66. Leqi Liu et al., “Analyzing Personality Through Social Media Profile Picture Choice,” Association for the Advancement of Artificial Intelligence, 2016, https://sites.sas.upenn.edu/sites/default/files/danielpr/files/persimages16icwsm.pdf; Sharath Chandra Guntuku et al., “Do Others Perceive You as You Want Them To? Modeling Personality Based on Selfies,” in Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia, ASM ’15 (New York: ACM, 2015), 21–26, https://doi.org/10.1145/2813524.2813528; Bruce Ferwerda, Markus Schedl, and Marko Tkalcic, “Using Instagram Picture Features to Predict Users’ Personality,” in MultiMedia Modeling (Cham, Switzerland: Springer, 2016), 850–61, https://doi.org/10.1007/978-3-319-27671-7_71; Golbeck, Robles, and Turner, “Predicting Personality with Social Media”; Chen, Tsai, and Chen, “A User’s Personality Prediction Approach”; Schwartz et al., “Predicting Individual Well-Being.”
67. CaPPr, Interview with Michal Kosinski.
68. “IBM Cloud Makes Hybrid a Reality for the Enterprise,” IBM, February 23, 2015, https://www-03.ibm.com/press/us/en/pressrelease/46136.wss.
69. “IBM Watson Personality Insights,” IBM Watson Developer Cloud, October 14, 2017, https://personality-insights-livedemo.mybluemix.net; “IBM Personality Insights—Needs,” IBM Watson Developer Cloud, October 14, 2017, https://console.bluemix.net/docs/services/personality-insights/needs.html#needs; “IBM Personality Insights—Values,” IBM Watson Developer Cloud, October 14, 2017, https://console.bluemix.net/docs/services/personality-insights/values.html#values.
70. “IBM Personality Insights—Use Cases,” IBM Cloud Docs, November 8, 2017, https://console.bluemix.net/docs/services/personality-insights/usecases.html #usecases.
71. See Vibha Sinha, “Personality of Your Agent Matters—an Empirical Study on Twitter Conversations—Watson Dev,” Watson, November 3, 2016, https://developer.ibm.com/watson/blog/2016/11/03/personality-of-your-agent-matters-an-empirical-study-on-twitter-conversations. In a study undertaken with data broker Acxiom, the two giant corporations set out to determine whether IBM’s personality insights more accurately predict consumption preferences than the more standard demographic information amassed by the data brokers. The findings were affirmative. After examining 133 consumption preferences of about 785,000 US individuals, the addition of personality data improved prediction accuracy for 115 preferences (86.5 percent). Personality data alone provided better prediction accuracy than demographic data for 23 of those preferences. The researchers note with some enthusiasm that in 61 percent of the cases, Watson’s “personality insights” can accurately predict certain preference categories, such as “camping/hiking,” “without collecting any data from the user.” They concede that an individual’s income is also a powerful predictor of consumption, but they complain that income information is “very sensitive” and “hard to collect” compared to personality data, which “can be directly derived from the people’s social media profile.” See IBM-Acxiom, “Improving Consumer Consumption Preference Prediction Accuracy with Personality Insights,” March 2016, https://www.ibm.com/watson/developer cloud/doc/personality-insights/applied.shtml.
72. IBM-Acxiom, “Improving Consumer Consumption Preference Prediction Accuracy.”
73. “Social Media Analytics,” Xerox Research Center Europe, April 3, 2017, http://www.xrce.xerox.com/Our-Research/Natural-Language-Processing/Social-Media-Analytics; Amy Webb, “8 Tech Trends to Watch in 2016,” Harvard Business Review, December 8, 2015, https://hbr.org/2015/12/8-tech-trends-to-watch-in-2016; Christina Crowell, “Machines That Talk to Us May Soon Sense Our Feelings, Too,” Scientific American, June 24, 2016, https://www.scientificamerican.com/article/machines-that-talk-to-us-may-soon-sense-our-feelings-too; R. G. Conlee, “How Automation and Analytics Are Changing Customer Care,” Conduent Blog, July 18, 2016, https://www.blogs.conduent.com/2016/07/18/how-automation-and-analytics-are-changing-customer-care; Ryan Knutson, “Call Centers May Know a Surprising Amount About You,” Wall Street Journal, January 6, 2017, http://www.wsj.com/articles/that-anonymous-voice-at-the-call-center-they-may-know-a-lot-about-you-1483698608.
