositional differences in cognitive motivation: the life and times of individuals varying in need for cognition. Psychol Bull 119(2):197Google Scholar
  17. Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197Google Scholar
  18. Centola D (2011) An experimental study of homophily in the adoption of health behavior. Science 334(6060):1269–1272Google Scholar
  19. Chang J, Gerrish S, Wang C, Boyd-Graber JL, Blei DM (2009) Reading tea leaves: how humans interpret topic models. In: Advances in neural information processing systems, pp 288–296Google Scholar
  20. Chen T, Dredze M (2018) Vaccine images on twitter: analysis of what images are shared. J Med Internet Res 20(4):e130Google Scholar
  21. Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted? In: Proceedings of the 23rd international conference on World wide web, ACM, pp 925–936Google Scholar
  22. Chou WYS, Oh A, Klein WM (2018) Addressing health-related misinformation on social media. Jama 320(23):2417–2418Google Scholar
  23. Cokely ET, Kelley CM (2009) Cognitive abilities and superior decision making under risk: a protocol analysis and process model evaluation. Judgm Decis Mak 4(1):20–33Google Scholar
  24. Cowling BJ, Fang VJ, Nishiura H, Chan KH, Ng S, Ip DK, Chiu SS, Leung GM, Peiris JM (2012) Increased risk of noninfluenza respiratory virus infections associated with receipt of inactivated influenza vaccine. Clin Infect Dis 54(12):1778–1783Google Scholar
  25. Curseu PL (2006) Need for cognition and rationality in decision-making. Stud Psychol 48(2):141Google Scholar
  26. Diehl JJ, Bennetto L, Young EC (2006) Story recall and narrative coherence of high-functioning children with autism spectrum disorders. J Abnorm Child Psychol 34(1):83–98Google Scholar
  27. Dredze M, Broniatowski DA, Hilyard KM (2016a) Zika vaccine misconceptions: a social media analysis. Vaccine 34(30):3441–3442Google Scholar
  28. Dredze M, Broniatowski DA, Smith MC, Hilyard KM (2016b) Understanding vaccine refusal: why we need social media now. AmJ Prev Med 50(4):550–552Google Scholar
  29. Dredze M, Wood-Doughty Z, Quinn SC, Broniatowski DA (2017) Vaccine opponents’ use of twitter during the 2016 us presidential election: implications for practice and policy. Vaccine 35(36):4670–4672Google Scholar
  30. Frederick S (2005) Cognitive reflection and decision making. J Econ Perspect 19(4):25–42Google Scholar
  31. Fukukura J, Ferguson MJ, Fujita K (2013) Psychological distance can improve decision making under information overload via gist memory. J Exp Psychol 142(3):658Google Scholar
  32. Galván A (2017) The neuroscience of adolescence, 1st edn. Cambridge University Press, Cambridge, New YorkGoogle Scholar
  33. Gernsbacher MA, Varner KR, Faust ME (1990) Investigating differences in general comprehension skill. J Exp Psychol 16(3):430Google Scholar
  34. Gernsbacher MA (1996) The structure-building framework: what it is, what it might also be, and why. In: Britton BK, Graesser AC (eds) Models of understanding text. Psychology Press, New York, NY, pp 289–311Google Scholar
  35. Golbeck J, Robles C, Edmondson M, Turner K (2011) Predicting personality from twitter. In: 2011 IEEE Third International conference on privacy, security, risk and trust (PASSAT) and 2011 IEEE Third International conference on social computing (SocialCom), IEEE, pp 149–156Google Scholar
  36. Goldman SR, McCarthy KS, Burkett C (2015) Interpretive inferences in literature. In: Inferences during reading, p 386Google Scholar
  37. Granovetter M, Soong R (1983) Threshold models of diffusion and collective behavior. J Math Sociol 9(3):165–179Google Scholar
  38. Griffiths TL, Steyvers M, Tenenbaum JB (2007) Topics in semantic representation. Psychol Rev 114(2):211Google Scholar
  39. Grinberg N, Joseph K, Friedland L, Swire-Thompson B, Lazer D (2019) Fake news on twitter during the 2016 us presidential election. Science 363(6425):374–378Google Scholar
  40. Hsee CK, Rottenstreich Y (2004) Music, pandas, and muggers: on the affective psychology of value. J Exp Psychol 133(1):23Google Scholar
  41. Jamison AM, Broniatowski D, Quinn SC (2019) Malicious actors on twitter: a guide for public health researchers. Am J Public Health 109:688–692Google Scholar
  42. Kintsch W (1974) The representation of meaning in memory. Lawrence Erlbnum Associates, HillsdaleGoogle Scholar
