Sociology in the Era of Big Data: The Ascent of Forensic Social Science

American Sociologist

Volumn 47, Issue 1, 2016, Pages 12-35

  McFarland, Daniel A ^a   Lewis, Kevin ^b   Goldberg, Amir ^a  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Big data; Computational social science; Forensic social science; Sociology of science

Indexed keywords

EID: 84959119436     PISSN: 00031232     EISSN: None     Source Type: Journal    
DOI: 10.1007/s12108-015-9291-8     Document Type: Article

References (92)

1. Abbott, A. (1988). Transcending general linear reality. Sociological Theory, 6(2), 169–86.
2. Agresti, A., & Finlay, B. (2009). Statistical methods for the social sciences (4th ed.). Upper Saddle River: Prentice Hall.
3. Alpaydin, E. (2004). Introduction to machine learning. Cambridge: MIT Press.
4. Anand, G. (2010). A weird way of thinking has prevailed worldwide. New York Times (August 25, 2010).
5. Anderson, M. J. (1988). The American census: a social history. New York: Yale University Press.
6. Anderson, A., McFarland, D. A., & Jurafsky, D. (2012). Towards a computational history of the ACL: 1980–2008. Association of Computational Linguistics, Workshop (ACL Workshop 2012).
7. Backstrom, L., Kleinberg, J., Lee, L., & Danescu-Niculescu-Mizil, C. (2013). Characterizing and curating conversation threads: expansion, focus, volume, re-entry. Proceedings of WSDM, 2013.
8. Bail, C. A. (2014). The cultural environment: measuring culture with big data. Theory and Society, 43, 465–482.
9. Barabasi, A. (2003). Linked: How everything is connected to everything else and what it means for business, science, and everyday life. New York: Plume.
10. Bender-deMoll, S., & McFarland, D. A. (2006). The art and science of dynamic network visualization. Journal of Social Structure, 7(2).
11. Berger, P., & Luckmann, T. (1966). The social construction of reality: a treatise in the sociology of knowledge. New York: Anchor.
12. Bishop, C. (2007). Pattern recognition and machine learning (information science and statistics). Cambridge: Springer.
13. Blei, D. (2012). Probabilistic topic models. Review article, Communication of the ACM, 55(4), 77–84.
14. Bohn, A., Buchta, C., Hornik, K., & Mair, P. (2014). Making friends and communicating on facebook: implications for the access to social capital. Social Networks, 37, 29–41.
15. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323, 892–95.
16. Boyd, D., & Crawford, K. (2012). Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15, 662–79.
17. Brandes, U., Robins, G., McCranie, A., & Wasserman, S. (2013). What is network science? Network Science, 1, 1–15.
18. Brown, J. S., & Duguid, P. (2002). The social life of information. Harvard Business Review Press
19. Bruch, E. E., & Mare, R. D. (2012). Methodological issues in the analysis of residential preferences and residential mobility. Sociological Methodology, 42, 103–54.
20. Camic, C., & Xie, Y. (1994). The statistical turn in American social science: Columbia University, 1890 to 1915. American Sociological Review, 59(5), 773–805.
21. Castells, M., Fernández-Ardèvol, M., Qiu, J. L., & Sey, A. (2007). Mobile communication and society: a global perspective. Cambridge: MIT Press.
22. Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–97.
23. Coleman, J. S. (1986). Social theory, social research, and a theory of action. American Journal of Sociology, 91(6), 1309–35.
24. Coleman, J. S. (1994a). Foundations of social theory. Cambridge: Belknap Press.
25. Coleman, J. S. (1994b). A vision for sociology. Society, 30, 29–34.
26. Collins, H., Evans, R., & Gorman, M. (2007). Trading zones and interactional expertise. Studies in History and Philosophy of Science, 38(4), 657–66.
27. Converse, J. M. (1987). Survey research in the United States: roots and emergence 1890–1960. Berkeley: University of California Press.
28. Cukier, K., & Mayer-Schoenberge, V. (2013). The rise of big data: how it’s changing the way we think about the world. Foreign Affairs, 28–41.
29. Diehl, D., & McFarland, D. A. (2010). Towards a historical sociology of situations. American Journal of Sociology, 115(6), 1713–52.
30. Dodds, P. S., Muhamad, R., & Watts, D. (2003). An experimental study of search in global social networks. Science, 301(5634), 827–9.
31. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: reasoning about a highly connected world. Cambridge: Cambridge University Press.
32. Einav, L., Levin, J., Popov, I., & Sundaresan, N. (2014). Growth, adoption and use of mobile e-commerce. American Economic Review: Papers and Proceedings, 104(5), 489–94.
33. Fleck, L. (1979). Genesis and development of a scientific fact. Chicago: University of Chicago Press.
34. Galison, P. (1997). Image and logic: a material culture of microphysics. Chicago: University of Chicago Press.
35. Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: strategies for qualitative research. Chicago: Aldine Pub. Co.
36. Goldberg, A. (in press). In defense of forensic social science. Big Data & Society.
37. Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep and daylength across diverse cultures. Science, 333(6051), 1878–81.
38. Golder, S. A., & Macy, M. W. (2014). Digital footprints: opportunities and challenges for online social research. Annual Review of Sociology, 40, 129–52.
39. González-Bailón, S., Borge-Holthoeter, J., Rivero, A., & Moreno, Y. (2011). The dynamics of protest recruitment through an online network. Scientific Reports, 1, 197.
40. González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J., & Moreno, Y. (2014). Assessing the bias in samples of large online networks. Social Networks, 38, 16–27.
41. Grimmer, J., Westwood, S. J., & Messing, S. (2014). The impression of influence: legislator communication, representation, and democratic accountability. Princeton: Princeton University Press.
42. Hacking, I. (2006). The emergence of probability. Cambridge: Cambridge University Press.
43. Hilbert, M., & López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–5.
44. Jurafsky, D., & Martin, J. H. (2009). Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. New York: Prentice Hall.
45. Kagan, J. (2009). The three cultures: natural sciences, social sciences, and the humanities in the 21st century. New York: Cambridge University Press.
46. Kirchner, C., & Mohr, J. W. (2010). “Meanings and relations: an introduction to the study of language, discourse, and networks.”. Poetics, 38(6), 555–66.
47. Kohavi, R., & Longbotham, R. (2007). Online experiments: lessons learned. Computer, 40(9), 103–5.
48. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.
49. Kuhn, T. S. (1996). The structure of scientific revolutions (3rd ed.). Chicago: University of Chicago Press.
50. Latour, B. (1988). Science in action. Cambirdge: Harvard University Press.
51. Latour, B., & Woolgar, S. (1986). Laboratory life: the construction of scientific facts (2nd ed.). Princeton: Princeton University Press.
52. Laumann, E. O., Marsden, P., & Prensky, D. (1983). “The boundary specification problem in network analysis.”. In R. S. Burt & M. J. Minor (Eds.), Applied network analysis: A methodological introduction. London: Sage Publications.
53. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Alstyne, M. V. (2009). Computational social science. Science, 323(5915), 721–3.
54. Leskovec, J., & Horvitz, E. (2008). Planetary-scale views on a large instant-messaging network. International World Wide Web Conference (WWW).
55. Leskovec, J., Lang, K., & Mahoney, M. (2010). Empirical comparison of algorithms for network community detection. In WWW ’10: Proceedings of the 19th International Conference on World Wide Web. New York: ACM.
56. Levine, D. N. (1995). Visions of the sociological tradition. Chicago: University of Chicago Press.
57. Lewis, K. (2015). Studying online behavior: comment on Anderson et al. 2014. Sociological Science, 2, 20–31.
58. Lewis, K. (in press). Three fallacies of digital footprints. Big Data & Society.
59. Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., & Christakis, N. (2008). Tastes, ties, and time: a new social network dataset using facebook.com. Social Networks, 30(4), 330–42.
60. Lohr, S. (2012). “The Age of Big Data.” New York Times (February 11, 2012)
61. Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge: MIT Press.
62. McCallum, A., Corrada-Emmanuel, A., & Wang, X. (2005). Topic and role discovery in social networks. IJCAI (International Joint Conferences on Artificial Intelligence).
