The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think

By Eli Pariser. 2012

1,289 citations recorded by the source corpus.

Subfields: Sociology and Political Science

How researchers see this paper

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

In Sociology and Political Science

Citations in the corpus

Birth of Industry 5.0: Making Sense of Big Data with Artificial Intelligence, “The Internet of Things” and Next-Generation Technology Policy (2018, neutral): “…Facebook based on where we login, what we have searched for in the past, and other user attributes are creating ﬁlter bubbles that increasingly deﬁne (and narrow) the range of our online experiences, exposure to alternative epistemologies, and how we consume data and information (Pariser, 2011).”
The Personalization of Conversational Agents in Health Care: Systematic Review (2019, background): “For example, a recent research paper drew attention to the limitations of implicit and explicit personalization [61]: while implicit personalization with its often-imperceptible user models and hidden assumptions can result in biased decision-making [62], over-reliance on system suggestions [63], an”
“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content (2019, background): “This can narrow the universe of stories read by any one individual and lead to the creation of “echo chambers” (Sunstein, 2002) and “filter bubbles” (Pariser, 2011).”
Does Too Much News on Social Media Discourage News Seeking? Mediating Role of News Efficacy Between Perceived News Overload and News Avoidance on Social Media (2019, background): “News content offered by algorithmic filtering tends to match users’ preferences and political orientations (Pariser, 2011), thereby reinforcing confirmation biases (Y. Kim, Chen, & Gil de Zúñiga, 2013).”
Can Students Evaluate Online Sources? Learning From Assessments of Civic Online Reasoning (2018, background): “However, it is imperative for students to understand how the Internet shapes the information they receive (e.g., Lynch, 2016; Mason & Metzger, 2012; Pariser, 2011) and to know how to find reliable information (Kahne, Hodgin, & Eidman-Aadahl, 2016; Metzger, 2007; Metzger, Flanagin, & Medders, 2010).”
AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2022, neutral): “However, this level of per-sonalisation can create filter bubbles (Pariser, 2011) against the ethos of public service delivery in providing consistent services and messages to all citizens alike.”
In Related News, That Was Wrong: The Correction of Misinformation Through Related Stories Functionality in Social Media (2015, background): “These algorithms are often opaque in how they select information, encompassing predictions of relevance based on past behaviors, content type, interactions, and, of course, commercial goals (Gillespie, 2014; Messing & Westwood, 2014; Pariser, 2011).”
Filter Bubbles, Echo Chambers, and Fake News: How Social Media Conditions Individuals to Be Less Critical of Political Misinformation (2021, background): “users’ preferences and display agreeable content in an effort to increase the time spent on their respective platforms (Mims, 2017; Pariser, 2011).”
The spread of low-credibility content by social bots (2017, neutral): “These include information overload and finite attention [41], novelty of false news [57], the selective exposure [52, 40, 39] caused by polarized and segregated online social networks [9, 8], algorithmic popularity bias [45, 20, 37], and other cognitive vulnerabilities such as confirmation bias and ”
The age of digital entrepreneurship (2019, background): “…motivations in a world in which consumption is increasingly driven by self-expression and not only to collect demographic data and purchase histories (Pariser 2011); and (ii) the second dimension implies moving from a controlled value chain orientation to a network orientation, based on a web of…”
What is AI Literacy? Competencies and Design Considerations (2020, background): “Misinformation: The spread of misinformation and “fake news” has been exacerbated by AI algorithms on social media and search engines that promote “clickbait” articles and create “filter bubbles” [104].”
Measuring Online Social Bubbles (2015, background): “In one line of reasoning, Sunstein (2002), Sunstein (2009) and Pariser (2011) have argued that the reliance on personalization and social media can lead people to being exposed to a narrow set of viewpoints.”