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The Challenge of Big Data and Data Science

Abstract

Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation. Along with these changes in society, powerful new data science methods support research using administrative, internet, textual, and sensor-audio-video data. Burgeoning data and innovative methods facilitate answering previously hard-to-tackle questions about society by offering new ways to form concepts from data, to do descriptive inference, to make causal inferences, and to generate predictions. They also pose challenges as social scientists must grasp the meaning of concepts and predictions generated by convoluted algorithms, weigh the relative value of prediction versus causal inference, and cope with ethical challenges as their methods, such as algorithms for mobilizing voters or determining bail, are adopted by policy makers.

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    2019-05-11
    2026-02-14

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    Literature Cited

    1. AhlquistJA,BreunigC2012. Model-based clustering and typologies in the social sciences.Political Anal20:192–112
      [Google Scholar]
    2. AlbusJS1984. Robots and the economy.Futurist18:638–44
      [Google Scholar]
    3. AlvarezRM2016.Computational Social Science: Discovery and Prediction (Analytical Methods for Social Research) Cambridge, UK: Cambridge Univ. Press
      [Google Scholar]
    4. AnsolabehereS,HershE2012. Validation: what big data reveal about survey misreporting and the real electorate.Political Anal.20:4437–59
      [Google Scholar]
    5. AtheyS2018. Draft chapter, Natl. Bur. Econ. Res. Cambridge, MA:http://www.nber.org/chapters/c14009.pdf
    6. AtkinsDE,DroegemeierKK,FeldmanSI,Garcia-MolinaH,KleinM et al.2003.Revolutionizing science and engineering through cyberinfrastructure: report of the National Science Foundation blue-ribbon advisory panel on cyberinfrastructure Rep. Natl. Sci. Found. Washington, DC:https://stewardshipgap.net/node/17
      [Google Scholar]
    7. BailCA2014. The cultural environment: measuring culture with big data.Theory Soc43:3/4465–82
      [Google Scholar]
    8. BarberáP2015. Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data.Political Anal23:76–91
      [Google Scholar]
    9. BeachySH,OlsonS,BergerAC2015.Genomics-Enabled Learning Health Care Systems: Gathering and Using Genomic Information to Improve Patient Care and Research: Workshop Summary Washington, DC: Natl. Acad. Press
      [Google Scholar]
    10. BennettWL,SegerbergA2012. The logic of connective action.Inf. Commun. Soc.15:5739–68
      [Google Scholar]
    11. BerkRA2008.Statistical Learning from a Regression Perspective New York: Springer
      [Google Scholar]
    12. BermanF,BradyH2005.Workshop on cyberinfrastructure for the social and behavioral sciences: final report. Rep., Natl. Sci. Found., Alexandria, VA.https://www.sdsc.edu/assets/docs/SBE-CISE-FINAL.pdf. Accessed Dec. 2, 2018
    13. BishopCM2011.Pattern Recognition and Machine Learning New York: Springer
      [Google Scholar]
    14. BohnR,ShortJ2012. Measuring consumer information.Int. J. Commun.6:980–1000
      [Google Scholar]
    15. BondRM,FarissCJ,JonesJJ,KramerAD,MarlowC et al.2012. A 61-milllion-person experiment in social influence and political mobilization.Nature489:7415295–98
      [Google Scholar]
    16. BondR,MessingS2015. Quantifying social media's political space: estimating ideology from publicly revealed preferences on Facebook.Am. Political Sci. Rev.109:162–78
      [Google Scholar]
    17. BonicaA2013. Ideology and interests in the political marketplace.Am. J. Political Sci.57:2294–311
      [Google Scholar]
    18. BonicaA2016. A data-driven voter guide for U.S. elections: adapting quantitative measures of the preferences and priorities of political elites to help votes learn about candidates.RSF Russell Sage Found. J. Soc. Sci.2:711–32
      [Google Scholar]
    19. BonicaA,ChiltonA,SenM2016. The political ideologies of American lawyers.J. Legal Analysis8:2277–335
      [Google Scholar]
    20. BonicaA,RosenthalH,RothmanDJ2014. The political polarization of physicians in the United States: an analysis of campaign contributions to federal elections, 1991 through 2012.JAMA Intern. Med.174:81308–17
      [Google Scholar]
    21. BoullierD2015. The social sciences and traces of big data: society, opinion, or vibrations?.Rev. Française Sci. Politique65:5–671–93
      [Google Scholar]
    22. boydD,CrawfordK2012. Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon.Inf. Commun. Soc.15:5662–79
      [Google Scholar]
    23. BradyHE2009. Causation and explanation in political science.The Oxford Handbook of Political Science R Goodin217–70 Oxford, UK: Oxford Univ. Press
      [Google Scholar]
    24. BradyHE,GrandSA,PowellMA,SchinkW2001. Access and confidentiality issues with administrative data.Studies of Welfare Populations: Data Collection and Research Issues Natl. Res. Counc.220–74 Washington, DC: Natl. Acad. Press
      [Google Scholar]
    25. BradyHE,McNultyJE2011. Turning out to vote: the costs of finding and getting to the polling place.Am. Political Sci. Rev.105:1115–34
      [Google Scholar]
    26. BradyHE,SchlozmanKL,VerbaS1999. Prospecting for participants: rational expectations and the recruitment of political activists.Am. Political Sci. Rev.93:1153–68
      [Google Scholar]
    27. BreimanL2001. Statistical modeling: the two cultures.Stat. Sci.16:3199–231
      [Google Scholar]
    28. ChenH,ChiangRHL,StoreyVC2012. Business intelligence and analytics: from big data to big impact.MIS Q36:41165–88
      [Google Scholar]
    29. ChristianoLJ2012. Christopher A. Sims and vector autoregressions.Scand. J. Econ.114:41082–104
      [Google Scholar]
    30. ClarkWR,GolderM2015. Big data, causal inference, and formal theory: contradictory trends in political science.PS Political Sci. Politics48:165–70
      [Google Scholar]
    31. ClarkeRA,KnakeR2011.Cyber War: The Next Threat to National Security and What to Do About It New York: HarperCollins
      [Google Scholar]
    32. ClevelandWS2001. Data science: an action plan for expanding the technical areas of the field of statistics.Int. Stat. Rev.69:121–26
      [Google Scholar]
    33. Corbett-DaviesS,PiersonE,FellerA,GoelS,HuqA2017. Algorithmic decision making and the cost of fairness.Proceedings of 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Canada New York: ACMhttps://arxiv.org/abs/1701.08230
      [Google Scholar]
    34. CukierK,Mayer-SchoenbergerV2013. The rise of big data: how it's changing the way we think about the world.Foreign Aff92:328–40
      [Google Scholar]
    35. DeutschKW1963.The Nerves of Government: Models of Political Communication and Control New York: Free Press
      [Google Scholar]
    36. DonohoD2017. 50 years of data science.J. Comput. Graphical Stat.26:4745–66
      [Google Scholar]
    37. DunlapCJ2014. The hyper-personalization of war: cyber, big data, and the changing face of conflict.Georgetown J. Int. Aff.15:108–18
      [Google Scholar]