63. McFarland, D.A. and H.R. McFarland. (in press). Big data and the danger of being precisely inaccurate. Big Data & Society.
64. McFarland, D. A., Diehl, D., & Rawlings, C. (2011). “Methodological transactionalism and the sociology of education.”. In H. Maureen (Ed.), Chapter 5 in Frontiers in sociology of education (pp. 87–109). New York: Springer.
65. McFarland, D. A., Manning, C. D., Ramage, D., Chuang, J., Heer, J., & Jurafsky, D. (2013a). Differentiating language usage through topic models. Poetics, 41(6), 607–25.
66. McFarland, D. A., Jurafsky, D., & Rawlings, C. (2013b). Making the connection: social bonding in courtship situations. American Journal of Sociology, 118(6), 1596–1649.
67. Menand, L. (2010). The marketplace of ideas: issues of our time. New York: W.W. Norton & Company.
68. National Research Council. (2014). Convergence: Facilitating transdisciplinary integration of life sciences, physical sciences, engineering and beyond. National Research Council.
69. Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98, 404–409.
70. Newman, M. E. J. (2009). Networks: an introduction. Oxford: Oxford University Press.
71. Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 026113.
72. Pentland, A. (2014). Social physics: How good ideas spread--the lessons from a new science. New York: Penguin Press.
73. Platt, J. (1996). A history of sociological research methods in America, 1920–1960. Cambridge: Cambridge University Press.
74. Porter, T. M. (1995). Trust in numbers. Princeton: Princeton University Press.
75. Porter, T. M., & Ross, D. (Eds.). (2003). The modern social sciences. New York: Cambridge University Press.
76. Ranganath, R., Jurafsky, D., & McFarland, D. A. (2012). Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates. Computer Speech and Language, 27(1), 89–115.
77. Rogers, E. M. (1987). Progress, problems and prospects for network research: investigating relationships in the age of electronic communication technologies. Social Networks, 9, 285–310.
78. Rosenfeld, M. J., & Thomas, R. J. (2012). Searching for a mate: the rise of the internet as a social intermediary. American Sociological Review, 77(4), 523–47.
79. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854–6.
80. Shi, X., Leskovec, J., & McFarland, D. A. (2010). Citing for high impact. Joint Conference on Digital Libraries, (JCDL 2010).
81. Shwed, U., & Bearman, P. S. (2010). The temporal structure of scientific consensus formation. American Sociological Review, 75(6), 817–40.
82. Smith, A., & Duggan, M. (2013). Online dating & relationships. Washington: Pew Research Center.
83. Snow, C. P. (2001). The two cultures. London: Cambridge University Press. 1959.
84. Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: cognitive consequences of having information at our fingertips. Science, 333, 776–8.
85. Stokes, D. E. (1997). Pasteur’s quadrant: basic science and technological innovation. Washington: Brookings Institution Press.
86. Stouffer, S. A. (1949). In The American Soldier, 4 vols Studies in social psychology during World War II. Princeton, NJ: Princeton University Press.
87. Szell, M., & Thurner, S. (2010). Measuring social dynamics in a massive multiplayer online game. Social Networks, 32, 313–29.
88. Talley, E., Newman, D., Herr, B., II, Wallach, H., Burns, G., Leenders, M., & McCallum, A. (2011). A database of national institutes of health (NIH) research using machine learned categories and graphically clustered grant awards. Nature Methods, 8, 443–4.
89. Vaisey, S. (2009). Motivation and justification: a dual-process model of culture in action. American Journal of Sociology, 114, 1675–1715.
90. Vaughan, D. (2014). Analogy, cases, and comparative social organization. In R. Swedberg (Ed.), Theorizing in social science: the context of discovery (pp. 61–84). Stanford: Stanford University Press.
91. Wang, D. J., Shi, X., McFarland, D. A., & Leskovec, J. (2012). Measurement error in social network data: a re-classification. Social Networks, 34(4), 396–409.
92. Wasserman, S., & Faust, K. (1994). Social network analysis: methods and applications. Cambridge: Cambridge University Press.