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Annual Review of Sociology

Volume 42, 2016

Machine Translation: Mining Text for Social Theory

Abstract

More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring () collective attention and reasoning through the content of communication; () social relationships through the process of communication; and () social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight.

Keyword(s): big data, computational methods, content analysis, grounded theory, machine learning, natural language processing, text analysis
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2016-07-30
2024-04-24
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Literature Cited

  1. Adamic LA, Lento TM, Adar E, Ng PC. 2014. Information evolution in social networks. arXiv:1402.6792v1 [cs.SI]
  2. Alberti C, Weiss D, Coppola G, Petrov S. 2015. Improved transition-based parsing and tagging with neural networks. Proc. 2015 Conf. Empir. Methods Nat. Lang. Process Sept. 17–21 1354–59 Seattle, WA: Assoc. Comput. Linguist. [Google Scholar]
  3. Aral S, Van Alstyne M. 2011. The diversity-bandwidth trade-off. Am. J. Sociol. 117:190–171 [Google Scholar]
  4. Bail CA. 2012. The fringe effect civil society organizations and the evolution of media discourse about Islam since the September 11th attacks. Am. Sociol. Rev. 77:6855–79 [Google Scholar]
  5. Bail CA. 2014. The cultural environment: measuring culture with big data. Theory Soc. 43:3–4465–82 [Google Scholar]
  6. Bail CA. 2016. Cultural carrying capacity: organ donation advocacy, discursive framing, and social media engagement. Soc. Sci. Med. In press. doi:10.1016/j.socscimed.2016.01.049 [Google Scholar]
  7. Baker SR, Bloom N, Davis SJ. 2013. Measuring economic policy uncertainty NBER Work. Pap. 21633, Natl. Bur. Econ. Res., Cambridge, MA
  8. Bakshy E, Messing S, Adamic LA. 2015. Exposure to ideologically diverse news and opinion on Facebook. Science 348:1130–32 [Google Scholar]
  9. Bales RF. 1950. A set of categories for the analysis of small group interaction. Am. Sociol. Rev. 15:2257–63 [Google Scholar]
  10. Berelson B, Lazarsfeld PF. 1948. The Analysis of Communication Content Oslo, Nor: Univ. Stud.
  11. Blackburn J, Kwak H. 2014. STFU NOOB!: predicting crowdsourced decisions on toxic behavior in online games. Proc. 23rd Int. Conf. World Wide Web, Apr. 7–11877–88 New York: Assoc. Comput. Mach. [Google Scholar]
  12. Blei DM. 2012. Probabilistic topic models. Commun. ACM 55:477–84 [Google Scholar]
  13. Blei DM, Lafferty JD. 2006. Dynamic topic models. Proc. 23rd Int. Conf. Mach. Learn. June 25–29113–20 New York: Assoc. Comput. Mach. [Google Scholar]
  14. Blei DM, Ng AY, Jordan MI. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3:993–1022 [Google Scholar]
  15. Bollen J, Mao H, Zeng X. 2011. Twitter mood predicts the stock market. J. Comput. Sci. 2:11–8 [Google Scholar]
  16. Bonilla T, Grimmer J. 2013. Elevated threat levels and decreased expectations: How democracy handles terrorist threats. Poetics 41:6650–69 [Google Scholar]
  17. Bourdieu P. 2013. Distinction: A Social Critique of the Judgement of Taste Abington, UK: Routledge
  18. Cadilhac A, Asher N, Benamara F, Lascarides A. 2013. Grounding strategic conversation: using negotiation dialogues to predict trades in a win-lose game. Proc. 2013 Conf. Empir. Methods Nat. Lang. Process., Oct. 18–21357–68 Seattle, WA: Assoc. Comput. Linguist. [Google Scholar]
  19. Carley K. 1993. Coding choices for textual analysis: a comparison of content analysis and map analysis. Sociol. Methodol. 23:75–126 [Google Scholar]
  20. Carley K. 1994. Extracting culture through textual analysis. Poetics 22:4291–312 [Google Scholar]
  21. Carley KM, Kaufer DS. 1993. Semantic connectivity: an approach for analyzing symbols in semantic networks. Commun. Theory 3:3183–213 [Google Scholar]
