Talk:Filter bubble

2001 prediction by a video game

Isn’t worth note that a video game predicted the whole thing back in 2001?

Wiki Education assignment: Advanced Topics in Digital Culture

This article was the subject of a Wiki Education Foundation-supported course assignment, between 5 September 2025 and 11 December 2025. Further details are available on the course page. Student editor(s): Ebuhe, Username2112, User193614, Anonymouszy08 (article contribs).

— Assignment last updated by StarryGlow (talk) 19:50, 15 October 2025 (UTC)

A myth.

There is more than a minuscule amount of evidence suggesting filter bubbles are overblown. https://reutersinstitute.politics.ox.ac.uk/news/truth-behind-filter-bubbles-bursting-some-myths 66.96.251.202 (talk) 00:45, 16 October 2025 (UTC)

Assessment of contributions

Hello, our group for a university assignment added several improvements and additions to this article including but not limited to:

• Further clarification of filter bubble's and aligning the definition more closely with Eli Pariser's original concept.

• Expanded the explanation of filter bubbles' effect on users

• Improved the description by using more precise and plain language to convey how personalization shapes a narrow worldview

• Added "Mechanisms" section

• Added paragraphs to Platform Studies section, Countermeasures section, and Academia Studies and Reactions section informed by recent academic literature

We would love to hear feedback and an assessment of our contributions to see if they are substantial enough to warrant an improved grade on Wikipedia's content assessment scale. Ebuhe (talk) 21:32, 24 November 2025 (UTC)

Wiki Education assignment: Digital and Mass Communication Media Literacy

This article was the subject of a Wiki Education Foundation-supported course assignment, between 13 January 2026 and 7 May 2026. Further details are available on the course page. Student editor(s): Gmweenpeefus (article contribs).

— Assignment last updated by Pinkwildcat (talk) 01:58, 29 April 2026 (UTC)

COI edit request: the paragraph stating the personalisation mechanism is unreferenced

I have a conflict of interest here and am requesting rather than making this edit.

The issue: The paragraph reading "The filter bubble also uses an algorithm to analyze user data, ie. likes, shares, time spent, to prioritize content that best aligns with a user's preferences. This personalization can allow users to be primarily exposed to information that follows their beliefs and interests." carries no reference at all. The paragraphs on either side of it are cited (Berman and Katona before it, Napoli after it), so this one is the unsourced statement of the mechanism the whole article is about. There is no maintenance template on it. A published audit measures the second of the two sentences directly, on live platform recommendations rather than by modeling.

Proposed change: Attach a reference to the second sentence of that paragraph and add one sentence reporting what the audit found:

This personalization can allow users to be primarily exposed to information that follows their beliefs and interests.<ref name="Ye2025">{{cite journal |last1=Ye |first1=Jinyi |last2=Luceri |first2=Luca |last3=Ferrara |first3=Emilio |title=Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election |journal=Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency |year=2025 |pages=2349–2362 |doi=10.1145/3715275.3732159}}</ref> A 2025 audit that operated 120 automated accounts on Twitter/X and collected 9.79 million recommended posts around the 2024 United States presidential election found that, for both left-leaning and right-leaning accounts, the most-recommended voices sharing that account's own political orientation were amplified more than 50% above a politically balanced baseline, while exposure to opposing viewpoints was reduced.<ref name="Ye2025" />

Source: Ye, Jinyi; Luceri, Luca; Ferrara, Emilio (2025). "Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election". Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency: 2349–2362. doi:10.1145/3715275.3732159. Narrowing this honestly: the audit measures exposure outcomes on one platform, so it supports the second sentence of the paragraph. It does not describe which signals a ranking system uses, so the first sentence, about likes, shares and time spent, would remain unsourced and needs a different reference.

Disclosure: This citation is to work I co-authored (Jinyi Ye, Luca Luceri, Emilio Ferrara). See User:Emilio Ferrara.

Happy for this to be declined or reworded; I will not make the edit myself. Emilio Ferrara (talk) 04:36, 27 July 2026 (UTC)