74. Nicholas Confessore and Danny Hakim, “Bold Promises Fade to Doubts for a Trump-Linked Data Firm,” New York Times, March 6, 2017, https://www.nytimes.com/2017/03/06/us/politics/cambridge-analytica.html; Mary-Ann Russon, “Political Revolution: How Big Data Won the US Presidency for Donald Trump,” International Business Times UK, January 20, 2017, http://www.ibtimes.co.uk/political-revolution-how-big-data-won-us-presidency-donald-trump-1602269; Grassegger and Krogerus, “The Data That Turned the World Upside Down”; Carole Cadwalladr, “Revealed: How US Billionaire Helped to Back Brexit,” Guardian, February 25, 2017, https://www.theguardian.com/politics/2017/feb/26/us-billionaire-mercer-helped-back-brexit; Paul-Olivier Dehaye, “The (Dis)Information Mercenaries Now Controlling Trump’s Databases,” Medium, January 3, 2017, https://medium.com/personaldata-io/the-dis-information-mercenaries-now-controlling-trumps-databases-4f6a20d4f3e7; Harry Davies, “Ted Cruz Using Firm That Harvested Data on Millions of Unwitting Facebook Users,” Guardian, December 11, 2015, https://www.theguardian.com/us-news/2015/dec/11/senator-ted-cruz-president-campaign-facebook-user-data.
75. Concordia, The Power of Big Data and Psychographics, 2016, https://www.youtube.com/watch?v=n8Dd5aVXLCc.
76. See “Speak the Customer’s Language with Behavioral Microtargeting,” Dealer Marketing, December 1, 2016, http://www.dealermarketing.com/speak-the-customers-language-with-behavioral-microtargeting.
77. Biddle, “Facebook Uses Artificial Intelligence to Predict Your Future Actions.”
78. “Introducing FBLearner Flow: Facebook’s AI Backbone,” Facebook Code, April 16, 2018, https://code.facebook.com/posts/1072626246134461/introducing-fblearner-flow-facebook-s-ai-backbone.
79. Andy Kroll, “Cloak and Data: The Real Story Behind Cambridge Analytica’s Rise and Fall,” Mother Jones, March 24, 2018, https://www.motherjones.com/politics/2018/03/cloak-and-data-cambridge-analytica-robert-mercer.
80. Carole Cadwalladr, “‘I Made Steve Bannon’s Psychological Warfare Tool’: Meet the Data War Whistleblower,” Guardian, March 18, 2018, http://www.the guardian.com/news/2018/mar/17/data-war-whistleblower-christopher-wylie-faceook-nix-bannon-trump; Kroll, “Cloak and Data.”
81. Matthew Rosenberg, Nicholas Confessore, and Carole Cadwalladr, “How Trump Consultants Exploited the Facebook Data of Millions,” New York Times, March 17, 2018, https://www.nytimes.com/2018/03/17/us/politics/cambridge-analytica-trump-campaign.html; Emma Graham-Harrison and Carole Cadwalladr, “Revealed: 50 Million Facebook Profiles Harvested for Cambridge Analytica in Major Data Breach,” Guardian, March 17, 2018, http://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-election; Julia Carrie Wong and Paul Lewis, “Facebook Gave Data About 57bn Friendships to Academic,” Guardian, March 22, 2018, http://www.theguardian.com/news/2018/mar/22/facebook-gave-data-about-57bn-friendships-to-academic-aleksandr-kogan; Olivia Solon, “Facebook Says Cambridge Analytica May Have Gained 37m More Users’ Data,” Guardian, April 4, 2018, http://www.theguardian.com/technology/2018/apr/04/facebook-cambridge-analytica-user-data-latest-more-than-thought.