    38. DustdarS,NastićS,ŠćekićO2017.Smart Cities: The Internet of Things, People, and Systems New York: Springer Int. Publ.
      [Google Scholar]
    39. DzauVJ,GinsburgGS2016. Realizing the full potential of precision medicine in health and health care.JAMA316:161659–60
      [Google Scholar]
    40. EnosRD2016. What the demolition of public housing teaches us about the impact of racial threat on political behavior.Am. J. Political Sci.60:1123–42
      [Google Scholar]
    41. EvansP2018. Harnessing big data: a tsunami of transformation.Opening Government137–44 Acton, ACT, Aust.: ANU Press
      [Google Scholar]
    42. FarrellH2012. The consequences of the internet for politics.Annu. Rev. Political Sci.15:35–52
      [Google Scholar]
    43. GlaeserEL,CominersSD,LucaM,NaikN2018. Big data and big cities: the promises and limitations of improved measures of urban life.Econ. Inq.56:1114–37
      [Google Scholar]
    44. GoffPA,LloydT,GellerA2016.The science of justice: race, arrests, and police use of force Rep. Cent. Policing Equity New York, NY:
      [Google Scholar]
    45. Gomez-RodriguezM,LeskovecJ,KrauseA2012. Inferring networks of diffusion and influence.ACM Trans. Knowledge Discov. Data5:421
      [Google Scholar]
    46. GranatoJ,ScioliF2004. Puzzles, proverbs, and omega matrices: the scientific and social significance of Empirical Implications of Theoretical Models (EITM).Perspect. Politics2:2313–23
      [Google Scholar]
    47. GrayJ2009. Jim Gray on eScience: a transformed scientific method.The Fourth Paradigm: Data-Intensive Scientific Discovery T Hey, S Tansley, K Tollexvii–xxxi Redmond, WA: Microsoft Res.
      [Google Scholar]
    48. GrimmerJ,MessingS,WestwoodSJ2012. How words and money cultivate a personal vote: the effect of legislator credit claiming on constituent credit allocation.Am. Political Sci. Rev.106:4703–19
      [Google Scholar]
    49. GrimmerJ,StewartBM2013. Text as data: the promise and pitfalls of automatic content analysis methods for political texts.Political Anal21:3267–97
      [Google Scholar]
    50. HanauerDA,RhodesDR,ChinnaiyanAM2009. Exploring clinical associations using ‘-omics’ based enrichment analyses.PLOS ONE4:4e5203
      [Google Scholar]
    51. HarcourtBE2007.Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age Chicago: Univ. Chicago Press
      [Google Scholar]
    52. HashemIAT,ChangV,AnuarNB,AdewoleK,YaqoobI et al.2016. The role of big data in Smart City.Int. J. Inf. Manag.36:748–58
      [Google Scholar]
    53. HastieT,TibshiraniR,FriedmanJ2016.The Elements of Statistical Learning: Data Mining, Inference, and Prediction Stanford, CA: Stanford Univ. Press, 2nd ed..
      [Google Scholar]
    54. HershED2013. Long-term effect of September 11 on the political behavior of victims' families and neighbors.PNAS110:5220959–63
      [Google Scholar]
    55. HilbertM,LópezP2011. The world's technological capacity to store, communicate, and compute information.Science332:60–65
      [Google Scholar]
    56. HochschildJ,SenM2015. Genetic determinism, technology, optimism, and race: views of the American public.Ann. AAPSS661:160–80
      [Google Scholar]
    57. HopkinsD,KingG2010. A method of automated nonparametric content analysis for social science.Am. J. Political Sci.54:1229–47
      [Google Scholar]
    58. HsiangSM,BurkeM,MiguelE2013. Quantifying the influence of climate on human conflict.Science341:1235367
      [Google Scholar]
    59. HsiangSM,MengKC,CaneMA2011. Civil conflicts are associated with the global climate.Nature476:438–41
      [Google Scholar]
    60. JamiesonK2018.Cyber-War: How Russian Hackers and Trolls Helped Elect a President New York: Oxford Univ. Press
      [Google Scholar]
    61. JordanM2018. Artificial intelligence—the revolution hasn't happened yet.Mediumhttps://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7
      [Google Scholar]
    62. KalilT2012. Big data is a big deal. Press release, The White House, Mar. 29.https://obamawhitehouse.archives.gov/blog/2012/03/29/big-data-big-deal
    63. KandelS,PaepekeA,HellersteinHeer J2011.Wrangler: interactive visual specification of data transformation scripts Paper presented at CHI Conference on Human Factors in Computing Systems, May 7–12, Vancouver, BC
      [Google Scholar]
    64. KandelS,PaepekeA,HellersteinHeer J2012. Enterprise data analysis and visualization: an interview study.IEEE Trans. Vis. Comput. Graph.18:122917–26
      [Google Scholar]
    65. KaplanF2017.Dark Territory: The Secret History of Cyber War New York: Simon & Schuster
      [Google Scholar]
    66. KimIS2017. Political cleavages within industry: firm-level lobbying for trade liberalization.Am. Political Sci. Rev.111:11–20
      [Google Scholar]
    67. KimIS,KuniskyD2018.Mapping political communities: a statistical analysis of lobbying networks in legislative politics Work. Pap., Mass. Inst. Technol.http://web.mit.edu/insong/www/pdf/network.pdf. Accessed Dec. 2, 2018
      [Google Scholar]
    68. KingG,PanJ,RobertsME2013. How censorship in China allows government criticism but silences collective expression.Am. Political Sci. Rev.107:2326–43
      [Google Scholar]
    69. KitchinR2014. The real-time city? Big data and smart urbanism.GeoJournal79:11–14
      [Google Scholar]
    70. KitzesJ,TurekD,DenizF2017.The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences Oakland: Univ. Calif. Press
      [Google Scholar]
