Upward Pull was a term used in marketing to describe the effects of people seeing advertisements that made them aspire. Examples including seeing a nice watch or a luxury vehicle in a magazine. Someone who may have to work for years for those items may see them and aspire to obtain them, despite them normally appearing outside their socioeconomic demographic. With the internet and advertisers, upward pull has all but vanished. When one views websites they see almost nothing aspirational. They instead see what is immediately obtainable.

Parmy Olsen describes the filter bubble that exists on the internet.

“…as algorithms make predictions about people based on their web behavior, they can inadvertently deepen existing disparities on aspects like culture, race or gender. In a few years you could, for instance, be looking at a richer or poorer version of the Internet depending on how things work out with your credit score or where you live, and not even know it.” (1)

She further says “The Princeton researchers will compare search results, prices, ads, offers and emails that their fake profiles receive over the coming months, and look for patterns to measure what kind of discrimination is happening across different sites.” (1)

1. Olson, Parmy. “This Landmark Study Could Reveal How The Web Discriminates Against You – Forbes.” News. This Landmark Study Could Reveal How The Web Discriminates Against You, 2 Dec. 2013, http://www.forbes.com/sites/parmyolson/2013/12/02/this-landmark-study-could-reveal-how-the-web-discriminates-against-you/.

2. Englehardt, Steven, et al. Web Privacy Measurement: Scientific Principles, Engineering Platform, and New Results., Draft, June 1, 2014 https://www.cs.princeton.edu/~arvindn/publications/WebPrivacyMeasurement.pdf

3. Englehardt, Steven, et al. OpenWPM: An Automated Platform for Web Privacy Measurement. Zotero, https://senglehardt.com/papers/openwpm_03-2015.pdf.

Addendum: The study was published. They focused on news site personalization at the time, which was before Google entered the foray as a primary driver of news content via Google News. Steven Englehardt maintains a webpage at https://senglehardt.com/pages/publications.html. The published material is very interesting and reveals how tracking mechanisms work over time. I have been unable to find detailed analysis of the different filter bubbles encountered by their bots. They published a second paper on their privacy analysis tool, but it does not delve into the different potential experiences of the web user. The material relates to the technical aspects of what occurs. Those studies have been added with notes 2 and 3.

Last modified on March 7th, 2026 at 10:44 pm