Author(s): Anchal Bhardwaj
- Notre Dame High School Class of 2024
When you check social media for new updates/posts, you may not be aware that you are being followed: anonymous cookies sponsored by big tech companies collectdata based on your activity and search history. These anonymous cookies and detectors are part of an algorithmic process that identifies a user’s preferences and provides a “personal ecosystem of information,” according to Eli Pariser, an internet activist (Pariser E. The Filter Bubble). Pariser first introduced the concept of Filter Media Bubbles through an experiment conducted with two friends with different ideologies. He related an example in which one user Google searched for “BP” and got investment news about British Petroleum, while another user received information about the Deepwater Horizon oil spill. The two different search results are an effect of filter bubbles that filter out news, videos, and other media that the algorithm does not think will align with the user’s perspectives. Therefore, filter bubbles slowly narrow our thinking and understanding of the world by providing us with results that are biased towards our own opinion.
As stated by HowStuffWorks, the cookies that are implemented to create personal ads and filter bubbles are initially random pieces of data passed through the web browser and given to the user’s device. The cookie is then returned to the server with every action executed by the user as well as past activity. Cookies are not programs that can execute on your hard drive. They are empty pieces of text that a web server can store in the user’s device that can collect user information in name-value pairs. A site can store as many cookies as it wants as regulated, and the most occurring is UserID which is a random ID given by a website once a user visits it. These websites and past activities help ad algorithms find ads based on user events.
Once we can identify the function of a cookie, we can begin to recognize the implementation within users’ devices. Filter bubbles are automatic for all users and each usually contain about 64 bits of cookies and hidden detection beacons. An example of the effects of filter bubbles are the personalized ads on certain websites that are based on websites you have previously visited. The cookies stored on the user’s server are used to personalize ads. It is explained as internet isolation when referred to the biased news and web outputs.
According to a Wall Street Journal study, top websites, such as CNN or Yahoo, install 64 data-laden cookies and after search for certain key phrases, sites can install up to 223 tracking cookies and beacons on your computer (Spread Privacy DDG). A web beacon is often referred to as a tag and is smaller than 1 by 1 pixel. The tags are hidden, transparent , graphic images that are on your computer and activated when the user visits a certain website. The Web Browser doesn’t recognize these tags but doesn’t raise alarms either. As explained in an article by What’s my IP Address, beacons are usually used in spam email and when a user opens spam, the tracking beacon will send the original sender a confirmation that the email was open and the email address was valid. Other than malicious activity, these beacons can detect the IP address of the user’s computer, url visited, and cookies implemented.
Though these filter bubbles can go by many different aliases such as ideological frames or echo chambers, they are effective on many user’s devices and can cause the user to have “tunnel vision” on many topics, as stated by GroupLens Research, “recommender systems expose users to a slightly narrowing set of items over time.” A recent study by Google shows that the filter bubbles are even in place when the user is logged out or in private mode. The search results were slightly unique to each person and had a fixed view. As shown in recent data collected by user search results, there was, on average, 3 domain changes within private browsing users as well as, on certain topics, there was up to 90% variation in terms of news results. (Privacy Research on DDG).
“Machine learning algorithms are penetrated into almost every different social media platforms such as Twitter and Facebook. They help to predict human intention based on their historical and behavioral data like users’ online connections and the news they follow, with the primary purpose of keeping users more engaged with the content, and increasing both the number of views and time spent on the platforms.” The Boston University Center for Mobile Communication Studies explains how internet users have shifted their focus to social media and how filter bubbles are prominent in those areas with help from machine learning algorithms, cookies, and tracking beacons.
The impacts of filter bubbles are arising and harmful. Vulnerability to manipulation and propaganda are some of the rising issues and fears that will soon challenge the pretense of anonymity. People will continue to browse the web and filter bubbles will continue to slowly form the perfect “tunnel vision” for you.
Bibliography
1. Boston University. The isolating web: Do filter bubbles narrow down our mind? | Center for Mobile Communication Studies. Bu.edu. https://sites.bu.edu/cmcs/2018/12/06/the-isolating-web-do-filter-bubbles-narrow-down-our-mind/. Published 2017.
2. Measuring the Filter Bubble: How Google is influencing what you click. DuckDuckGo Blog. https://spreadprivacy.com/google-filter-bubble-study/. Published December 4, 2018.
3. Filter bubble. Wikipedia. https://en.wikipedia.org/wiki/Filter_bubble#:~:text=A%20filter%20bubble%20%E2%80%93%20a%20term. Published June 20, 2020. Accessed July 2, 2020.
4. Algorithms, filter bubbles, and how personalization can change your perception. Algorithms, filter bubbles, and how personalization can change your perception. epoint. https://www.e-point.com/blog/algorithms-filter-bubbles-and-how-personalization-can-change-your-perception. Published 2020.
5. https://www.facebook.com/FarnamStreet. How Filter Bubbles Distort Reality: Everything You Need to Know. Farnam Street. https://fs.blog/2017/07/filter-bubbles/. Published July 31, 2017.
6. How Stuff Works. How Internet Cookies Work. HowStuffWorks. https://computer.howstuffworks.com/cookie1.htm. Published April 26, 2000.
7.Pariser E. The Filter Bubble : What the Internet Is Hiding from You. New York: Penguin Press; 2011.
8. Green H. Breaking Out of Your Internet Filter Bubble. Forbes. https://www.forbes.com/sites/work-in-progress/2011/08/29/breaking-out-of-your-internet-filter-bubble/#3f7a9a1043ac. Accessed July 2, 2020.
9. What is a Web Bug/Beacon? WhatIsMyIPAddress.com. https://whatismyipaddress.com/web-beacon. Published 2020.
10. Nguyen TT, Hui P-M, Harper FM, Terveen L, Konstan JA. Exploring the filter bubble. Proceedings of the 23rd international conference on World wide web – WWW ’14. 2014. doi:10.1145/2566486.2568012
International Youth Scientists 