    71. KleinbergJ,LudwigJ,MullainathanS,ObermeyerZ2015. Prediction policy problems.Am. Econ. Rev. Pap. Proc.105:5491–95
      [Google Scholar]
    72. KnightW2017. The dark secret at the heart of AI.MIT Technol. Rev. May/June.https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
      [Google Scholar]
    73. LaneyD2001.3D data management: controlling data volume, velocity, and variety. Application Delivery Strategies File 949, Feb. 6, META Group.https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
    74. LasswellHD1951. The policy orientation.The Policy Sciences: Recent Developments in Scope and Method D Lerner, H Lasswell3–15 Stanford, CA: Stanford Univ. Press
      [Google Scholar]
    75. LaverM,BenoitK,GarryJ2003. Extracting policy positions from political texts using words as data.Am. Political Sci. Rev.97:2311–31
      [Google Scholar]
    76. LazerD,KennedyR,KingG,VespignaniA2014. The parable of Google flu: traps in big data analysis.Science343:61761203–4
      [Google Scholar]
    77. LeCunY,BengioY,HintonG2015. Deep learning.Nature521:436–44
      [Google Scholar]
    78. LeskovecJ,BackstromL,KleinbergJ2009.Meme-tracking and the dynamics of the news cycle Paper presented at 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28–July 1, Paris, France
      [Google Scholar]
    79. LibickiMC2014. Why cyber war will not and should not have its grand strategist.Strateg. Stud. Q.8:123–39
      [Google Scholar]
    80. LinH,TegmarkM,RolnickD2017. Why does deep and cheap learning work so well?.J. Stat. Phys.168:61223–47
      [Google Scholar]
    81. LugmayrA,StocklebenB,ScheibC2016. A comprehensive survey on big-data research and its implications—What is really ‘new’ in big data?—It's cognitive big data!.PACIS 2016 Proceedings Abstr. 248.https://aisel.aisnet.org/pacis2016/248
      [Google Scholar]
    82. LuksS,BradyHE2003. Defining welfare spells. Coping with problems of survey responses and administrative data.Eval. Rev.27:4395–420
      [Google Scholar]
    83. LymanP,VarianHR2003.How much information? Executive summary Rep. School Inf. Manag. Syst., Univ. Calif. Berkeley, CA:http://groups.ischool.berkeley.edu/archive/how-much-info-2003/execsum.htm
      [Google Scholar]
    84. MaimonO,RoachL2005.The Data Mining and Knowledge Discovery Handbook New York: Springer
      [Google Scholar]
    85. ManjooF2016. A plan in case robots take the jobs: give everyone a paycheck.New York Times Mar. 2.https://www.nytimes.com/2016/03/03/technology/plan-to-fight-robot-invasion-at-work-give-everyone-a-paycheck.html
      [Google Scholar]
    86. Mayer-SchönbergerV,CukierK2014.Big Data: A Revolution That Will Transform How We Live, Work, and Think Boston: Houghton Mifflin Harcourt
      [Google Scholar]
    87. MbadiweT2018. Algorithmic injustice.New Atlantis54:3–28
      [Google Scholar]
    88. MergelI2016. Big data in public affairs education.J. Public Aff. Educ.22:2231–48
      [Google Scholar]
    89. MillerK2012. Big data analytics in biomedical research.Biomed. Comput. Rev. Winter 2011/2012:14–21.http://biomedicalcomputationreview.org/content/big-data-analytics-biomedical-research
      [Google Scholar]
    90. MoscoV2014.To the Cloud: Big Data in a Turbulent World New York: Paradigm
      [Google Scholar]
    91. MullainathanS,SpiessJ2017. Machine learning: an applied econometric approach.J. Econ. Perspect.31:287–106
      [Google Scholar]
    92. NaglerJ,TuckerJA2015. Drawing inferences and testing theories with big data.PS Political Sci. Politics48:184–88
      [Google Scholar]
    93. National Research Council.2011.Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease Washington, DC: Natl. Acad. Press
      [Google Scholar]
    94. National Research Council.2013.Frontiers in Massive Data Analysis Washington, DC: Natl. Acad. Press
      [Google Scholar]
    95. NeumannR2016.The Digital Difference: Media Technology and the Theory of Communication Effects Cambridge, MA: Harvard Univ. Press
      [Google Scholar]
    96. NickersonDW,RogersT2014. Political campaigns and big data.J. Econ. Perspect.28:251–73
      [Google Scholar]
    97. NIST (Natl. Inst. Standards Technol.).2015.Big data interoperability framework: Volume 1, definitions NIST Spec. Publ. 1500-1.https://bigdatawg.nist.gov/_uploadfiles/NIST.SP.1500-1.pdf
      [Google Scholar]
    98. NITRD (Netw. Inf. Technol. Res. Dev.).2016.The federal big data research and development strategic plan Rep. Big Data Senior Steering Group, Subcomm. NITRD Washington, DC:https://www.nitrd.gov/PUBS/bigdatardstrategicplan.pdf
      [Google Scholar]
    99. NobleS2018.Algorithms of Oppression: How Search Engines Reinforce Racism New York: New York Univ. Press
      [Google Scholar]
    100. OussousA,BenjellounFZ,LahcenAA,BelfkihS2018. Big data technologies: a survey.J. King Saud Univ.—Comput. Inf. Sci.30:4431–48
      [Google Scholar]
    101. PiconA2015.Smart Cities: A Spatialised Intelligence New York: Wiley
      [Google Scholar]
    102. PiersonE,SimoiuC,OvergoorJ,OvergoorJ,Corbett-DaviesS et al.2017. A large-scale analysis of racial disparities in police stops across the United States. arXiv:1706.05678 [stat.AP]
    103. PoolIS1983. Tracking the flow of information.Science221:4611609–13
      [Google Scholar]
    104. PorcheIR,WilsonB,JohnsonEE,TierneyS,SaltzmanE2014. Barrier to benefiting from big data.Data Flood: Helping the Navy Address the Rising Tide of Sensor Information13–21 Santa Monica, CA: RAND Corp.