  22. Chang J, Blei DM. 2010. Hierarchical relational models for document networks. Ann. Appl. Stat. 4:124–50 [Google Scholar]
  23. Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J. 2014. Can cascades be predicted? See Blackburn & Kwak, 2014, pp. 925–36
  24. Cheng J, Danescu-Niculescu-Mizil C, Leskovec J. 2015. Antisocial behavior in online discussion communities. arXiv:1504.00680v1 [cs.SI]
  25. Clark A, Fox C, Lappin S. 2010. The Handbook of Computational Linguistics and Natural Language Processing Oxford, UK: Wiley-Blackwell
  26. Corman SR, Kuhn T, Mcphee RD, Dooley KJ. 2002. Studying complex discursive systems: centering resonance analysis of communication. Hum. Commun. Res. 28:2157–206 [Google Scholar]
  27. Danescu-Niculescu-Mizil C, Lee L, Pang B, Kleinberg J. 2012. Echoes of power: language effects and power differences in social interaction. Proc. 21st Int. Conf. World Wide Web, Apr. 16–20699–708 New York: Assoc. Comput. Mach. [Google Scholar]
  28. Danescu-Niculescu-Mizil C, West R, Jurafsky D, Leskovec J, Potts C. 2013. No country for old members: user lifecycle and linguistic change in online communities. Proc. 22nd Int. Conf. World Wide Web, May 13–17307–18 New York: Assoc. Comput. Mach. [Google Scholar]
  29. Dascal M. 2006. Adam Smith's theory of language. The Cambridge Companion to Adam Smith79–111 Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  30. de Marneffe M-C, Dozat T, Silveira N, Haverinen K, Ginter F. et al. 2014. Universal Stanford dependencies: a cross-linguistic typology. Proc. 9th Int. Conf. Lang. Resour. Eval. http://www.lrec-conf.org/proceedings/lrec2014/index.html
  31. DiMaggio P, Nag M, Blei D. 2013. Exploiting affinities between topic modeling and the sociological perspective on culture: application to newspaper coverage of U.S. government arts funding. Poetics 41:6570–606 [Google Scholar]
  32. Fligstein N, Brundage JS, Schultz M. 2014. Why the Federal Reserve failed to see the financial crisis of 2008: the role of “macroeconomics” as a sense making and cultural frame IRLE Work. Pap. #111-14, Inst. Res. Labor Employ., Univ. Calif. Berkeley
  33. Foster JG, Rzhetsky A, Evans JA. 2015. Tradition and innovation in scientists' research strategies. Am. Sociol. Rev. 80:875–908 [Google Scholar]
  34. Foulds JR, Smyth P. 2013. Modeling scientific impact with topical influence regression. See Cadilhac et al. 2013. 113–23
  35. Franzosi R. 2004. From Words to Numbers: Narrative, Data, and Social Science Cambridge, UK: Cambridge Univ. Press
  36. Freese J. 2007. Replication standards for quantitative social science: Why not sociology?. Sociol. Methods Res. 36:2153–72 [Google Scholar]
  37. Garfinkel H. 1967. Studies in Ethnomethodology Malden, MA: Blackwell
  38. Gentzkow M, Shapiro JM. 2010. What drives media slant? Evidence from U.S. daily newspapers. Econometrica 78:135–71 [Google Scholar]
  39. Gibson DR. 2012. Talk at the Brink: Deliberation and Decision during the Cuban Missile Crisis Princeton, NJ: Princeton Univ. Press
  40. Glaser BG. 1965. The constant comparative method of qualitative analysis. Soc. Probl. 12:4436–45 [Google Scholar]
  41. Glaser BG, Strauss AL. 1967. The Discovery of Grounded Theory; Strategies for Qualitative Research Chicago: Aldine
  42. Goffman E. 2005. Interaction Ritual: Essays in Face to Face Behavior New Brunswick, NJ: Aldine Trans.
  43. Goldberg A, Srivastava SB, Manian VG, Monroe W, Potts C. 2015a. Enculturation trajectories: An interactional language use model of cultural dynamics in organizations. Work. Pap., Haas School Bus., Univ. Calif. Berkeley
  44. Goldberg A, Srivastava SB, Manian VG, Monroe W, Potts C. 2015b. Fitting in or standing out? The tradeoffs of structural and cultural embeddedness. Work. Pap. 3285, Stanford Univ. Grad. School Bus.
