Predicting Risk Perception: New Insights from Data Science

References

• Asur S, Huberman BA (2010) Predicting the future with social media. Proc. 2010 IEEE/WIC/ACM Internat. Conf. Web Intelligence Intelligent Agent Tech., vol. 1 (IEEE, Piscataway, NJ), 492–499.Crossref, Google Scholar
• Bhatia S (2017a) Associative judgment and vector space semantics. Psych. Rev. 124(1):1–20.Crossref, Google Scholar
• Bhatia S (2017b) The semantic representation of prejudice and stereotypes. Cognition 164:46–60Crossref, Google Scholar
• Bhatia S (2019) Semantic processes in preferential decision making. J. Experiment. Psych.: Learn., Memory, Cognition 45(4):627–640.Crossref, Google Scholar
• Bhatia S, Goodwin G, Walasek L (2018) Trait associations for Hillary Clinton and Donald Trump in news media: A computational analysis. Soc. Psych. Personality Sci. 9(2):123–130.Crossref, Google Scholar
• Bickerstaff K (2004) Risk perception research: Socio-cultural perspectives on the public experience of air pollution. Environ. Internat. 30(6):827–840.Crossref, Google Scholar
• Borg I, Groenen PJ, Mair P (2012) Applied Multidimensional Scaling (Springer, New York).Google Scholar
• Brysbaert M, Warriner AB, Kuperman V (2014) Concreteness ratings for 40 thousand generally known English word lemmas. Behav. Res. Methods 46(3):904–911.Google Scholar
• Bullinaria JA, Levy JP (2007) Extracting semantic representations from word co-occurrence statistics: A computational study. Behav. Res. Methods 39:510–526.Crossref, Google Scholar
• Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183–186.Crossref, Google Scholar
• Chandran S, Menon G (2004) When a day means more than a year: Effects of temporal framing on judgments of health risk. J. Consumer Res. 31(2):375–389.Crossref, Google Scholar
• Chen MK (2013) The effect of language on economic behavior: Evidence from savings rates, health behaviors, and retirement assets. Amer. Econom. Rev. 103(2):690–731.Crossref, Google Scholar
• Choi H, Varian H (2012) Predicting the present with Google Trends. Econom. Record 88(1):2–9.Crossref, Google Scholar
• Curme C, Preis T, Stanley HE, Moat HS (2014) Quantifying the semantics of search behavior before stock market moves. Proc. Natl. Acad. Sci. USA 111(32):11600–11605.Crossref, Google Scholar
• Dawes RM, Faust D, Meehl PE (1989) Clinical vs. actuarial judgment. Science 243(4899):1668–1674.Crossref, Google Scholar
• Dehghani M, Sagae K, Sachdeva S, Gratch J (2014) Analyzing political rhetoric in conservative and liberal weblogs related to the construction of the “Ground Zero Mosque.” J. Inform. Tech. Politics 11(1):1–14.Crossref, Google Scholar
• Dhillon P, Foster DP, Ungar LH (2011) Multi-view learning of word embeddings via CCA. Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems 24 (Curran Associates, Red Hook, NY), 199–207.Google Scholar
• Erev I, Ert E, Plonsky O, Cohen D, Cohen O (2017) From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience. Psych. Rev. 124(4):369–409.Crossref, Google Scholar
• Finucane ML, Slovic P, Mertz CK, Flynn J, Satterfield TA (2000) Gender, race, and perceived risk: The ‘white male’ effect. Health Risk Soc. 2(2):159–172.Crossref, Google Scholar
• Firth JR (1957) Papers in Linguistics (Oxford University Press, Oxford, UK).Google Scholar
• Fischhoff B (1995) Risk perception and communication unplugged: twenty years of process. Risk Anal. 15(2):137–145.Crossref, Google Scholar
• Fischhoff B, Kadvany J (2011) Risk: A Very Short Introduction (Oxford University Press, Oxford, UK).Crossref, Google Scholar
• Fischhoff B, Slovic P, Lichtenstein S, Read S, Combs B (1978) How safe is safe enough? A psychometric study of attitudes toward technological risks and benefits. Policy Sci. 9(2):127–152.Crossref, Google Scholar