      [Google Scholar]
    105. PowellJ2017. Identification and asymptotic approximations: three examples of progress in econometric theory.J. Econ. Perspect.31:2107–24
      [Google Scholar]
    106. PrattGA2015. Is a Cambrian explosion coming for robotics?.J. Econ. Perspect.29:51–60
      [Google Scholar]
    107. PriorM2013. Media and political polarization.Annu. Rev. Political Sci.16:101–27
      [Google Scholar]
    108. RidT2012. Cyber war will not take place.J. Strateg. Stud.35:15–32
      [Google Scholar]
    109. RipleyBD1995.Pattern Recognition and Neural Networks New York: Cambridge Univ. Press
      [Google Scholar]
    110. RobertsM,StewartB,TingleyD,LucasC,Leder-LuisJ et al.2014. Structural topic models for open-ended survey responses.Am. J. Political Sci.58:41064–82
      [Google Scholar]
    111. RogersR2013.Digital Methods Cambridge, MA: MIT Press
      [Google Scholar]
    112. RussellS,NorvigP2009.Artificial Intelligence: A Modern Approach New York: Pearson, 3rd ed..
      [Google Scholar]
    113. SalganikMJ2017.Bit by Bit: Social Research in the Digital Age Princeton, NJ: Princeton Univ. Press
      [Google Scholar]
    114. SamuelA1962. Artificial intelligence: a frontier of automation.Ann. Am. Acad. Political Social Sci.340:10–20
      [Google Scholar]
    115. SangerDE2018.The Perfect Weapon: War, Sabotage, and Fear in the Cyber Age New York: Crown
      [Google Scholar]
    116. SarleW1994. Neural networks and statistical models.Proceedings of the Nineteenth Annual SAS Users Group International Conference, Dallas, Texas, Aprl 10–13 Cary, NC: SAS Insthttp://www.sascommunity.org/sugi/SUGI94/Sugi-94-255%20Sarle.pdf
      [Google Scholar]
    117. SchmidhuberJ2015. Deep learning in neural networks: an overview.Neural Netw61:85–117
      [Google Scholar]
    118. SchroederR2018.Social Theory after the Internet: Media, Technology, and Globalization London: UCL Press
      [Google Scholar]
    119. SchudsonM2002. The news media as political institutions.Annu. Rev. Political Sci.5:249–69
      [Google Scholar]
    120. ScottJC1999.Seeing Like a State London: Yale Univ. Press
      [Google Scholar]
    121. ShmueliG2010. To explain or to predict.Stat. Sci.25:3289–310
      [Google Scholar]
    122. SimsCA1980. Macroeconomics and reality.Econometrics48:11–48
      [Google Scholar]
    123. SmithG2018.The AI Delusion New York: Oxford Univ. Press
      [Google Scholar]
    124. Statistical Science.2003. Tribute to John W. Tukey.Stat. Sci.18:3)
      [Google Scholar]
    125. Stephens-DavidowitzS2014. The cost of racial animus on a black candidate: evidence using Google search data.J. Public Econ.118:26–40
      [Google Scholar]
    126. TankersleyJ2018. Democrats' next big thing: government-guaranteed jobs.New York Times May 22.https://www.nytimes.com/2018/05/22/us/politics/democrats-guaranteed-jobs.html
      [Google Scholar]
    127. TaylorGR1951.The Transportation Revolution 1815–1860 New York: Rinehart
      [Google Scholar]
    128. ThagardP1992.Conceptual Revolutions Princeton, NJ: Princeton Univ. Press
      [Google Scholar]
    129. TheodoridisAG,NelsonAJ2012. Of BOLD claims and excessive fears: a call for cautionand patience regarding political neuroscience.Political Psychol33:127–28
      [Google Scholar]
    130. TinatiR,HalfordS,CarrL et al.2014. Big data: methodological challenges and approaches for sociological analysis.Sociology48:4663–81
      [Google Scholar]
    131. TitiunikR2015. Can big data solve the fundamental problem of causal inference?.PS Political Sci. Politics48:175–79
      [Google Scholar]
    132. TownsendAM2013.Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia New York/London: W.W. Norton
      [Google Scholar]
    133. TukeyJ1962. The future of data analysis.Ann. Math. Stat.33:11–67
      [Google Scholar]
    134. TurnbullN2008. Harold Lasswell's “problem orientation” for the policy sciences.Crit. Policy Anal.2:272–91
      [Google Scholar]
    135. VarianHR2014. Big data: new tricks for econometrics.J. Econ. Perspect.28:23–27
      [Google Scholar]
    136. VoigtR,CampNP,PrabhakaranV et al.2017. Language from policy body camera footage shows racial disparities in officer respect.PNAS114:256521–26
      [Google Scholar]
    137. WardJS,BarkerA2013. Undefined by data: a survey of big data definitions. arXiv:1309.5821 [cs.DB]
    138. WarnerB,MisraM1996. Understanding neural networks as statistical tools.Am. Statistician50:40284–93
      [Google Scholar]
    139. WeilF2012. The sinews of society are changing.Huffington Post, Apr. 17.https://www.huffingtonpost.com/frank-a-weil/the-sinews-of-society-are_b_1277241.html
    140. WhiteH1992.Artificial Neural Networks: Approximation and Learning Theory Cambridge, MA: Blackwell
      [Google Scholar]
    141. WickhamH2014. Tidy data.J. Stat. Softw.59:101–24
      [Google Scholar]
    142. WiedemannG2013. Opening up to big data: computer-assisted analysis of textual data in social sciences.Forum Qual. Soc. Res.14:213http://www.qualitative-research.net/index.php/fqs/article/view/1949
      [Google Scholar]
    143. WignerE1960. The unreasonable effectiveness of mathematics in the natural sciences.Commun. Pure Appl. Math.13:11–14
      [Google Scholar]
    144. WilkersonJ,CasasA2017. Large-scale computerized text analysis in political science: opportunities and challenges.Annu. Rev. Political Sci.20:529–44
      [Google Scholar]
    145. WilliamsBA,BrooksCF,ShmargadY2018. How algorithms discriminate based on data they lack: challenges, solutions, and policy implications.J. Inf. Policy8:78–115
      [Google Scholar]
    146. YarkoniT,WestfallJ2017. Choosing prediction over explanation in psychology: lessons from machine learning.Perspect. Psychol. Sci.12:61100–22
      [Google Scholar]
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    Literature Cited

    1. AhlquistJA,BreunigC2012. Model-based clustering and typologies in the social sciences.Political Anal20:192–112
