Invited presentation to Media Futures: Research Centre for Responsible Media Technology and Innovation, Bergen, Norway, 16 Apr. 2021.

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Are Filter Bubbles Real?
Prof. Axel Bruns
Digital Media Research Centre,
Queensland University of Technology
@snurb_dot_info

2. @qutdmrc

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The Concerns

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Obama, B.H. (2017)
• Barack Obama’s farewell address:
• “For too many of us it’s become safer to retreat into our
own bubbles, whether in our neighborhoods, or on
college campuses, or places of worship, or especially
our social media feeds, surrounded by people who look
like us and share the same political outlook and never
challenge our assumptions.”
• Nicholas Negroponte: Daily Me (1995)
• Cass Sunstein: echo chambers (2001, 2009, 2017, …)
• Eli Pariser: filter bubbles (2011)
(https://edition.cnn.com/2017/01/10/politics/president-obama-farewell-
speech/index.html, 11 Jan. 2017)

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Bubble Trouble
• Echo Chambers? Filter Bubbles?
• Where exactly?
• General search engines
• News search engines, portals, and recommender systems
• Social media (but where – profiles, pages, hashtags, groups …?)
• What exactly?
• Hermetically sealed information enclaves full of misinformation?
• Self-reinforcing ideological in-groups of hyperpartisans?
• Politically partisan communities of any kind?
• Why exactly?
• Ideological and societal polarisation amongst citizens?
• Algorithmic construction of distinct and separate publics?
• Feedback loop between the two?
• Defined how exactly?
• Argument from anecdote and ‘common sense’, rather than empirical evidence
• Promoted by non-experts (Sunstein: legal scholar; Pariser: activist and tech entrepreneur)

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Echo Chambers? Filter Bubbles?
• What even are they?
• Definitional uncertainty, despite (or because of) Sunstein and Pariser
• Vague uses especially in mainstream discourse, often used interchangeably
• Both fundamentally related to underlying network structures
• Fundamental differences:
• Echo chambers: connectivity, i.e. closed groups vs. overlapping publics
• Filter bubbles: communication, i.e. deliberate exclusion vs. widespread sharing
echo chamber filter bubble

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Working Definitions
• An echo chamber comes into being where a group of participants choose to preferentially connect with
each other, to the exclusion of outsiders. The more fully formed this network is (that is, the more
connections are created within the group, and the more connections with outsiders are severed), the more
isolated from the introduction of outside views is the group, while the views of its members are able to
circulate widely within it.
• A filter bubble emerges when a group of participants, independent of the underlying network structures of
their connections with others, choose to preferentially communicate with each other, to the exclusion of
outsiders. The more consistently they exercise this choice, the more likely it is that participants’ own views
and information will circulate amongst group members, rather than any information introduced from the
outside.
• Note that these patterns are determined by a mix of both algorithmic curation and shaping and personal
choice.

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The Evidence

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Echo Chambers and Filter Bubbles in Social Media
• Early blogosphere studies:
• Strong U.S. focus
• Polarisation and ‘mild echo chambers’
• E.g. Adamic & Glance (2005)
• Social media studies:
• Especially Twitter, less research on Facebook or other platforms
• Hashtag / keyword datasets
• ‘Open forums and echo chambers’
• Significant distinctions between @mention, retweet, follow networks
• And between lead users and more casual participants
• E.g. Williams et al. (2015)
Adamic & Glance (2005)
Williams et al. (2015)

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Pew Center (2016)
Süddeutsche Zeitung (2017)
Bruns et al. (2017)
And Yet…
• Social media surveys:
• Users do encounter counter-attitudinal political views in their networks …
• … to the point of exhaustion
• E.g. Pew Center (2016)
• Broader network mapping:
• Political partisans share similar interests (except for the political fringe)
• E.g. Süddeutsche Zeitung (2017)
• Comprehensive national studies:
• Whole-of-platform networks show thematic clustering, but few
fundamental disconnections
• E.g. Bruns et al. (2017)
US

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New Evidence on Filter Bubbles in Search
• Mid-scale tests:
• No personalised filter bubbles in search results for U.S. politicians
• 41 of 47 outlets recommended to conservatives and liberals
• Five dominant news sources: almost too much uniformity
• See Nechushtai & Lewis (2019)
• Also for Germany: Haim et al. (2018)
• Large-scale tests:
• No personalised filter bubble in searches for German parties and politicians
• Largely identical search results
• In 5-10% of cases even in the same order
• See AlgorithmWatch (2018)
Nechushtai & Lewis (2019)
(https://www.youtube.com/watch?v=lQ3KHiqGmDE)

