Abstract
As we have shifted from an information economy to an attention economy in the past two decades, we have almost simultaneously shifted from mass media to social media. The drastic increase in available media necessitates a way for individuals to sift through the media that is literally at their fingertips. Content recommendation systems have emerged as the technological solution to this social/informational problem. Understanding how recommendation system algorithms work, and how they reinforce (and even exaggerate) unconscious human bias, is essential to understanding the way data influences opinion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Charles Duhigg, “How Companies Learn Your Secrets,” The New York Times Magazine, published February 16, 2012, www.nytimes.com/2012/02/19/magazine/shopping-habits.html .
- 2.
Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (New York University Press, 2018), p. 18.
- 3.
“Google and the Miseducation of Dylann Roof,” Southern Poverty Law Center, published January 18, 2017, www.splcenter.org/20170118/google-and-miseducation-dylann-roof .
- 4.
Carole Cadwalladr, “How to bump Holocaust deniers off Google’s top spot? Pay Google,” The Observer, published December 17, 2016, www.theguardian.com/technology/2016/dec/17/holocaust-deniers-google-search-top-spot .
- 5.
Jeff John Roberts, “Google Demotes Holocaust Denial and Hate Sites in Update to Algorithm,” Fortune, published December 20, 2016, http://fortune.com/2016/12/20/google-algorithm-update/ .
- 6.
“How search algorithms work,” Google, www.google.com/search/howsearchworks/algorithms/ .
- 7.
You can find some of the information Google has inferred about you from your online activity at https://adssettings.google.com/ .
- 8.
I don’t want to call out any single developer or company here, but a web search for “track user scrolls on a page” returns a number of solutions for web developers who want to track what web site visitors scroll by and what they don’t.
- 9.
This is even true for “personalized” education apps. See “Knewton Adaptive Learning: Building the world’s most powerful education recommendation engine,” Knewton, accessed July 24, 2017, https://cdn.tc-library.org/Edlab/Knewton-adaptive-learning-white-paper-1.pdf .
- 10.
This type of model is true for survey-based dating apps like eHarmony and OkCupid, not for behavior-based apps like Tinder.
- 11.
“How the Matching Algorithm Works,” The National Resident Match Program, www.nrmp.org/matching-algorithm/ .
- 12.
Albert Au Yueng, “Matrix Factorization: A Simple Tutorial and Implementation in Python,” quuxlabs, published September 16, 2010, www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/ .
- 13.
Note that I didn’t say “most likely to be satisfied with.” Attention is the commodity, and engagement the currency, in this new economy. Taste is much harder to quantify, and thus to charge advertisers for.
- 14.
Kevin Curry, “More and more people get their news via social media. Is that good or bad?,” Monkey Cage, The Washington Post, published September 30, 2016, www.washingtonpost.com/news/monkey-cage/wp/2016/09/30/more-and-more-people-get-their-news-via-social-media-is-that-good-or-bad/ .
- 15.
Craig Silverman, “This Analysis Shows How Viral Fake Election News Stories Outperformed Real News On Facebook,” BuzzFeed News, published November 16, 2016, www.buzzfeednews.com/article/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook .
- 16.
“Political Polarization in the American Public,” Pew Research Center, published June 12, 2014, www.people-press.org/2014/06/12/political-polarization-in-the-american-public/ .
- 17.
Renee DiResta, “Free Speech in the Age of Algorithmic Microphones,” WIRED, published October 12, 2018, www.wired.com/story/facebook-domestic-disinformation-algorithmic-megaphones/ .
- 18.
Drew Olanoff, “Twitter Sees 6% Increase In ‘Like’ Activity After First Week Of Hearts,” TechCrunch, published November 10, 2015, https://techcrunch.com/2015/11/10/twitter-sees-6-increase-in-like-activity-after-first-week-of-hearts/ .
- 19.
Bianca Bosker, “The Binge Breaker,” The Atlantic, published November, 2016, www.theatlantic.com/magazine/archive/2016/11/the-binge-breaker/501122/ .
- 20.
Nellie Bowles, “Silicon Valley Nannies Are Phone Police for Kids,” The New York Times, published October 26, 2018, www.nytimes.com/2018/10/26/style/silicon-valley-nannies.html .
- 21.
Tom Rosenstiel, Jeff Sonderman, Kevin Loker, Jennifer Benz, David Sterrett, Dan Malato, Trevor Tompson, Liz Kantor, and Emily Swanson, “‘Who shared it?’: How Americans decide what news to trust on social media,” American Press Institute, published March 20, 2017, www.americanpressinstitute.org/publications/reports/survey-research/trust-social-media/ .
- 22.
Mike Caulfield, “Facebook Broke Democracy, but the Fix Is Harder Than People Realize,” Hapgood (blog), published November 10, 2016, https://hapgood.us/2016/11/10/facebook-broke-democracy-but-the-fix-is-harder-than-people-realize/ .
- 23.
Bordia, Prashant and Nicholas DiFonzo. 2017. Rumor Psychology: Social and Organizational Approaches. Washington, D.C.: American Psychological Association.
- 24.
Alexis Sobel Fitts, “We still don’t know how to stop misinformation online,” Colombia Journalism Review, published October 9, 2014, https://archives.cjr.org/behind_the_news/corrections_dont_go_viral.php .
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Kris Shaffer
About this chapter
Cite this chapter
Shaffer, K. (2019). Swimming Upstream. In: Data versus Democracy. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4540-8_3
Download citation
DOI: https://doi.org/10.1007/978-1-4842-4540-8_3
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4539-2
Online ISBN: 978-1-4842-4540-8
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)