82. Paul Lewis and Julia Carrie Wong, “Facebook Employs Psychologist Whose Firm Sold Data to Cambridge Analytica,” Guardian, March 18, 2018, http://www.theguardian.com/news/2018/mar/18/facebook-cambridge-analytica-joseph-chancellor-gsr.
83. Kroll, “Cloak and Data.”
84. Frederik Zuiderveen Borgesius et al., “Online Political Microtargeting: Promises and Threats for Democracy” (SSRN Scholarly Paper, Rochester, NY: Social Science Research Network, February 9, 2018), https://papers.ssrn.com/abstract=3128787.
85. See Cadwalladr, “‘I Made Steve Bannon’s Psychological Warfare Tool.’”
86. Charlotte McEleny, “European Commission Issues €3.6m Grant for Tech That Measures Content ‘Likeability,’” CampaignLive.co.uk, April 20, 2015, http://www.campaignlive.co.uk/article/european-commission-issues-€36m-grant-tech
-measures-content-likeability/1343366.
87. “2016 Innovation Radar Prize Winners,” Digital Single Market, September 26, 2016, https://ec.europa.eu/digital-single-market/en/news/2016-innovation-radar-prize-winners.
88. “Affective Computing Market—Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015–2023,” Transparency Market Research, 2017, http://www.transparencymarketresearch.com/affective-computing-market.html.
89. Patrick Mannion, “Facial-Recognition Sensors Adapt to Track Emotions, Mood, and Stress,” EDN, March 3, 2016, http://www.edn.com/electronics-blogs/sensor-ee-perception/4441565/Facial-recognition-sensors-adapt-to-track-emotions—mood—and-stress; “Marketers, Welcome to the World of Emotional Analytics,” MarTech Today, January 12, 2016, https://martechtoday.com/marketers-welcome-to-the-world-of-emotional-analytics-159152; Ben Virdee-Chapman, “5 Companies Using Facial Recognition to Change the World,” Kairos, May 26, 2016, https://www.kairos.com/blog/5-companies-using-facial-recognition-to-change-the-world; “Affectiva Announces New Facial Coding Solution for Qualitative Research,” Affectiva, May 7, 2014, https://web-beta.archive.org/web/20160625173829/http://www.affectiva.com/news/affectiva-announces-new-facial-coding-solution-for-qualitative-research; Ahmad Jalal, Shaharyar Kamal, and Daijin Kim, “Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments,” Journal of Computer Networks and Communications (2016), https://doi.org/10.1155/2016/8087545; M. Kakarla and G. R. M. Reddy, “A Real Time Facial Emotion Recognition Using Depth Sensor and Interfacing with Second Life Based Virtual 3D Avatar,” in International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), 2014, 1–7, https://doi.org/10.1109/ICRAIE.2014.6909153.
90. “Sewa Project: Automatic Sentiment Analysis in the Wild,” SEWA, April 25, 2017, https://sewaproject.eu/description.
91. Mihkel Jäätma, “Realeyes—Emotion Measurement,” Realeyes Data Services, 2016, https://www.realeyesit.com/Media/Default/Whitepaper/Realeyes_White paper.pdf.
92. Mihkel Jäätma, “Realeyes—Emotion Measurement.”
93. Alex Browne, “Realeyes—Play Your Audience Emotions to Stay on Top of the Game,” Realeyes, February 21, 2017, https://www.realeyesit.com/blog/play-your-audience-emotions.
94. “Realeyes—Emotions,” Realeyes, April 2, 2017, https://www.realeyesit.com/emotions.
95. “See What Industrial Advisors Think About SEWA,” SEWA, April 24, 2017, https://sewaproject.eu/qa#ElissaMoses.
96. Roland Marchand, Advertising the American Dream: Making Way for Modernity, 1920–1940 (Berkeley: University of California Press, 1985).