      [Google Scholar]
    2. AlbusJS1984. Robots and the economy.Futurist18:638–44
      [Google Scholar]
    3. AlvarezRM2016.Computational Social Science: Discovery and Prediction (Analytical Methods for Social Research) Cambridge, UK: Cambridge Univ. Press
      [Google Scholar]
    4. AnsolabehereS,HershE2012. Validation: what big data reveal about survey misreporting and the real electorate.Political Anal.20:4437–59
      [Google Scholar]
    5. AtheyS2018. Draft chapter, Natl. Bur. Econ. Res. Cambridge, MA:http://www.nber.org/chapters/c14009.pdf
    6. AtkinsDE,DroegemeierKK,FeldmanSI,Garcia-MolinaH,KleinM et al.2003.Revolutionizing science and engineering through cyberinfrastructure: report of the National Science Foundation blue-ribbon advisory panel on cyberinfrastructure Rep. Natl. Sci. Found. Washington, DC:https://stewardshipgap.net/node/17
      [Google Scholar]
    7. BailCA2014. The cultural environment: measuring culture with big data.Theory Soc43:3/4465–82
      [Google Scholar]
    8. BarberáP2015. Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data.Political Anal23:76–91
      [Google Scholar]
    9. BeachySH,OlsonS,BergerAC2015.Genomics-Enabled Learning Health Care Systems: Gathering and Using Genomic Information to Improve Patient Care and Research: Workshop Summary Washington, DC: Natl. Acad. Press
      [Google Scholar]
    10. BennettWL,SegerbergA2012. The logic of connective action.Inf. Commun. Soc.15:5739–68
      [Google Scholar]
    11. BerkRA2008.Statistical Learning from a Regression Perspective New York: Springer
      [Google Scholar]
    12. BermanF,BradyH2005.Workshop on cyberinfrastructure for the social and behavioral sciences: final report. Rep., Natl. Sci. Found., Alexandria, VA.https://www.sdsc.edu/assets/docs/SBE-CISE-FINAL.pdf. Accessed Dec. 2, 2018
    13. BishopCM2011.Pattern Recognition and Machine Learning New York: Springer
      [Google Scholar]
    14. BohnR,ShortJ2012. Measuring consumer information.Int. J. Commun.6:980–1000
      [Google Scholar]
    15. BondRM,FarissCJ,JonesJJ,KramerAD,MarlowC et al.2012. A 61-milllion-person experiment in social influence and political mobilization.Nature489:7415295–98
      [Google Scholar]
    16. BondR,MessingS2015. Quantifying social media's political space: estimating ideology from publicly revealed preferences on Facebook.Am. Political Sci. Rev.109:162–78
      [Google Scholar]
    17. BonicaA2013. Ideology and interests in the political marketplace.Am. J. Political Sci.57:2294–311
      [Google Scholar]
    18. BonicaA2016. A data-driven voter guide for U.S. elections: adapting quantitative measures of the preferences and priorities of political elites to help votes learn about candidates.RSF Russell Sage Found. J. Soc. Sci.2:711–32
      [Google Scholar]
    19. BonicaA,ChiltonA,SenM2016. The political ideologies of American lawyers.J. Legal Analysis8:2277–335
      [Google Scholar]
    20. BonicaA,RosenthalH,RothmanDJ2014. The political polarization of physicians in the United States: an analysis of campaign contributions to federal elections, 1991 through 2012.JAMA Intern. Med.174:81308–17
      [Google Scholar]
    21. BoullierD2015. The social sciences and traces of big data: society, opinion, or vibrations?.Rev. Française Sci. Politique65:5–671–93
      [Google Scholar]
    22. boydD,CrawfordK2012. Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon.Inf. Commun. Soc.15:5662–79
      [Google Scholar]
    23. BradyHE2009. Causation and explanation in political science.The Oxford Handbook of Political Science R Goodin217–70 Oxford, UK: Oxford Univ. Press
      [Google Scholar]
    24. BradyHE,GrandSA,PowellMA,SchinkW2001. Access and confidentiality issues with administrative data.Studies of Welfare Populations: Data Collection and Research Issues Natl. Res. Counc.220–74 Washington, DC: Natl. Acad. Press
      [Google Scholar]
    25. BradyHE,McNultyJE2011. Turning out to vote: the costs of finding and getting to the polling place.Am. Political Sci. Rev.105:1115–34
      [Google Scholar]
    26. BradyHE,SchlozmanKL,VerbaS1999. Prospecting for participants: rational expectations and the recruitment of political activists.Am. Political Sci. Rev.93:1153–68
      [Google Scholar]
    27. BreimanL2001. Statistical modeling: the two cultures.Stat. Sci.16:3199–231
      [Google Scholar]
    28. ChenH,ChiangRHL,StoreyVC2012. Business intelligence and analytics: from big data to big impact.MIS Q36:41165–88
      [Google Scholar]
    29. ChristianoLJ2012. Christopher A. Sims and vector autoregressions.Scand. J. Econ.114:41082–104
      [Google Scholar]
    30. ClarkWR,GolderM2015. Big data, causal inference, and formal theory: contradictory trends in political science.PS Political Sci. Politics48:165–70
      [Google Scholar]
    31. ClarkeRA,KnakeR2011.Cyber War: The Next Threat to National Security and What to Do About It New York: HarperCollins
      [Google Scholar]
    32. ClevelandWS2001. Data science: an action plan for expanding the technical areas of the field of statistics.Int. Stat. Rev.69:121–26
      [Google Scholar]
    33. Corbett-DaviesS,PiersonE,FellerA,GoelS,HuqA2017. Algorithmic decision making and the cost of fairness.Proceedings of 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Canada New York: ACMhttps://arxiv.org/abs/1701.08230
      [Google Scholar]
    34. CukierK,Mayer-SchoenbergerV2013. The rise of big data: how it's changing the way we think about the world.Foreign Aff92:328–40
      [Google Scholar]
    35. DeutschKW1963.The Nerves of Government: Models of Political Communication and Control New York: Free Press
      [Google Scholar]
    36. DonohoD2017. 50 years of data science.J. Comput. Graphical Stat.26:4745–66
      [Google Scholar]
    37. DunlapCJ2014. The hyper-personalization of war: cyber, big data, and the changing face of conflict.Georgetown J. Int. Aff.15:108–18
      [Google Scholar]