  43. Klein EY, Martinez EM, May L, Saheed M, Reyna V, Broniatowski DA (2017) Categorical risk perception drives variability in antibiotic prescribing in the Emergency Department: a mixed methods observational study. J Gen Intern Med 32(10):1083–1089Google Scholar
  44. LaTour KA, LaTour MS, Brainerd C (2014) Fuzzy trace theory and “smart” false memories: implications for advertising. J Advert 43(1):3–17Google Scholar
  45. LeBoeuf RA, Shafir E (2003) Deep thoughts and shallow frames: on the susceptibility to framing effects. J Behav Decis Mak 16(2):77–92Google Scholar
  46. Liberali JM, Reyna VF, Furlan S, Stein LM, Pardo ST (2012) Individual differences in numeracy and cognitive reflection, with implications for biases and fallacies in probability judgment. J Behav Decis Mak 25(4):361–381Google Scholar
  47. Linderholm T, Everson MG, van den Broek P, Mischinski M, Crittenden A, Samuels J (2000) Effects of causal text revisions on more- and less-skilled readers’ comprehension of easy and difficult texts. Cogn Instr 18(4):525–556Google Scholar
  48. Mandler JM (1983) What a story is. Behav Brain Sci 6(04):603–604Google Scholar
  49. Mohammad SM, Kiritchenko S (2015) Using hashtags to capture fine emotion categories from tweets. Comput Intell 31(2):301–326Google Scholar
  50. Mohammad SM, Turney PD (2013) Crowdsourcing a word-emotion association lexicon. Comput Intell 29(3):436–465Google Scholar
  51. Myrick JG (2015) Emotion regulation, procrastination, and watching cat videos online: who watches internet cats, why, and to what effect? Comput Hum Behav 52:168–176Google Scholar
  52. Mnøsted B, Sapieżyński P, Ferrara E, Lehmann S (2017) Evidence of complex contagion of information in social media: an experiment using twitter bots. PLoS ONE 12(9):e0184148Google Scholar
  53. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  54. Pennington N, Hastie R (1991) A cognitive theory of juror decision making: the story model. Cardozo L Rev 13:519Google Scholar
  55. Pennycook G, Rand DG (2018) Lazy, not biased: susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188:39–50Google Scholar
  56. Pennycook G, Cheyne JA, Koehler DJ, Fugelsang JA (2016) Is the cognitive reflection test a measure of both reflection and intuition? Behav Res Methods 48(1):341–348Google Scholar
  57. Pennycook G, Cannon TD, Rand DG (2018) Prior exposure increases perceived accuracy of fake news. J Exp Psychol 147:1865–1880Google Scholar
  58. Perrin A (2015) Social media usage: 2005–2015Google Scholar
  59. Peters E, Levin IP (2008) Dissecting the risky-choice framing effect: numeracy as an individual-difference factor in weighting risky and riskless options. Judgm Decis Mak 3(6):435–448Google Scholar
  60. Peters E, Västfjäll D, Slovic P, Mertz CK, Mazzocco K, Dickert S (2006) Numeracy and decision making. Psychol Sci 17(5):407–413Google Scholar
  61. Petrovic S, Osborne M, Lavrenko V (2011) RT to win! Predicting message propagation in twitter. ICWSM 11:586–589Google Scholar
  62. Plutchik R (2001) The nature of emotions: human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am Sci 89(4):344–350Google Scholar
  63. Quercia D, Kosinski M, Stillwell D, Crowcroft J (2011) Our twitter profiles, our selves: predicting personality with twitter. In: 2011 IEEE Third International conference on privacy, security, risk and trust (PASSAT) and 2011 IEEE Third International conference on social computing (SocialCom), IEEE, pp 180–185Google Scholar
  64. Quinn SC, Parmer J, Freimuth VS, Hilyard KM, Musa D, Kim KH (2013) Exploring communication, trust in government, and vaccination intention later in the 2009 H1n1 pandemic: results of a national survey. Biosecur Bioterror 11(2):96–106Google Scholar
  65. Rapp DN, Pvd Broek, McMaster KL, Kendeou P, Espin CA (2007) Higher-order comprehension processes in struggling readers: a perspective for research and intervention. Sci Stud Read 11(4):289–312Google Scholar
  66. Reese E, Haden CA, Baker-Ward L, Bauer P, Fivush R, Ornstein PA (2011) Coherence of personal narratives across the lifespan: a multidimensional model and coding method. J Cogn Dev 12(4):424–462Google Scholar
  67. Reyna VF (2012) Risk perception and communication in vaccination decisions: a fuzzy-trace theory approach. Vaccine 30(25):3790–3797Google Scholar
  68. Reyna VF, Adam MB (2003) Fuzzy-trace theory, risk communication, and product labeling in sexually transmitted diseases. Risk Anal 23(2):325–342Google Scholar
