Glossary

Successful collaboration begins with a shared language, hence the need for a glossary. This joint effort of contributors from several teams ensures, on the one hand, terminological and conceptual coherence across not only our theoretical approaches, but also the qualitative case studies and quantitative research conducted in OPPORTUNITIES. On the other hand, our glossary facilitates communication between the academic side of the project and the fieldwork conducted by NGOs, uniting our teams working from Austria, Belgium, France, Germany, Ghana, Italy, Mauritania, the Netherlands, Portugal, Romania and Senegal.

For more information about the Structure and Objectives of the Glossary, click here...)

Filter bubble

Stefan Mertens, Leen d’Haenens and Rozane De Cock (2019, 142-143) observed that “[p]roponents of the filter bubble theory stress that within non-diverse, closed online groups where there is no room for alternative voices, opinions tend to ‘echo’, which locks users into their own – possibly false, but certainly limited – beliefs.” Eli Pariser (2011) similarly warns against the rise of online ‘micro-universes’ of personalized information – bubbles that filter out any contradicting information, letting in only what we want to hear. The term filter bubble is most notoriously used by Eli Pariser (2011) but other terms referring to the same phenomenon circulate as well such as “echo chambers” (Garrett 2009) or “partial information blindness” (Haim et al. 2018). Mertens et al. (2019) found that attitudes about immigration tend to be either far more positive or far more negative among frequent consumers of online news when they are compared with people who mostly get their news from legacy media (see also the entry on “legacy media”). Recently the concept of the filter bubble has been criticized as being used too frequently and hence the assumption of its reality overshadows the evidence of its existence (Bruns 2019).

⇢ see also: Attitudes, beliefs, and values, Legacy media

References and further reading:

Bruns, Axel. 2019. “Filter Bubble.” Internet Policy Review 8.4: 2–14. URL: https://policyreview.info/node/1426. Date of access: August 4, 2020.

Eisele, Olga, Tobias Heidenreich, Olga Litvyak, and Hajo G. Boomgaarden. 2023. “Capturing a News Frame – Comparing Machine-Learning Approaches to Frame Analysis with Different Degrees of Supervision” In Communication Methods and Measures 17.3: 205–226.

Garrett, R. Kelly. 2009. “Echo Chambers Online? Politically Motivated, Selective Exposure among Internet News Users.” Journal of Computer-Mediated Communication 14.2: 265–285.

Haim, Mario, Andras Graefe, and Hans-Bernd Brosius. 2018. “Burst of the Filter Bubble?” Digital Journalism 6.3: 330–343.

Mertens, Stefan, Leen d’Haenens, Rozane De Cock. 2019. “Online News Consumption and Public Sentiment towards Refugees: Is There a Filter Bubble at Play? Belgium, France, the Netherlands and Sweden: A Comparison.” In Images of Immigrants and Refugees in Western Europe: Media Representations, Public Opinion and Refugees’ Experiences, edited by Leen d’Haenens, Willem Joris, and François Heinderyckx, 141–157. Leuven: Leuven University Press. URL: https://www.jstor.org/stable/j.ctvh1dkhm.10. Date of access: September 8, 2023.

Pariser, Eli. 2011. The Filter Bubble: What the Internet Is Hiding from You. London: Penguin.

Turcotte, Jason, Lauren Furey, J. Omar Garcia-Ortega, Nicolas Hernandez; Carrissa Siccion, and Emily Stephenson. 2021. “The Novelty News Frame: How Social Identity Influences Policy Attention of Minority Presidential Candidates.” In Newspaper Research Journal 42.1: 95–110.

Category: A

Work Package: 4, 5

[DC / LH / SM]

 

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