  45. Golder SA, Macy MW. 2011. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333:60511878–81 [Google Scholar]
  46. Golder SA, Macy MW. 2014. Digital footprints: opportunities and challenges for online social research. Annu. Rev. Sociol. 40:1129–52 [Google Scholar]
  47. Griffiths TL, Steyvers M, Blei DM, Tenenbaum JB. 2005. Integrating topics and syntax. Advances in Neural Information Processing Systems 17 LK Saul, Y Weiss, L Bottou 537–44 Cambridge, MA: MIT Press [Google Scholar]
  48. Grimmer J. 2010. A Bayesian hierarchical topic model for political texts: measuring expressed agendas in senate press releases. Polit. Anal. 18:11–35 [Google Scholar]
  49. Grimmer J. 2013. Appropriators not position takers: the distorting effects of electoral incentives on congressional representation. Am. J. Polit. Sci. 57:3624–42 [Google Scholar]
  50. Grimmer J, King G. 2011. General purpose computer-assisted clustering and conceptualization. PNAS 108:72643–50 [Google Scholar]
  51. Grimmer J, Stewart BM. 2013. Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Polit. Anal. 21:2667–97 [Google Scholar]
  52. Grogger J. 2011. Speech patterns and racial wage inequality. J. Hum. Resour. 46:11–25 [Google Scholar]
  53. Gross DP. 2014. Creativity under fire: the effects of competition on innovation and the creative process Work. Pap., Job Market Pap., Univ. Calif. Berkeley
  54. Hardt H. 2001. Social Theories of the Press: Constituents of Communication Research, 1840s to 1920s Lanham, MD: Rowman & Littlefield
  55. Hasan KS, Ng V. 2014. Why are you taking this stance? Identifying and classifying reasons in ideological debates. Proc. 2014 Conf. Empir. Methods Nat. Lang. Process Oct. 25–29751–62 Seattle, WA: Assoc. Comput. Linguist [Google Scholar]
  56. Hays DC. 1960. Automatic Content Analysis Santa Monica, CA: Rand Corp.
  57. Hearst MA. 1997. TextTiling: segmenting text into multi-paragraph subtopic passages. Comput. Linguist. 23:133–64 [Google Scholar]
  58. Hinton GE, Osindero S, Teh Y-W. 2006. A fast learning algorithm for deep belief nets. Neural Comput. 18:71527–54 [Google Scholar]
  59. Hirschberg J, Manning CD. 2015. Advances in natural language processing. Science 349:6245261–66 [Google Scholar]
  60. Huizinga J. 1971. Homo Ludens: A Study of the Play-Element in Culture Boston, MA: Beacon
  61. Ioannidis JP. 2005. Why most published research findings are false. PLOS Med 2:8e124 [Google Scholar]
  62. Ioannidis JP, Doucouliagos C. 2013. What's to know about the credibility of empirical economics?. J. Econ. Surv. 27:5997–1004 [Google Scholar]
  63. Iyyer M, Enns P, Boyd-Graber J, Resnik P. 2014. Political ideology detection using recursive neural networks. Proc. 52nd Annu. Meet. Assoc. Comput. Linguist.: Styst. Demonstr., Jun. 23–25, Baltimore, MD1113–22 Stroudsburg, PA: Assoc. Comput. Linguist. [Google Scholar]
  64. Jakobson R. 1960. Closing statement: linguistics and poetics. In Style in Language, ed. TA Sebeok 350–77 Cambridge, MA: MIT Press [Google Scholar]
  65. Jelveh Z, Kogut B, Naidu S. 2014. Detecting latent ideology in expert text: evidence from academic papers in economics. See Hasan & Ng, 2014 1804–9
  66. Jockers ML, Mimno D. 2013. Significant themes in 19th-century literature. Poetics 41:6750–69 [Google Scholar]
  67. Jordan MI, Mitchell TM. 2015. Machine learning: trends, perspectives, and prospects. Science 349:6245255–60 [Google Scholar]
  68. Joshi M, Das D, Gimpel K, Smith NA. 2010. Movie reviews and revenues: an experiment in text regression. Proc. Hum. Lang. Technol. 2010 Annu. Conf. N. Am. Chapter Assoc. Comput. Linguist293–96 Stroudsburg, PA: Assoc. Comput. Linguist. [Google Scholar]
  69. Jurafsky D, Martin JH. 2000. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition Upper Saddle River, NJ: Prentice Hall
  70. Kemp C, Tenenbaum JB. 2008. The discovery of structural form. PNAS 105:3110687–92 [Google Scholar]
  71. Klingenstein S, Hitchcock T, DeDeo S. 2014. The civilizing process in London's Old Bailey. PNAS 111:269419–24 [Google Scholar]
  72. Kramer ADI, Guillory JE, Hancock JT. 2014. Experimental evidence of massive-scale emotional contagion through social networks. PNAS 111:248788–90 [Google Scholar]