  69. Reyna VF, Brainerd CJ (2008) Numeracy, ratio bias, and denominator neglect in judgments of risk and probability. Learn Individ Differ 18(1):89–107Google Scholar
  70. Reyna VF, Lloyd FJ (2006) Physician decision making and cardiac risk: effects of knowledge, risk perception, risk tolerance, and fuzzy processing. J Exp Psychol 12(3):179Google Scholar
  71. Reyna VF, Estrada SM, DeMarinis JA, Myers RM, Stanisz JM, Mills BA (2011) Neurobiological and memory models of risky decision making in adolescents versus young adults. J Exp Psychol 37(5):1125Google Scholar
  72. Reyna VF, Corbin JC, Weldon RB, Brainerd CJ (2016) How fuzzy-trace theory predicts true and false memories for words, sentences, and narratives. J Appl Res Mem Cogn 5(1):1–9Google Scholar
  73. Riddell A (2014) Lda: 0.3.2. 10.5281/zenodo.592664. https://zenodo.org/record/592664. Accessed 16 July 2018
  74. Rivers SE, Reyna VF, Mills B (2008) Risk taking under the influence: a fuzzy-trace theory of emotion in adolescence. Dev Rev 28(1):107–144Google Scholar
  75. Rogers EM (2010) Diffusion of innovations. Simon and Schuster, New YorkGoogle Scholar
  76. Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on World wide web, ACM, pp 695–704Google Scholar
  77. Schley DR, Peters E (2014) Assessing economic value symbolic-number mappings predict risky and riskless valuations. Psychol Sci 25:753–761Google Scholar
  78. Shmueli G et al (2010) To explain or to predict? Stat Sci 25(3):289–310Google Scholar
  79. Silverman C (2016) This analysis shows how viral fake election news stories outperformed real news on facebook. Retrieved February 15, 2017, from https://www.buzzfeed.com/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook
  80. Simon AF, Fagley NS, Halleran JG (2004) Decision framing: moderating effects of individual differences and cognitive processing. J Behav Decis Mak 17(2):77–93Google Scholar
  81. Smith A, Anderson M (2018) Social media use in 2018. Pew Research Center 1Google Scholar
  82. Stevens SS et al (1946) On the theory of scales of measurement. Science 103:677–680Google Scholar
  83. Subrahmanian V, Azaria A, Durst S, Kagan V, Galstyan A, Lerman K, Zhu L, Ferrara E, Flammini A, Menczer F (2016) The darpa twitter bot challenge. Computer 49(6):38–46Google Scholar
  84. Sundaram ME, McClure DL, VanWormer JJ, Friedrich TC, Meece JK, Belongia EA (2013) Influenza vaccination is not associated with detection of noninfluenza respiratory viruses in seasonal studies of influenza vaccine effectiveness. Clin Infect Dis 57(6):789–793Google Scholar
  85. Swire B, Berinsky AJ, Lewandowsky S, Ecker UK (2017) Processing political misinformation: comprehending the trump phenomenon. R Soc Open Sci 4(3):160802Google Scholar
  86. Trabasso T, Sperry LL (1985) Causal relatedness and importance of story events. J Mem Lang 24(5):595–611Google Scholar
  87. Trabasso T, Secco T, Van Den Broek P (1984) Causal cohesion and story coherence. In: Mandl H, Stein NL, Trabasso T (eds) Learning and comprehension of text. Lawrence Erlbaum Associates, Hillsdale, NJ, pp 83–110Google Scholar
  88. Trope Y, Liberman N (2010) Construal-level theory of psychological distance. Psychol Rev 117(2):440Google Scholar
  89. Tsur O, Rappoport A (2012) What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the fifth ACM international conference on Web search and data mining, ACM, pp 643–652Google Scholar
  90. Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211(4481):453–458Google Scholar
  91. Van den Broek P (2010) Using texts in science education: cognitive processes and knowledge representation. Science 328(5977):453–456Google Scholar
  92. van den Broek P, Helder A (2017) Cognitive processes in discourse comprehension: passive processes, reader-initiated processes, and evolving mental representations. Discourse Process 54:1–13Google Scholar
  93. Vazquez MA (2016) Informe de Médicos de Pueblos Fumigados sobre Dengue-Zika y fumigaciones con venenos químicoshttp://alimentoyconciencia.com/informe-de-medicos-de-pueblos-fumigados-sobre-dengue-zika-y-fumigaciones-con-venenos-quimicos/. Accessed 06 Feb 2017
  94. Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Engineering Management and Systems Engineering, School of Engineering and Applied ScienceThe George Washington UniversityWashingtonUSA
  2. 2.Human Neuroscience Institute, Center for Behavioral Economics and Decision Research, and Cornell Magnetic Resonance Image FacilityCornell UniversityIthacaUSA

Personalised recommendations