    38. DustdarS,NastićS,ŠćekićO2017.Smart Cities: The Internet of Things, People, and Systems New York: Springer Int. Publ.
      [Google Scholar]
    39. DzauVJ,GinsburgGS2016. Realizing the full potential of precision medicine in health and health care.JAMA316:161659–60
      [Google Scholar]
    40. EnosRD2016. What the demolition of public housing teaches us about the impact of racial threat on political behavior.Am. J. Political Sci.60:1123–42
      [Google Scholar]
    41. EvansP2018. Harnessing big data: a tsunami of transformation.Opening Government137–44 Acton, ACT, Aust.: ANU Press
      [Google Scholar]
    42. FarrellH2012. The consequences of the internet for politics.Annu. Rev. Political Sci.15:35–52
      [Google Scholar]
    43. GlaeserEL,CominersSD,LucaM,NaikN2018. Big data and big cities: the promises and limitations of improved measures of urban life.Econ. Inq.56:1114–37
      [Google Scholar]
    44. GoffPA,LloydT,GellerA2016.The science of justice: race, arrests, and police use of force Rep. Cent. Policing Equity New York, NY:
      [Google Scholar]
    45. Gomez-RodriguezM,LeskovecJ,KrauseA2012. Inferring networks of diffusion and influence.ACM Trans. Knowledge Discov. Data5:421
      [Google Scholar]
    46. GranatoJ,ScioliF2004. Puzzles, proverbs, and omega matrices: the scientific and social significance of Empirical Implications of Theoretical Models (EITM).Perspect. Politics2:2313–23
      [Google Scholar]
    47. GrayJ2009. Jim Gray on eScience: a transformed scientific method.The Fourth Paradigm: Data-Intensive Scientific Discovery T Hey, S Tansley, K Tollexvii–xxxi Redmond, WA: Microsoft Res.
      [Google Scholar]
    48. GrimmerJ,MessingS,WestwoodSJ2012. How words and money cultivate a personal vote: the effect of legislator credit claiming on constituent credit allocation.Am. Political Sci. Rev.106:4703–19
      [Google Scholar]
    49. GrimmerJ,StewartBM2013. Text as data: the promise and pitfalls of automatic content analysis methods for political texts.Political Anal21:3267–97
      [Google Scholar]
    50. HanauerDA,RhodesDR,ChinnaiyanAM2009. Exploring clinical associations using ‘-omics’ based enrichment analyses.PLOS ONE4:4e5203
      [Google Scholar]
    51. HarcourtBE2007.Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age Chicago: Univ. Chicago Press
      [Google Scholar]
    52. HashemIAT,ChangV,AnuarNB,AdewoleK,YaqoobI et al.2016. The role of big data in Smart City.Int. J. Inf. Manag.36:748–58
      [Google Scholar]
    53. HastieT,TibshiraniR,FriedmanJ2016.The Elements of Statistical Learning: Data Mining, Inference, and Prediction Stanford, CA: Stanford Univ. Press, 2nd ed..
      [Google Scholar]
    54. HershED2013. Long-term effect of September 11 on the political behavior of victims' families and neighbors.PNAS110:5220959–63
      [Google Scholar]
    55. HilbertM,LópezP2011. The world's technological capacity to store, communicate, and compute information.Science332:60–65
      [Google Scholar]
    56. HochschildJ,SenM2015. Genetic determinism, technology, optimism, and race: views of the American public.Ann. AAPSS661:160–80
      [Google Scholar]
    57. HopkinsD,KingG2010. A method of automated nonparametric content analysis for social science.Am. J. Political Sci.54:1229–47
      [Google Scholar]
    58. HsiangSM,BurkeM,MiguelE2013. Quantifying the influence of climate on human conflict.Science341:1235367
      [Google Scholar]
    59. HsiangSM,MengKC,CaneMA2011. Civil conflicts are associated with the global climate.Nature476:438–41
      [Google Scholar]
    60. JamiesonK2018.Cyber-War: How Russian Hackers and Trolls Helped Elect a President New York: Oxford Univ. Press
      [Google Scholar]
    61. JordanM2018. Artificial intelligence—the revolution hasn't happened yet.Mediumhttps://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7
      [Google Scholar]
    62. KalilT2012. Big data is a big deal. Press release, The White House, Mar. 29.https://obamawhitehouse.archives.gov/blog/2012/03/29/big-data-big-deal
    63. KandelS,PaepekeA,HellersteinHeer J2011.Wrangler: interactive visual specification of data transformation scripts Paper presented at CHI Conference on Human Factors in Computing Systems, May 7–12, Vancouver, BC
      [Google Scholar]
    64. KandelS,PaepekeA,HellersteinHeer J2012. Enterprise data analysis and visualization: an interview study.IEEE Trans. Vis. Comput. Graph.18:122917–26
      [Google Scholar]
    65. KaplanF2017.Dark Territory: The Secret History of Cyber War New York: Simon & Schuster
      [Google Scholar]
    66. KimIS2017. Political cleavages within industry: firm-level lobbying for trade liberalization.Am. Political Sci. Rev.111:11–20
      [Google Scholar]
    67. KimIS,KuniskyD2018.Mapping political communities: a statistical analysis of lobbying networks in legislative politics Work. Pap., Mass. Inst. Technol.http://web.mit.edu/insong/www/pdf/network.pdf. Accessed Dec. 2, 2018
      [Google Scholar]
    68. KingG,PanJ,RobertsME2013. How censorship in China allows government criticism but silences collective expression.Am. Political Sci. Rev.107:2326–43
      [Google Scholar]
    69. KitchinR2014. The real-time city? Big data and smart urbanism.GeoJournal79:11–14
      [Google Scholar]
    70. KitzesJ,TurekD,DenizF2017.The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences Oakland: Univ. Calif. Press
      [Google Scholar]