  73. Kuhn T, Perc M, Helbing D. 2014. Inheritance patterns in citation networks reveal scientific memes. Phys. Rev. X 4:4041036 [Google Scholar]
  74. Kulkarni V, Al-Rfou R, Perozzi B, Skiena S. 2015. Statistically significant detection of linguistic change. Proc. 24th Int. Conf. World Wide Web May 18–22 625–35 New York: Assoc. Comput. Mach. [Google Scholar]
  75. Laver M, Benoit K, Garry J. 2003. Extracting policy positions from political texts using words as data. Am. Polit. Sci. Rev. 97:02311–31 [Google Scholar]
  76. Lee M, Martin JL. 2015. Coding, counting and cultural cartography. Am. J. Cult. Sociol. 3:11–33 [Google Scholar]
  77. Leskovec J, Backstrom L, Kleinberg J. 2009. Meme-tracking and the dynamics of the news cycle. Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.497–506 New York: Assoc. Comput. Mach. [Google Scholar]
  78. Livne A, Simmons MP, Adar E, Adamic LA. 2011. The party is over here: structure and content in the 2010 election. Proc. Fifth Int. AAAI Conf. Weblogs Soc. Media., Barcelona, July 17–21
  79. Long NE. 1958. The local community as an ecology of games. Am. J. Sociol. 64:3251–61 [Google Scholar]
  80. Loughran T, McDonald B. 2011. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66:135–65 [Google Scholar]
  81. Manning CD. 2015. Computational linguistics and deep learning. Comput. Linguist. 41:701–7 [Google Scholar]
  82. Manning CD, Raghavan P, Schütze H. 2008. Introduction to Information Retrieval Cambridge, UK: Cambridge Univ. Press
  83. Manning CD, Schütze H. 1999. Foundations of Statistical Natural Language Processing Cambridge, MA: MIT Press
  84. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McCloskey D. 2014. The Stanford CoreNLP natural language processing toolkit. See Iyyer et al. 2014, pp. 55–60
  85. Marshall EA. 2013. Defining population problems: using topic models for cross-national comparison of disciplinary development. Poetics 41:6701–24 [Google Scholar]
  86. Massy WF. 1965. Principal components regression in exploratory statistical research. J. Am. Stat. Assoc. 60:309234–56 [Google Scholar]
  87. McAuliffe JD, Blei DM. 2008. Supervised topic models. Advances in Neural Information Processing Systems 20 JC Platt, D Koller, Y Singer 121–28 Cambridge, MA: MIT Press [Google Scholar]
  88. McFarland DA, Jurafsky D, Rawlings C. 2013a. Making the connection: social bonding in courtship situations. Am. J. Sociol. 118:61596–649 [Google Scholar]
  89. McFarland DA, Ramage D, Chuang J, Heer J, Manning CD, Jurafsky D. 2013b. Differentiating language usage through topic models. Poetics 41:6607–25 [Google Scholar]
  90. Michel J-B, Shen YK, Aiden AP, Veres A, Gray MK. et al. 2011. Quantitative analysis of culture using millions of digitized books. Science 331:6014176–82 [Google Scholar]
  91. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. 2013. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 26 CJC Burges, L Bottou, M Welling, Z Ghahramani, KQ Weinberger 3111–19 Red Hook, NY: Curran [Google Scholar]
  92. Miller IM. 2013. Rebellion, crime and violence in Qing China, 1722–1911: a topic modeling approach. Poetics 41:6626–49 [Google Scholar]
  93. Mohr JW, Bogdanov P. 2013. Introduction—topic models: What they are and why they matter. Poetics 41:6545–69 [Google Scholar]
  94. Mohr JW, Wagner-Pacifici R, Breiger RL, Bogdanov P. 2013. Graphing the grammar of motives in national security strategies: cultural interpretation, automated text analysis and the drama of global politics. Poetics 41:6670–700 [Google Scholar]
  95. Mosteller F, Wallace DL. 1964. Inference and Disputed Authorship: The Federalist Boston, MA: Addison-Wesley
  96. Mukherjee S, Weikum G, Danescu-Niculescu-Mizil C. 2014. People on drugs: credibility of user statements in health communities. Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.65–74 New York: Assoc. Comput. Mach. [Google Scholar]
  97. Myerson RB. 2013. Game Theory: Analysis of Conflict Cambridge, MA: Harvard Univ. Press
  98. Nelson LK. 2015. Political logics as cultural memory: cognitive structures, local continuities, and women's organizations in Chicago and New York City Work. Pap. Kellogg School Manag., Northwestern Univ.