97. Some key early papers include Paul Ekman and Wallace V. Friesen, “The Repertoire of Nonverbal Behavior: Categories, Origins, Usage and Coding,” Semiotica 1, no. 1 (1969): 49–98; Paul Ekman and Wallace V. Friesen, “Constants Across Cultures in the Face and Emotion,” Journal of Personality and Social Psychology 17, no. 2 (1971): 124–29; P. Ekman and W. V. Friesen, “Nonverbal Leakage and Clues to Deception,” Psychiatry 32, no. 1 (1969): 88–106; Paul Ekman, E. Richard Sorenson, and Wallace V. Friesen, “Pan-Cultural Elements in Facial Displays of Emotion,” Science 164, no. 3875 (1969): 86–88, https://doi.org/10.1126/science.164.3875.86; Paul Ekman, Wallace V. Friesen, and Silvan S. Tomkins, “Facial Affect Scoring Technique: A First Validity Study,” Semiotica 3, no. 1 (1971), https://doi.org/10.1515/semi.1971.3.1.37.
98. Ekman and Friesen, “Nonverbal Leakage.”
99. Ekman and Friesen, “The Repertoire of Nonverbal Behavior.”
100. Paul Ekman, “An Argument for Basic Emotions,” Cognition and Emotion 6, nos. 3–4 (1992): 169–200, https://doi.org/10.1080/02699939208411068.
101. Ekman and colleagues published an article describing their own approach to “automatic facial expression measurement” in 1997, the same year as Rosalind W. Picard’s book Affective Computing (Cambridge, MA: MIT Press, 2000).
102. Rosalind W. Picard, Affective Computing, Chapter 3.
103. Picard, Affective Computing, 244.
104. Picard, Chapter 4, especially 123–24, 136–37.
105. Barak Reuven Naveh, Techniques for emotion detection and content delivery, US20150242679 A1, filed February 25, 2014, and issued August 27, 2015, http://www.google.com/patents/US20150242679.
106. Naveh, Techniques for emotion detection and content delivery, paragraph 32.
107. “Affective Computing Market by Technology (Touch-Based and Touchless), Software (Speech Recognition, Gesture Recognition, Facial Feature Extraction, Analytics Software, & Enterprise Software), Hardware, Vertical, and Region—Forecast to 2021,” MarketsandMarkets, March 2017, http://www.marketsandmarkets.com/Market-Reports/affective-computing-market-130730395.html.
108. Raffi Khatchadourian, “We Know How You Feel,” New Yorker, January 19, 2015, http://www.newyorker.com/magazine/2015/01/19/know-feel.
109. Khatchadourian, “We Know How You Feel.”
110. Khatchadourian.
111. “Affectiva,” Crunchbase, October 22, 2017, https://www.crunchbase.com/organization/affectiva.
112. Lora Kolodny, “Affectiva Raises $14 Million to Bring Apps, Robots Emotional Intelligence,” TechCrunch, May 25, 2016, http://social.techcrunch.com/2016/05/25/affectiva-raises-14-million-to-bring-apps-robots-emotional-intelli gence; Rana el Kaliouby, “Emotion Technology Year in Review: Affectiva in 2016,” Affectiva, December 29, 2016, http://blog.affectiva.com/emotion-technology-year-in-review-affectiva-in-2016.
113. Matthew Hutson, “Our Bots, Ourselves,” Atlantic, March 2017, https://www.theatlantic.com/magazine/archive/2017/03/our-bots-ourselves/513839.
114. Patrick Levy-Rosenthal, “Emoshape Announces Production of the Emotions Processing Unit II,” Emoshape | Emotions Synthesis, January 18, 2016, http://emoshape.com/emoshape-announces-production-of-the-emotions-process ing-unit-ii.
