Paper presented at the Misinformation and Media Symposium, University of Canberra, 10 Sep. 2018.

1. @qutdmrc
Misinformation and Media Symposium, Canberra, 10 Sep. 2018
Axel Bruns | @snurb_dot_info
Filter Bubbles in the Australian Twittersphere?

2. @qutdmrcAre Filter Bubbles Real?
Hell No.

3. @qutdmrc
Obama, B.H., & Windsor, H.C.A.D. (2017)
● Barack Obama to Prince Harry:
● “One of the dangers of the internet is
people can have entirely different
realities. They can be just cocooned
in information that reinforces their
current biases.”
● Nicholas Negroponte: Daily Me (1995)
● Cass Sunstein: echo chambers (2001,
2002, 2009, …)
● Eli Pariser: filter bubbles (2011)
(https://www.theguardian.com/uk-news/video/2017/dec/27/prince-harry-quizzes-
barack-obama-in-rapid-fire-exchange-video, 27 Dec. 2017)

4. @qutdmrc
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)

5. @qutdmrc
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 adhere to such practices, the more likely it is that
participants’ own views and information will circulate amongst group
members, rather than information introduced from the outside.
● Note that these patterns are determined by a mix of both algorithmic
curation and shaping and personal choice.

6. Empirical Work

7. @qutdmrc
Twitter in Australia
● A ‘big data’ approach, for one platform:
● Twitter in Australia:
● ~3.7m accounts (as of Feb. 2016), ~167m follower connections
● Filtered to accounts with 1000+ global follower connections:
● 255k accounts, 61m connections
● Captured all (public) tweets during Q1/2017:
● 55m tweets
Bruns, A., Moon, B., Münch, F., & Sadkowsky, T. (2017). The Australian Twittersphere in 2016:
Mapping the Follower/Followee Network. Social Media + Society, 3(4), 1–15.
https://doi.org/10.1177/2056305117748162
● Questions:
● Echo chamber tendencies in connection networks between these accounts?
● Follower / followee relationships
● Filter bubble tendencies in communicative engagement between these accounts?
● @mentions, retweets, all tweets

8. Clusters in the Australian Twittersphere
3.7m known Australian accounts
Network of follower connections
Filter: degree ≥1000 – 255k nodes (6.4%), 61m edges
Position: Force Atlas 2 algorithm in Gephi
Colour: Louvain Community Detection algorithm (resolution 0.25)

9. 3.7m known Australian accounts
Network of follower connections
Filter: degree ≥1000 – 255k nodes (6.4%), 61m edges
Labels: qualitative examination of lead accounts in each cluster
Clusters in the Australian Twittersphere
Teen Culture
Aspirational
Sports
Netizens
Arts & Culture
Politics
Television
Fashion
Popular Music
Food & Drinks
Agriculture Activism
Porn
Education
Cycling
News &
Generic
Hard Right
Progressive
South
Australia
Celebrities

10. @qutdmrc
Assessing Network Structures
● How exclusive are the groups?
● Strongly inwardly focussed = echo chambers / filter bubbles
● Strongly outwardly focussed = network bridges / information hubs
● Structural measure: Krackhardt E-I Index
● Difference of external and internal links as proportion of total:
𝐸−𝐼 𝐼𝑛𝑑𝑒𝑥 =
# 𝐸𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠 − # 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠
# 𝐸𝑥𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠 + # 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝐿𝑖𝑛𝑘𝑠
● Scale from +1 (100% external) to -1 (100% internal)

11. E-I Indices: follower relationships (in Feb. 2016)
Internal External
Teens
Gourmets
Porn
Netizens
Celebrities
Generic
Misc
Travel

12. @qutdmrc
E-I Indices: @mentions and retweets (Q1/2017)
Teens
Gamers
Horses
Porn
Commentators
Journalists
Politicians
Partisan Politics
Horses
Internal External
LGBTIQ
Gamers
Agriculture
Sharing from within Sharing from outside

13. @qutdmrc
Echo Chambers? Filter Bubbles?
● Very limited evidence in the Australian Twittersphere:
● E-I Index values largely positive (connections) or balanced (engagement)
● No sign of highly exclusionary patterns, except for outliers
● Echo chambers:
● Clear clustering tendencies, but disconnect only for specialist clusters (teens, gourmets, porn)
● Most E-I Indices > 0: more external than internal connections
● Filter bubbles:
● Balanced or moderately inward engagement; strongly inward only for specialist groups
● Retweeting generally more externally-focussed than @mentions: seeking information from outside
● Partisan political clusters diverge: pushing internal views to outside through retweets
● Limitations:
● Analysis only for accounts with 1000+ global follower/followee connections – need to repeat for full
network
● Engagement patterns during Q1/2017 may be affected by key events (e.g. Trump administration)

14. The Bigger Picture

15. @qutdmrc
A Lack of Evidence
● Echo chambers? Filter bubbles?
● Search:
● very limited personalisation of Google Search / News results in Germany (Krafft et al. 2018)
● Google News U.S. highly centralised around major mastheads (Nechushtai & Lewis 2018)
● Social media:
● Facebook promotes context collapse, not echo chambers / filter bubbles (Beam et al. 2018)
● range from supportive to contrarian engagement on Twitter (Vaccari et al. 2016)
● U.S. social media users hate seeing so much counter-attitudinal content (Pew Center 2016)
● Why this lack of evidence?
 People don’t actually care that much about news and politics
 Social media are engines of context collapse, not social isolation (Litt & Hargittai 2016)
 Inadvertent exposure bridges partisan divides (Brundidge 2010)
 Reinforcement-seeking and challenge-avoidance not intrinsically linked (Garrett 2009)
 Hyperpartisans actively monitor, share, critique, troll ‘enemy’ media (Garrett et al. 2013)
 People do encounter a diverse range of content – the question is what they do with it

16. @qutdmrc
Choc, M.V., & Kenny, C.K. (2018)

17. @qutdmrc
Misinformation and Media Symposium, Canberra, 10 Sep. 2018
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.”