• Garg N, Schiebinger L, Jurafsky D, Zou J (2018) Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc. Natl. Acad. Sci. USA 115(16):E3635–E3644.Crossref, Google Scholar
• Garten J, Hoover J, Johnson K, Boghrati R, Iskiwitch C, Dehghani M (2017) Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis. Behav. Res. Methods 50(1):344–361Crossref, Google Scholar
• George G, Osinga EC, Lavie D, Scott BA (2016) Big data and data science methods for management research. Acad. Management J. 59(5):1493–1507.Crossref, Google Scholar
• Gigerenzer G, Brighton H (2009) Homo heuristicus: Why biased minds make better inferences. Topics Cognitive Sci. 1(1):107–143.Crossref, Google Scholar
• Griffiths TL (2015) Manifesto for a new (computational) cognitive revolution. Cognition 135:21–23.Crossref, Google Scholar
• Griffiths TL, Steyvers M, Tenenbaum JB (2007) Topics in semantic representation. Psych. Rev. 114(2):211.Crossref, Google Scholar
• Harlow LL, Oswald FL (2016) Big data in psychology: Introduction to the special issue. Psych. Methods 21(4):447–457.Crossref, Google Scholar
• Harris ZS (1954) Distributional structure. Word 2:146–162.Crossref, Google Scholar
• Hawn C (2009) Take two aspirin and tweet me in the morning: How Twitter, Facebook, and other social media are reshaping healthcare. Health Affairs 28(2):361–368.Crossref, Google Scholar
• Holtzman NS, Schott JP, Jones MN, Balota DA, Yarkoni T (2011) Exploring media bias with semantic analysis tools: Validation of the contrast analysis of semantic similarity (CASS). Behav. Res. Methods 43(1):193–200.Crossref, Google Scholar
• Holtgrave DR, Weber EU (1993) Dimensions of risk perception for financial and health risks. Risk Anal. 13(5):553–558.Crossref, Google Scholar
• Humphreys A, Wang RJ-H (2017) Automated text analysis for consumer research. J. Consumer Res. 6(1):1274–1306.Google Scholar
• Iliev R, Hoover J, Dehghani M, Axelrod R (2016) Linguistic positivity in historical texts reflects dynamic environmental and psychological factors. Proc. Natl. Acad. Sci. USA 113(49):E7871–E7879.Crossref, Google Scholar
• Jenkin CM (2006) Risk perception and terrorism: Applying the psychometric paradigm. Homeland Security Affairs J. 2:1–14.Google Scholar
• Johnson EJ, Tversky A (1983) Affect, generalization, and the perception of risk. J. Personality Soc. Psych. 45(1):20.Crossref, Google Scholar
• Johnson EJ, Tversky A (1984) Representations of perceptions of risks. J. Experiment. Psych. General 113(1):55.Crossref, Google Scholar
• Jones MN, Mewhort DJ (2007) Representing word meaning and order information in a composite holographic lexicon. Psych. Rev. 114(1):1.Crossref, Google Scholar
• Jones MN, Willits JA, Dennis S (2015) Models of semantic memory. Busemeyer JR, Townsend JT, eds. Oxford Handbook of Mathematical and Computational Psychology (Oxford University Press, Oxford, UK), 232–254.Google Scholar
• Jones MN, ed. (2017) Big Data in Cognitive Science, Frontiers of Cognitive Psychology (Psychology Press, Taylor & Francis Group, Abingdon, UK).Google Scholar
• Krimsky S, Golding D (1992) Social Theories of Risk (Praeger, Santa Barbara, CA).Google Scholar
• Kruskal JB (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1):1–27.Crossref, Google Scholar
• Kosinski M, Behrend T (2017) Big data in the behavioral sciences. Current Opinion Behav. Sci. 18:4–6.Google Scholar
• Landauer TK, Dumais ST (1997) A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psych. Rev. 104(2):211–240.Crossref, Google Scholar
• Lerner JS, Keltner D (2001) Fear, anger, and risk. J. Personality Soc. Psych. 81(1):146.Crossref, Google Scholar
