Me, myself and I: Filter Bubbles & Echo Chambers

‘Filter bubbles’ and ‘echo chambers’ are popular terms to describe the phenomenon social scientists call ‘selective exposure’. The theory of selective exposure (Klapper, 1957) in brief states that people tend to select information which are in accord with their existing likes, and consequently avoid information that contradicts their beliefs and values.

Different digital tools, algorithms and behaviours rely on the collection of personal data to filter and/or rank items in the daily information stream, creating ‘filter bubbles’ and ‘echo chambers’. As a consequence, they result in a higher personalisation, but also a decreasing diversity of information. Diversity of information may refer to either source or content. Source diversity means the inclusion of a multitude of information sources by a news outlet, as well as the variety of news outlets consumed by a recipient. Content diversity means the range of topics and perspectives on a given topic (Haim, Graefe, & Brosius, 2018).

Despite describing similar phenomena, ‘filter bubbles’ and ‘echo chambers’ are not the same concept. ‘Echo chambers’, on the one hand, describe the phenomenon of being surrounded by link-minded contacts. This might lead to an amplification or reinforcement of pre-existing beliefs. ‘Filter bubbles’, on the other hand, refer to the algorithmic filtering of information to match a user’s needs (Haim, Graefe, & Brosius, 2018). However, there is no consistency in the use of both terms; for example, Lewandowsky et al (2017) describe ‘echo chambers’ as the space where “most available information conforms to pre-existing attitudes and biases” (p. 359).

Studies have shown that people are more likely to share articles with which they agree (An, Quercia, Cha, Gummadi, & Crowcroft, 2014) and that social media expose the community to a narrower range of information sources, compared to a baseline of information seeking activities. Research has also shown that the diversity of social media communication is significantly lower than the one of interpersonal communication, both on an individual and collective level (Nikolov, Oliveira, Flammini, & Menczer, 2015).

But why do people surround themselves with like-minded contacts, why do they choose information that confirms what they already believe? There are different answers to this question. The theory of cognitive dissonance (Festinger, 1957) explains this phenomenon arguing that individual strives for consistency (or consonance) of their believes, attitudes, knowledge etc. Inconsistencies cause psychological discomfort, which Festinger calls dissonance. Another answer is that surrounding oneself with familiar information helps to cope with or even overcome information overload (Pariser 2011). A third answer refers to the social aspect of social media: because of its sharing mechanisms, discovering information becomes a social endeavour rather than an individual process.

In the context of social media, ‘filter bubbles’ and ‘echo chambers’ therefore allow users to avoid psychological discomfort and information overload and to engage in a social endeavour in the process of information seeking. However, they pose great risks leading to self-reinforcement and reduced information diversity (Haim, Graefe, & Brosius, 2018). The tendency to surround oneself with like-minded opinions might also prevent an engagement with other ideas. This can facilitate confirmation bias and polarisation (Nikolov, Oliveira, Flammini, & Menczer, 2015; Haim, Graefe, & Brosius, 2018).

But it’s not all bad news: ‘echo chambers’ seem to be focused mainly on political discourse (Nikolov, Oliveira, Flammini, & Menczer, 2015) whereas other topic areas are less affected. Furthermore, there are tools that encourage and enable users to seek information beyond their ‘bubble’. The EUNOMIA feature illustrating how information has been spread and changed by different users aims exactly to show how similar information is discussed in different ‘bubbles’.

References

An, J., Quercia, D., & Crowcroft, J. (2013). Fragmented Social Media: A Look into Selective Exposure to Political News. WWW 2013 Companion, May 13–17, 2013, Rio de Janeiro, Brazil. ACM 978-1-4503-2038-2/13/05.

Festinger, L. (1957). A Theory of Cognitive Dissonance. Stanford, CA: Stanford University Press.

Haim, M., Graefe, A., & Brosius, H.-B. (2018). Burst of the Filter Bubble? Effects of personalization on the diversity of Google News. Digital Journalism, 6(3), 330-343. https://doi.org/10.1080/21670811.2017.1338145

Klapper, J. T. (1957). What We Know About the Effects of Mass Communication: The Brink of Hope. The Public Opinion Quarterly, 21(4), 453-474. https://doi.org/10.1086/266744

Lewandowsky, S., Ecker, U. K., & Cook, J. (2017). Beyond Misinformation: Understanding and Coping with the “Post-Truth” Era. Journal of Applied Research in Memory and Cognition, 6(4), 353–369. https://doi.org/10.1016/j.jarmac.2017.07.008

Nikolov, D., Oliveira, D. F., Flammini, A., & Menczer, F. (2015). Measuring online social bubbles. PeerJ Computer Science, 1(38), 1-14. https://doi.org/10.7717/peerj-cs.38

Pariser, E. (2011). The Filter Bubble: How the New Personalized Web is Changing What We Read and How We Think. New York: Penguin.

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