115. Tom Foster, “Ready or Not, Companies Will Soon Be Tracking Your Emotions,” Inc.com, June 21, 2016, https://www.inc.com/magazine/201607/tom-foster/lightwave-monitor-customer-emotions.html; “Emotion as a Service,” Affectiva, March 30, 2017, http://www.affectiva.com/product/emotion-as-a-service; “Affectiva Announces Availability of Emotion as a Service, a New Data Solution, and Version 2.0 of Its Emotion-Sensing SDK,” PR Newswire, September 8, 2015, http://www.prnewswire.com/news-releases/affectiva-announces-availability-of-emotion-as-a-service-a-new-data-solution-and-version-20-of-its-emotion-sensing-sdk-300139001.html.
116. See Khatchadourian, “We Know How You Feel.”
117. Jean-Paul Sartre, Being and Nothingness, trans. Hazel E. Barnes (New York: Washington Square, 1993), 573.
118. Jean-Paul Sartre, Situations (New York: George Braziller, 1965), 333.
119. “Kairos for Market Researchers,” Kairos, March 9, 2017, https://www.kairos.com/human-analytics/market-researchers.
120. Picard, Affective Computing, 119, 123, 244, 123–24, 136–37. See also Chapter 4.
121. Rosalind Picard, “Towards Machines That Deny Their Maker—Lecture with Rosalind Picard,” VBG, April 22, 2016, http://www.vbg.net/ueber-uns/agenda/termin/3075.html.
122. Joseph Weizenbaum, “Not Without Us,” SIGCAS Computers and Society 16, nos. 2–3 (1986): 2–7, https://doi.org/10.1145/15483.15484.
CHAPTER TEN
1. Richard H. Thaler and Cass R. Sunstein, Nudge: Improving Decisions About Health, Wealth, and Happiness, rev. ed. (New York: Penguin, 2009).
2. Elizabeth J. Lyons et al., “Behavior Change Techniques Implemented in Electronic Lifestyle Activity Monitors: A Systematic Content Analysis,” Journal of Medical Internet Research 16, no. 8 (2014), e192, https://doi.org/10.2196/jmir.3469.
The commercial theory and practice of behavior modification assume an inescapable networked presence and its cornucopia of digital tools. A team of British researchers surveyed fifty-five behavioral experts in order to compile “a consensually agreed hierarchically structured taxonomy of techniques used in behavior change interventions.” This exerc
ise identified ninety-three distinct behavior-change techniques, which were grouped into sixteen methodological clusters: “scheduled consequences,” “reward and threat,” “repetition and substitution,” “antecedents,” “associations,” “feedback and monitoring,” “goals and planning,” “social support,” “comparison of behavior,” “communication of natural consequences,” “self-belief,” “comparison of outcomes,” “shaping knowledge,” “regulation,” “identity,” and “covert learning.”
The researchers warn that behavior modification is a “fast-moving field.” As illustration, they note that the first such taxonomy, published only four years earlier, had identified just twenty-two behavior-change techniques, many of which were individually oriented and required face-to-face interaction and relationship building. In contrast, the newer techniques are aimed at “community and population-level interventions,” a fact that speaks to the migration of behavior-change operations to the novel capabilities (contextual control, automated digital nudges, operant conditioning at scale) of the internet-enabled tools upon which economies of action depend. See Susan Michie et al., “The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions,” Annals of Behavioral Medicine 46, no. 1 (2013): 81–95, https://doi.org/10.1007/s12160-013-9486-6.
3. Hal R. Varian, “Beyond Big Data,” Business Economics 49, no. 1 (2014): 6.
4. Varian, “Beyond Big Data,” 7.
5. Robert M. Bond et al., “A 61-Million-Person Experiment in Social Influence and Political Mobilization,” Nature 489, no. 7415 (2012): 295–98, https://doi.org/10.1038/nature11421.
6. Bond et al., “A 61-Million-Person Experiment.”
7. Andrew Ledvina, “10 Ways Facebook Is Actually the Devil,” AndrewLedvina.com, July 4, 2017, http://andrewledvina.com/code/2014/07/04/10-ways-facebook-is-the-devil.html.
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