• Lerner JS, Gonzalez RM, Small DA, Fischhoff B (2003) Effects of fear and anger on perceived risks of terrorism a national field experiment. Psych. Sci. 14(2):144–150.Crossref, Google Scholar
• Loewenstein GF, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psych. Bull. 127(2):267.Crossref, Google Scholar
• McDowell M, Rebitschek FG, Gigerenzer G, Wegwarth O (2016) A simple tool for communicating the benefits and harms of health interventions: A guide for creating a fact box. Medical Decision Making Policy Practice 1(1):1–10.Google Scholar
• Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems 26 (Curran Associates, Red Hook, NY), 3111–3119.Google Scholar
• Mohammad SM, Turney PD (2013) Crowdsourcing a word–emotion association lexicon. Comput. Intell. 29(3):436–465.Crossref, Google Scholar
• Noguchi T, Stewart N, Olivola CY, Moat HS, Preis T (2014) Characterizing the time-perspective of nations with search engine query data. PLoS One 9(4):e95209.Crossref, Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Prettenhofer P, Weiss R, Dubourg V, et al. (2011) Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12(October):2825–2830.Google Scholar
• Pennebaker JW, Francis ME, Booth RJ (2001) Linguistic Inquiry and Word Count (Lawrence Erlbaum Associates, Mahwah, NJ).Google Scholar
• Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. Proc. 2014 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 1532–1543.Crossref, Google Scholar
• Peters E, Slovic P (1996) The role of affect and worldviews as orienting dispositions in the perception and acceptance of nuclear power. J. Appl. Soc. Psych. 26(16):1427–1453.Crossref, Google Scholar
• Pidgeon N, Hood C, Jones D, Turner B, Gibson R (1992) Risk perception. Risk: Analysis, Perception and Management: Report of a Royal Society Study Group, The Royal Society, London, 89–134.Google Scholar
• Plonsky O, Erev I, Hazan T, Tennenholtz M (2017) Psychological forest: Predicting human behavior. Proc. 31st AAAO Conf. Artificial Intelligence (AAAI-17) (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 656–662.Google Scholar
• Sanquist TF, Mahy H, Morris F (2008) An exploratory risk perception study of attitudes toward homeland security systems. Risk Anal. 28(4):1125–1133.Google Scholar
• Slovic P (1987) Perception of risk. Science 236(4799):280.Crossref, Google Scholar
• Slovic P (1992) Perception of risk: Reflections on the psychometric paradigm. Krimsky S, Golding D, eds. Social Theories of Risk (Praeger, New York), 117–152.Google Scholar
• Slovic P, Peters E (2006) Risk perception and affect. Curr. Dir. Psych. Sci. 15(6):322–325.Crossref, Google Scholar
• Slovic P, Weber EU (2002) Perception of risk posed by extreme events. Working paper, University of Oregon, Eugene.Google Scholar
• Slovic P, Fischhoff B, Lichtenstein S (1984) Behavioral decision theory perspectives on risk and safety. Acta Psych. 56(1):183–203.Crossref, Google Scholar
• Slovic P, Finucane M, Peters E, MacGregor DG (2002) Rational actors or rational fools: Implications of the affect heuristic for behavioral economics. J. Sociol. Econom. 31(4):329–342.Crossref, Google Scholar
• Slovic P, Peters E, Finucane ML, MacGregor DG (2005) Affect, risk, and decision making. Health Psych. 24(4S):S35.Crossref, Google Scholar
• Trope Y, Liberman N (2010) Construal-level theory of psychological distance. Psych. Rev. 117(2):440.Crossref, Google Scholar
• Turney PD, Pantel P (2010) From frequency to meaning: Vector space models of semantics. J. Artif. Intell. Res. 37(1):141–188.Crossref, Google Scholar
• Yarkoni T, Westfall J (2017) Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect. Psych. Sci. 12(6):1100–1122.Crossref, Google Scholar