The document discusses whether filter bubbles and echo chambers are real phenomena on social media and in search engines. It defines filter bubbles as preferentially communicating within a group to exclude outsiders, while echo chambers preferentially connect within a group to exclude outsiders. Studies show some ideological clustering online but most people encounter diverse views. The problems are political polarization and fringe groups rejecting consensus, not technology fragmentation. Understanding and combating polarization is more important than worrying about filter bubbles and echo chambers.

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African Digital Media Research Methods Symposium, 6 June 2019
Axel Bruns | @snurb_dot_info
Are Filter Bubbles Real?

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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)

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Echo Chambers? Filter Bubbles?
● What even are they?
● Both fundamentally related to underlying network structures
● Definitional uncertainty, despite (or because of) Sunstein and Pariser
● Vague uses especially in mainstream discourse, often used interchangeably
● 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.

8. 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 Algorithm Watch (2018)
Nechushtai & Lewis (2019)
(https://www.youtube.com/watch?v=lQ3KHiqGmDE)

12. 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 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

15. 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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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 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

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African Digital Media Research Methods Symposium, 6 June 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.”