  99. Nguyen TH, Shirai K. 2015. Topic modeling based sentiment analysis on social media for stock market prediction. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Jt. Conf. Nat. Lang. Process. Int. Beijing: July 26–31 1354–64 http://www.anthology.aclweb.org/P/P15/P15-1131.pdf [Google Scholar]
  100. Niculae V, Kumar S, Boyd-Graber J, Danescu-Niculescu-Mizil C. 2015. Linguistic harbingers of betrayal: a case study on an online strategy game. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process. Beijing: July 26–31 1650–59 [Google Scholar]
  101. O'Neil C, Schutt R. 2013. Doing Data Science: Straight Talk from the Frontline Sebastopol, CA: O'Reilly
  102. Pachucki MA, Breiger RL. 2010. Cultural holes: beyond relationality in social networks and culture. Annu. Rev. Sociol. 36:1205–24 [Google Scholar]
  103. Pang B, Lee L. 2008. Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2:1–21–135 [Google Scholar]
  104. Pennebaker JW, Francis ME, Booth RJ. 2001. Linguistic Inquiry and Word Count: LIWC 2001 Mahwah, NJ: Erlbaum
  105. Pennington J, Socher R, Manning C. 2014. Glove: global vectors for word representation. See Hasan & Ng 2014. 1532–43
  106. Pentland A. 2012a. Big data's biggest obstacles. Harv. Bus. Rev. October. https://hbr.org/2012/10/big-datas-biggest-obstacles
  107. Pentland A. 2012b. The new science of building great teams. Harv. Bus. Rev. 90:460–69 [Google Scholar]
  108. Pentland A. 2014. Social Physics: How Good Ideas Spread—The Lessons from a New Science New York: Penguin
  109. Prabhakaran V, Arora A, Rambow O. 2014a. Staying on topic: an indicator of power in political debates. See Hasan & Ng 2014,. 1481–86
  110. Prabhakaran V, Rambow O, Diab M. 2012. Predicting overt display of power in written dialogs. Proc. 2012 Conf. N. Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. Montreal, Can., Jun. 3–8 518–22 Stroudsburg, PA: Assoc. Comput. Linguist. [Google Scholar]
  111. Prabhakaran V, Reid EE, Rambow O. 2014b. Gender and power: How gender and gender environment affect manifestations of power. See Hasan & Ng 2014,. 1965–76
  112. Resnik P, Garron A, Resnik R. 2013. Using topic modeling to improve prediction of neuroticism and depression in college students. See Cadilhac et al. 2013. 1348–53
  113. Rickford JR, Duncan GJ, Gennetian LA, Gou RY, Greene R. et al. 2015. Neighborhood effects on use of African-American vernacular English. PNAS 112:3811817–22 [Google Scholar]
  114. Rodriguez-Esteban R, Rzhetsky A. 2008. Six senses in the literature. The bleak sensory landscape of biomedical texts. EMBO Rep. 9:3212–15 [Google Scholar]
  115. Romero DM, Swaab RI, Uzzi B, Galinsky AD. 2015. Mimicry is presidential linguistic style matching in presidential debates and improved polling numbers. Pers. Soc. Psychol. Bull. 41:101311–19 [Google Scholar]
  116. Roscigno VJ, Hodson R. 2004. The organizational and social foundations of worker resistance. Am. Sociol. Rev. 69:114–39 [Google Scholar]
  117. Rule A, Cointet J-P, Bearman PS. 2015. Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790–2014. PNAS 112:10837–44 [Google Scholar]
  118. Rzhetsky A, Foster JG, Foster IT, Evans JA. 2015. Choosing experiments to accelerate collective discovery. PNAS 112:4714569–74 [Google Scholar]
  119. Saavedra S, Hagerty K, Uzzi B. 2011. Synchronicity, instant messaging, and performance among financial traders. PNAS 108:135296–301 [Google Scholar]
  120. Sacks H. 1995. Lectures on Conversation1–131 Malden, MA: Wiley-Blackwell
  121. Santorini B. 1990. Part-of-speech tagging guidelines for the Penn Treebank Project (3rd revision) Univ. Pa. Dep. Comput. Inf. Sci. Tech. Rep. No. MS-CIS-90-47
