How Companies Learn Your Secrets

By Charles Duhigg, A. Pole. 2012

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

"Wrong side of the tracks": Big Data and Protected Categories (2014, background): “As discussed elsewhere [21], machine learni ng also provides new challenges to individual privacy: in a famous example, statisticians at W alMart were able to infer, from her shopping habits, a teenager’s pregnancy before it became ph ysically evident to her father [22].”
Satisfiability modulo counting: a new approach for analyzing privacy properties (2014, background): “Recent reports suggest that in many cases the risks surpass consumers’ initial expectations [1, 19, 43], but this has not slowed the acceptance and widespread use of these applications, as they provide utility that cannot be readily weighed against such risks.”
Big Data consumer analytics and the transformation of marketing (2016, unknown): “Target is able to estimate whether a female shopper is pregnant and her due date weeks before competitors or even the woman's family knows of her pregnancy (Duhigg, 2012; Hill, 2012).”
Exploring Time-Dependent Concerns about Pregnancy and Childbirth from Search Logs (2015, background): “These life events often precipitate changes in our interests, preferences, and priorities [12], and the changes may evolve over the course of weeks or months (e.”
Tutorial: Big Data Analytics: Concepts, Technologies, and Applications (2014, background): “Target received considerable negative attention in publications such as the New York Times [Duhigg, 2012] and Forbes [Hill, 2012] for mining data to identify women who are pregnant.”
Recommended for you: The Netflix Prize and the production of algorithmic culture (2016, background): “…wisdom of marketing, where categories such as age, race, ethnicity, class, gender, and sexuality have long guided “captains of consciousness” in their efforts to pitch products to particular populations (Ewen, 2001; see also Cohen, 2003; Cross, 2000; Curtis, 2002; Zukin, 2004; cf. Duhigg, 2012).”
Free for All! Assessing User Data Exposure to Advertising Libraries on Android (2016, background): “This is well illustrated by events surrounding Target’s successful program to use the habits of registered expecting shoppers to derive clues about unregistered ones in order to target them with advertising about baby care products [13].”
Applied Predictive Modeling (2013, background): “Even a trip to the grocery store or gas station [everyday places where our purchase information is collected and analyzed in an attempt to understand who we are and what we want (Duhigg 2012)] brings us into the predictive modeling world, and we’re often not even aware that we’ve entered it.”
Measuring Price Discrimination and Steering on E-commerce Web Sites (2014, background): “In some cases, companies go to great lengths to obfuscate the fact that recommendations are personalized, because users sometimes find these practices to be creepy [10].”