    71. KleinbergJ,LudwigJ,MullainathanS,ObermeyerZ2015. Prediction policy problems.Am. Econ. Rev. Pap. Proc.105:5491–95
      [Google Scholar]
    72. KnightW2017. The dark secret at the heart of AI.MIT Technol. Rev. May/June.https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
      [Google Scholar]
    73. LaneyD2001.3D data management: controlling data volume, velocity, and variety. Application Delivery Strategies File 949, Feb. 6, META Group.https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
    74. LasswellHD1951. The policy orientation.The Policy Sciences: Recent Developments in Scope and Method D Lerner, H Lasswell3–15 Stanford, CA: Stanford Univ. Press
      [Google Scholar]
    75. LaverM,BenoitK,GarryJ2003. Extracting policy positions from political texts using words as data.Am. Political Sci. Rev.97:2311–31
      [Google Scholar]
    76. LazerD,KennedyR,KingG,VespignaniA2014. The parable of Google flu: traps in big data analysis.Science343:61761203–4
      [Google Scholar]
    77. LeCunY,BengioY,HintonG2015. Deep learning.Nature521:436–44
      [Google Scholar]
    78. LeskovecJ,BackstromL,KleinbergJ2009.Meme-tracking and the dynamics of the news cycle Paper presented at 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28–July 1, Paris, France
      [Google Scholar]
    79. LibickiMC2014. Why cyber war will not and should not have its grand strategist.Strateg. Stud. Q.8:123–39
      [Google Scholar]
    80. LinH,TegmarkM,RolnickD2017. Why does deep and cheap learning work so well?.J. Stat. Phys.168:61223–47
      [Google Scholar]
    81. LugmayrA,StocklebenB,ScheibC2016. A comprehensive survey on big-data research and its implications—What is really ‘new’ in big data?—It's cognitive big data!.PACIS 2016 Proceedings Abstr. 248.https://aisel.aisnet.org/pacis2016/248
      [Google Scholar]
    82. LuksS,BradyHE2003. Defining welfare spells. Coping with problems of survey responses and administrative data.Eval. Rev.27:4395–420
      [Google Scholar]
    83. LymanP,VarianHR2003.How much information? Executive summary Rep. School Inf. Manag. Syst., Univ. Calif. Berkeley, CA:http://groups.ischool.berkeley.edu/archive/how-much-info-2003/execsum.htm
      [Google Scholar]
    84. MaimonO,RoachL2005.The Data Mining and Knowledge Discovery Handbook New York: Springer
      [Google Scholar]
    85. ManjooF2016. A plan in case robots take the jobs: give everyone a paycheck.New York Times Mar. 2.https://www.nytimes.com/2016/03/03/technology/plan-to-fight-robot-invasion-at-work-give-everyone-a-paycheck.html
      [Google Scholar]
    86. Mayer-SchönbergerV,CukierK2014.Big Data: A Revolution That Will Transform How We Live, Work, and Think Boston: Houghton Mifflin Harcourt
      [Google Scholar]
    87. MbadiweT2018. Algorithmic injustice.New Atlantis54:3–28
      [Google Scholar]
    88. MergelI2016. Big data in public affairs education.J. Public Aff. Educ.22:2231–48
      [Google Scholar]
    89. MillerK2012. Big data analytics in biomedical research.Biomed. Comput. Rev. Winter 2011/2012:14–21.http://biomedicalcomputationreview.org/content/big-data-analytics-biomedical-research
      [Google Scholar]
    90. MoscoV2014.To the Cloud: Big Data in a Turbulent World New York: Paradigm
      [Google Scholar]
    91. MullainathanS,SpiessJ2017. Machine learning: an applied econometric approach.J. Econ. Perspect.31:287–106
      [Google Scholar]
    92. NaglerJ,TuckerJA2015. Drawing inferences and testing theories with big data.PS Political Sci. Politics48:184–88
      [Google Scholar]
    93. National Research Council.2011.Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease Washington, DC: Natl. Acad. Press
      [Google Scholar]
    94. National Research Council.2013.Frontiers in Massive Data Analysis Washington, DC: Natl. Acad. Press
      [Google Scholar]
    95. NeumannR2016.The Digital Difference: Media Technology and the Theory of Communication Effects Cambridge, MA: Harvard Univ. Press
      [Google Scholar]
    96. NickersonDW,RogersT2014. Political campaigns and big data.J. Econ. Perspect.28:251–73
      [Google Scholar]
    97. NIST (Natl. Inst. Standards Technol.).2015.Big data interoperability framework: Volume 1, definitions NIST Spec. Publ. 1500-1.https://bigdatawg.nist.gov/_uploadfiles/NIST.SP.1500-1.pdf
      [Google Scholar]
    98. NITRD (Netw. Inf. Technol. Res. Dev.).2016.The federal big data research and development strategic plan Rep. Big Data Senior Steering Group, Subcomm. NITRD Washington, DC:https://www.nitrd.gov/PUBS/bigdatardstrategicplan.pdf
      [Google Scholar]
    99. NobleS2018.Algorithms of Oppression: How Search Engines Reinforce Racism New York: New York Univ. Press
      [Google Scholar]
    100. OussousA,BenjellounFZ,LahcenAA,BelfkihS2018. Big data technologies: a survey.J. King Saud Univ.—Comput. Inf. Sci.30:4431–48
      [Google Scholar]
    101. PiconA2015.Smart Cities: A Spatialised Intelligence New York: Wiley
      [Google Scholar]
    102. PiersonE,SimoiuC,OvergoorJ,OvergoorJ,Corbett-DaviesS et al.2017. A large-scale analysis of racial disparities in police stops across the United States. arXiv:1706.05678 [stat.AP]
    103. PoolIS1983. Tracking the flow of information.Science221:4611609–13
      [Google Scholar]
    104. PorcheIR,WilsonB,JohnsonEE,TierneyS,SaltzmanE2014. Barrier to benefiting from big data.Data Flood: Helping the Navy Address the Rising Tide of Sensor Information13–21 Santa Monica, CA: RAND Corp.