  122. Schank RC, Colby KM. 1973. Computer Models of Thought and Language San Francisco: Freeman
  123. Schegloff EA. 1995. Introduction. See Sacks 1995. ix–lxii
  124. Sedelow SY. 1989. The interlingual thesaurus model for global technical communication: research results. Proc. Ann. East. Mich. Univ. Conf. Lang, Commun. World Bus. Prof., 8th, Ann Arbor, Mar. 30–April 1
  125. Shahaf D, Guestrin C, Horvitz E. 2012. Metro maps of science. Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. Beijing, Aug. 12–16 1122–30 New York: Assoc. Comput. Mach. [Google Scholar]
  126. Shapin S. 1994. A Social History of Truth: Civility and Science in Seventeenth-Century England Chicago: Univ. Chicago Press
  127. Shi F, Foster J, Evans J. 2015. Weaving the fabric of science: dynamic network models of science's unfolding structure. Social Networks 43:73–85 [Google Scholar]
  128. Sim Y, Acree BD, Gross JH, Smith NA. 2013. Measuring ideological proportions in political speeches. See Cadilhac et al. 2013. 91–101
  129. Simmons JP, Nelson LD, Simonsohn U. 2011a. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22:1359–66 [Google Scholar]
  130. Simmons MP, Adamic LA, Adar E. 2011b. Memes online: extracted, subtracted, injected, and recollected. Proc. Fifth Int. AAAI Conf. Weblogs Soc. Media, Barcelona, July 17–21
  131. Smith A. 1759. The Theory of Moral Sentiments London: Millar
  132. Socher R, Perelygin A, Wu JY, Chuang J, Manning CD. et al. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. See Cadilhac et al. 2013. 1631–42
  133. Spangler S, Wilkins AD, Bachman BJ, Nagarajan M, Dayaram T. et al. 2014. Automated hypothesis generation based on mining scientific literature. See Mukherjee et al. 2014. 1877–86
  134. Sridhar D, Foulds J, Huang B, Getoor L, Walker M. 2015. Joint models of disagreement and stance in online debate. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Jt. Conf. Nat. Lang. Process. Int. Beijing, July 26–31 116–25 http://www.aclweb.org/anthology/P15-1012
  135. Srivastava AN, Sahami M. 2009. Text Mining: Classification, Clustering, and Applications Boca Raton, FL: Chapman & Hall/CRC Press
  136. Stivers T, Enfield NJ, Brown P, Englert C, Hayashi M. et al. 2009. Universals and cultural variation in turn-taking in conversation. PNAS 106:2610587–92 [Google Scholar]
  137. Stone PJ, Dunphy DC, Smith MS, Oglivie DM. 1966. The General Inquirer: A Computer Approach to Content Analysis Cambridge, MA: MIT Press
  138. Stymne S, Hardmeier C, Tiedemann J, Nivre J. 2013. Feature weight optimization for discourse-level SMT. Proc. Workshop Discourse Mach. Transl., Sofia, Bulg., Aug. 960–69 http://www.aclweb.org/anthology/W13-3308
  139. Sudhof M, Goméz Emilsson A, Maas AL, Potts C. 2014. Sentiment expression conditioned by affective transitions and social forces. See Mukherjee et al. 2014. 1136–45
  140. Taddy M. 2013. Multinomial inverse regression for text analysis. J. Am. Stat. Assoc. 108:503755–70 [Google Scholar]
  141. Taddy M. 2015. Document classification by inversion of distributed language representations. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Jt. Conf. Nat. Lang. Process. Int. Beijing, July 26–31 45–49
  142. Tan C, Lee L, Pang B. 2014. The effect of wording on message propagation: topic- and author-controlled natural experiments on Twitter. See Iyyer et al. 2014. 175–85
  143. Tangherlini TR, Leonard P. 2013. Trawling in the sea of the great unread: sub-corpus topic modeling and humanities research. Poetics 41:6725–49 [Google Scholar]