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The Reasons

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Case Studies Shouldn’t Be Generalised
• Need to see the big picture:
• Individual hashtags or pages may be ideologically pure, …
• … but they’re embedded in a complex platform structure (Dubois & Blank 2018)
• Serendipity is ubiquitous:
• Habitual newssharing in everyday, non-political contexts
• Selective exposure ≠ selective avoidance: we seek, but we don’t evade (Weeks et al. 2016)
• Homophily ≠ heterophobia: ‘echo chambers’ might just be communities of interest

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Case Studies Shouldn’t Be Generalised
• Cross-ideological connections almost impossible to avoid:
• Facebook pages may be engines of homophily, …
• … but Facebook profiles are engines of context collapse (Litt & Hargittai 2016)
• Because we don’t only connect with our ‘political compadres’, pace Pariser (2015)
• ‘Hard’ echo chambers / filter bubbles are possible, but very rare:
• Requires cultish levels of devotion to ideological purity (O’Hara and Stevens 2015)
• E.g. specialty platforms for hyperpartisan fringe groups (4chan, 8chan, Gab, Parler), …
• … but the hyperpartisans are also heavy users of mainstream news
• Even if only to develop new conspiracy theories and disinformation (Garrett et al. 2013)

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Self-Serving Techno-Determinism
• Humans are complicated:
• Our interests and networks are diverse, complex, inconsistent
• Politics is just a small part of what people are interested in
• Algorithms provide only limited personalisation
• Mainstream information sources + random serendipity = mixed information diet
• Moral panics based on simplistic arguments:
• Sunstein & Pariser mainly provide personal, anecdotal evidence
• Significant overestimation of the power of AI at least since Negroponte
• “A myth just waiting to concretize into common wisdom” (Weinberger 2004)
• But very handy for blame-shifting and attacking social media platforms
• “The dumbest metaphor on the Internet” (Meineck 2018):
• Not just dumb, but keeping us from seeing more important challenges
• People do encounter a diverse range of content, …
• … but the question is what they do with it
https://www.vice.com/de/article/pam5nz/deshalb-ist-filterblase-die-blodeste-metapher-des-internets

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The Problem

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It’s the People, Stupid – Not the Technology
The problem with an extraterrestrial-conspiracy mailing list
isn’t that it’s an echo chamber; it’s that it thinks
there’s a conspiracy by extraterrestrials.
— Weinberger (2004)
• Seventeen years later:
• The problem isn’t that there are hyperpartisan echo chambers or filter bubbles; it’s that there
are hyperpartisan fringe groups that fundamentally reject, and actively fight, any mainstream
societal and democratic consensus.
The problem is political polarisation, not communicative fragmentation.
There is no echo chamber or filter bubble – the filter is in our heads.

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Further Outlook
• Understanding polarisation:
• How might we assess levels of polarisation – over time, across countries, between groups, across platforms?
• How do individuals slide into hyperpartisanship, and how can this be reversed?
• How do hyperpartisan groups process information that challenges their worldviews?
• What processes drive their dissemination of mis/disinformation, conspiracy theories, trolling, and abuse?
• Combatting polarisation:
• How can mainstream society be protected from hyperpartisanship?
• How can hyperpartisan extremists be deradicalised?
• How can mis/disinformation be countered and neutralised?
• What role can digital media literacy play, and how can its abuse be prevented?
• Methodological frameworks:
• Media and communication studies work on opinion leadership and multi-step flows
• Cultural studies work on negotiated and oppositional readings
• Media psychology work on cognitive processing of media content
• Digital media studies work on population-scale processes of news engagement
• Education work on digital media literacies across all age groups

19. @qutdmrc

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@socialmediaQUT – http://socialmedia.qut.edu.au/
@qutdmrc – https://research.qut.edu.au/dmrc/
This research was supported by the ARC Future
Fellowship project “Understanding Intermedia
Information Flows in the Australian Online Public
Sphere”, the ARC Discovery project “Journalism
beyond the Crisis: Emerging Forms, Practices, and
Uses”, and the ARC LIEF project “TrISMA: Tracking
Infrastructure for Social Media Analysis.”
Thank you!