      [Google Scholar]
    105. PowellJ2017. Identification and asymptotic approximations: three examples of progress in econometric theory.J. Econ. Perspect.31:2107–24
      [Google Scholar]
    106. PrattGA2015. Is a Cambrian explosion coming for robotics?.J. Econ. Perspect.29:51–60
      [Google Scholar]
    107. PriorM2013. Media and political polarization.Annu. Rev. Political Sci.16:101–27
      [Google Scholar]
    108. RidT2012. Cyber war will not take place.J. Strateg. Stud.35:15–32
      [Google Scholar]
    109. RipleyBD1995.Pattern Recognition and Neural Networks New York: Cambridge Univ. Press
      [Google Scholar]
    110. RobertsM,StewartB,TingleyD,LucasC,Leder-LuisJ et al.2014. Structural topic models for open-ended survey responses.Am. J. Political Sci.58:41064–82
      [Google Scholar]
    111. RogersR2013.Digital Methods Cambridge, MA: MIT Press
      [Google Scholar]
    112. RussellS,NorvigP2009.Artificial Intelligence: A Modern Approach New York: Pearson, 3rd ed..
      [Google Scholar]
    113. SalganikMJ2017.Bit by Bit: Social Research in the Digital Age Princeton, NJ: Princeton Univ. Press
      [Google Scholar]
    114. SamuelA1962. Artificial intelligence: a frontier of automation.Ann. Am. Acad. Political Social Sci.340:10–20
      [Google Scholar]
    115. SangerDE2018.The Perfect Weapon: War, Sabotage, and Fear in the Cyber Age New York: Crown
      [Google Scholar]
    116. SarleW1994. Neural networks and statistical models.Proceedings of the Nineteenth Annual SAS Users Group International Conference, Dallas, Texas, Aprl 10–13 Cary, NC: SAS Insthttp://www.sascommunity.org/sugi/SUGI94/Sugi-94-255%20Sarle.pdf
      [Google Scholar]
    117. SchmidhuberJ2015. Deep learning in neural networks: an overview.Neural Netw61:85–117
      [Google Scholar]
    118. SchroederR2018.Social Theory after the Internet: Media, Technology, and Globalization London: UCL Press
      [Google Scholar]
    119. SchudsonM2002. The news media as political institutions.Annu. Rev. Political Sci.5:249–69
      [Google Scholar]
    120. ScottJC1999.Seeing Like a State London: Yale Univ. Press
      [Google Scholar]
    121. ShmueliG2010. To explain or to predict.Stat. Sci.25:3289–310
      [Google Scholar]
    122. SimsCA1980. Macroeconomics and reality.Econometrics48:11–48
      [Google Scholar]
    123. SmithG2018.The AI Delusion New York: Oxford Univ. Press
      [Google Scholar]
    124. Statistical Science.2003. Tribute to John W. Tukey.Stat. Sci.18:3)
      [Google Scholar]
    125. Stephens-DavidowitzS2014. The cost of racial animus on a black candidate: evidence using Google search data.J. Public Econ.118:26–40
      [Google Scholar]
    126. TankersleyJ2018. Democrats' next big thing: government-guaranteed jobs.New York Times May 22.https://www.nytimes.com/2018/05/22/us/politics/democrats-guaranteed-jobs.html
      [Google Scholar]
    127. TaylorGR1951.The Transportation Revolution 1815–1860 New York: Rinehart
      [Google Scholar]
    128. ThagardP1992.Conceptual Revolutions Princeton, NJ: Princeton Univ. Press
      [Google Scholar]
    129. TheodoridisAG,NelsonAJ2012. Of BOLD claims and excessive fears: a call for cautionand patience regarding political neuroscience.Political Psychol33:127–28
      [Google Scholar]
    130. TinatiR,HalfordS,CarrL et al.2014. Big data: methodological challenges and approaches for sociological analysis.Sociology48:4663–81
      [Google Scholar]
    131. TitiunikR2015. Can big data solve the fundamental problem of causal inference?.PS Political Sci. Politics48:175–79
      [Google Scholar]
    132. TownsendAM2013.Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia New York/London: W.W. Norton
      [Google Scholar]
    133. TukeyJ1962. The future of data analysis.Ann. Math. Stat.33:11–67
      [Google Scholar]
    134. TurnbullN2008. Harold Lasswell's “problem orientation” for the policy sciences.Crit. Policy Anal.2:272–91
      [Google Scholar]
    135. VarianHR2014. Big data: new tricks for econometrics.J. Econ. Perspect.28:23–27
      [Google Scholar]
    136. VoigtR,CampNP,PrabhakaranV et al.2017. Language from policy body camera footage shows racial disparities in officer respect.PNAS114:256521–26
      [Google Scholar]
    137. WardJS,BarkerA2013. Undefined by data: a survey of big data definitions. arXiv:1309.5821 [cs.DB]
    138. WarnerB,MisraM1996. Understanding neural networks as statistical tools.Am. Statistician50:40284–93
      [Google Scholar]
    139. WeilF2012. The sinews of society are changing.Huffington Post, Apr. 17.https://www.huffingtonpost.com/frank-a-weil/the-sinews-of-society-are_b_1277241.html
    140. WhiteH1992.Artificial Neural Networks: Approximation and Learning Theory Cambridge, MA: Blackwell
      [Google Scholar]
    141. WickhamH2014. Tidy data.J. Stat. Softw.59:101–24
      [Google Scholar]
    142. WiedemannG2013. Opening up to big data: computer-assisted analysis of textual data in social sciences.Forum Qual. Soc. Res.14:213http://www.qualitative-research.net/index.php/fqs/article/view/1949
      [Google Scholar]
    143. WignerE1960. The unreasonable effectiveness of mathematics in the natural sciences.Commun. Pure Appl. Math.13:11–14
      [Google Scholar]
    144. WilkersonJ,CasasA2017. Large-scale computerized text analysis in political science: opportunities and challenges.Annu. Rev. Political Sci.20:529–44
      [Google Scholar]
    145. WilliamsBA,BrooksCF,ShmargadY2018. How algorithms discriminate based on data they lack: challenges, solutions, and policy implications.J. Inf. Policy8:78–115
      [Google Scholar]
    146. YarkoniT,WestfallJ2017. Choosing prediction over explanation in psychology: lessons from machine learning.Perspect. Psychol. Sci.12:61100–22
      [Google Scholar]

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