Paper presented at IAMCR 2019, Madrid, 10 July 2019.

1. @qutdmrc
IAMCR, Madrid, 7-11 July 2019
Axel Bruns | @snurb_dot_info
It’s Not the Technology, Stupid:
How the ‘Echo Chamber’ and ‘Filter Bubble’ Metaphors Have Failed Us

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3. 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)

6. 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

9. 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
● Cross-ideological connections almost impossible to avoid:
● Facebook groups and 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), …
● … 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:
● Algorithms provide only limited personalisation
● Also because our interests and networks are complex and inconsistent
● 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

12. 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)
● Fifteen 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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IAMCR, Madrid, 7-11 July 2019
Axel Bruns | @snurb_dot_info
@socialmediaQUT – http://socialmedia.qut.edu.au/
@qutdmrc – https://www.qut.edu.au/research/dmrc
This research is 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.”