  144. Tausczik YR, Pennebaker JW. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29:124–54 [Google Scholar]
  145. Tavory I, Timmermans S. 2014. Abductive Analysis: Theorizing Qualitative Research Chicago: Univ. Chicago Press
  146. Tetlock PC. 2007. Giving content to investor sentiment: the role of media in the stock market. J. Finance 62:31139–68 [Google Scholar]
  147. Tibshirani R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58:267–88 [Google Scholar]
  148. Timmermans S, Tavory I. 2012. Theory construction in qualitative research from grounded theory to abductive analysis. Sociol. Theory 30:3167–186 [Google Scholar]
  149. Tsur O, Calacci D, Lazer D. 2015. A frame of mind: using statistical models for detection of framing and agenda setting campaigns. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Jt. Conf. Nat. Lang. Process. Int. Beijing, July 26–31 1629–38
  150. Tsytsarau M, Palpanas T, Castellanos M. 2014. Dynamics of news events and social media reaction. See Mukherjee et al. 2014. 901–10
  151. van Atteveldt WH. 2008. Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content Charleston, SC: BookSurge
  152. van Atteveldt WH, Kleinnijenhuis J, Ruigrok N. 2008. Parsing, semantic networks, and political authority using syntactic analysis to extract semantic relations from Dutch newspaper articles. Polit. Anal. 16:4428–46 [Google Scholar]
  153. Vilhena D, Foster J, Rosvall M, West J, Evans J, Bergstrom C. 2014. Finding cultural holes: how structure and culture diverge in networks of scholarly communication. Sociol. Sci. 1:221–38 [Google Scholar]
  154. von Neumann J, Morgenstern O. 1944. Theory of Games and Economic Behavior Princeton, NJ: Princeton Univ. Press
  155. Wallace BC, Trikalinos TA, Laws MB, Wilson IB, Charniak E. 2013. A generative joint, additive, sequential model of topics and speech acts in patient-doctor communication.. See Cadilhac et al. , pp1765–75 [Google Scholar]
  156. Wallach HM. 2006. Topic modeling: beyond bag-of-words. See Blei & Lafferty. 977–84
  157. Whissell CM. 1989. The dictionary of affect in language. Emotion: Theory, Research, and Experience R Plutchik, H Kellerman 113–31 New York: Academic [Google Scholar]
  158. Willey MM. 1926. The Country Newspaper: A Study of Socialization and Newspaper Content Chapel Hill, NC: Univ. N. C. Press
  159. Wittgenstein L. 2010. Philosophical Investigations Chichester, UK: Wiley
  160. Woodward JL. 1934. Quantitative newspaper analysis as a technique of opinion research. Soc. Forces 12:4526–37 [Google Scholar]
  161. Woolley AW, Chabris CF, Pentland A, Hashmi N, Malone TW. 2010. Evidence for a collective intelligence factor in the performance of human groups. Science 330:6004686–88 [Google Scholar]
  162. Yang D, Wen M, Rose C. 2015. Weakly supervised role identification in teamwork interactions. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process. Beijing, July 26–31 1671–80
  163. Yu B, Kaufmann S, Diermeier D. 2008. Classifying party affiliation from political speech. J. Inf. Technol. Polit. 5:133–48 [Google Scholar]
  164. Yu D, Tyshchuk Y, Ji H, Wallace W. 2015. Detecting deceptive groups using conversations and network analysis. Proc. 53rd Annu. Meet. Assoc. Comput. Linguist. 7th Int. Jt. Conf. Nat. Lang. Process. Beijing, July 26–31 857–66
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Machine Translation: Mining Text for Social Theory
Annual Review of Sociology 42, 21 (2016); https://doi.org/10.1146/annurev-soc-081715-074206
/content/journals/10.1146/annurev-soc-081715-074206
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