Echo chambers and filter bubbles

Echo Chambers and Filter Bubbles of
Fake News in Social Media:
Man-Made or Produced by Algorithms?
Franziska Zimmer*, Katrin Scheibe*, Mechtild Stock**, &
Wolfgang G. Stock*
*: Heinrich Heine University Düsseldorf, Germany, Department of Information Science
**: Stock - Kerpen, Germany

ECHO CHAMBERS AND FILTER BUBBLES OF FAKE NEWS IN SOCIAL
MEDIA: MAN-MADE OR PRODUCED BY ALGORITHMS?
A B S T R A CT
2
This talk is on the influence of man-made echo chambers and
machine-produced filter bubbles on the dissemination of fake news
and deceptions on social media. Our leading research questions are:
(1) If filter bubbles are supported by machines, how do such
algorithms work? (2) And if echo chambers are indeed man-made,
what are the cognitive states’ patterns of those individuals reacting
on deceptions and fake news?
In Germany, there is a long tradition in information science research
on fake news going back to the 1960s. The researchers always
considered both, algorithms as well as people‘s information
behavior, as interwoven conditions of the explanation of the
transmission of fake news.

A B S T R A CT
3
and deceptions on social media (as, for instance, Facebook or
Reddit). Our leading research questions are: (1) If filter bubbles are
supported by machines, how do such algorithms work? (2) And if
echo chambers are indeed man-made, what are the cognitive states’
patterns of those individuals reacting on deceptions and fake news?
???

A B S T R A CT
4
and deceptions on social media (as, for instance, Facebook or
Reddit). Our leading research questions are: (1) If filter bubbles are
supported by machines, how do such algorithms work? (2) And if
echo chambers are indeed man-made, what are the cognitive states’
patterns of those individuals reacting on deceptions and fake news?
False propositions
Fake news
Disinformation

INTRODUCTION
5
False propositions
Fake news
Misinformation

AGENDA
6
1. Introduction
2. Background
3. Knowledge, information, and truth
4. Relevance, pertinence, and ranking algorithms
5. Patterns of cognitive processes in response to fake news
6. Conclusion

INTRODUCTION
8
“Fake News” (green) and “Filter bubble / echo chamber” (red) as
research topics
Source: Scopus; Search arguments: (1) “filter bubble*” OR “echo chamber*” (TITLE-ABS-KEY), N = 272; (2) “fake
news” (TITLE-ABS-KEY), N = 265;

INTRODUCTION
9
Fake News on social media in the popular press

INTRODUCTION
10
Research Question 1: Is the dissemination of fake news and
deceptions supported by machines through the automatic
construction of filter bubbles, and how do such algorithms work?

INTRODUCTION
11
Research Question 1: Is the dissemination of fake news and
deceptions supported by machines through the automatic
construction of filter bubbles, and how do such algorithms work?
Research Question 2: Are echo chambers of fake news man-made,
and, if yes, what are the information behavior patterns of those
individuals reacting on deceptions and fake news?

BACKGROUND
13
“Fake news:” information including “phony news stories maliciously
spread by outlets that mimic legitimate news sources” (Torres,
Gerhart, & Negahban, 2018)
• Misinformation: transmitting untrue propositions,
nonconsidering the cognitive state of the sender
• Disinformation: again, transmitting untrue propositions, but
now consciously by the sender
• Deception: kind of disinformation, which brings an
advantage to the sender

BACKGROUND
14
“User of social media services:” consumer (only receiving content),
producer (producing and distributing content), or participator (liking
or sharing content)
“Algorithms:” sets of rules defining sequences of operations;
implemented as computer programs in computational machinery

BACKGROUND
15
“Filter bubbles” and “echo chambers” are metaphorical expressions
“Filter bubble:” a “unique universe of information for each of us”
(Pariser, 2011); “algorithmic filtering which personalizes content
presented on social media” (Dubois & Blank, 2018)
“Filter bubble” is more on algorithmic information filtering and
results’ presentation in online services
“Echo chamber:” a “situation where only certain ideas, information
and beliefs are shared” (Dubois & Blank, 2018); users in echo
chambers are on a media or content “diet” (Case & Given, 2018) or
an “ideological isolation” (Flaxman, Goel, & Rao, 2016)
“Echo chamber” is more on human information behavior

BACKGROUND
16
Filter bubbles or echo chambers of fake news may be influenced by
“selective exposure of information” (Hyman & Sheatsley, 1947) and
a “confirmation bias” (Vydiswaran, Zhai, Roth, & Pirolli, 2015)
“The strongest form of the selective exposure proposition is that
people prefer exposure to communications that agree with their pre-
existing opinions” (Sears & Freedman, 1967)
“Disintermediation:” social media enables a direct path from
producers to consumers of user-generated contents
For Habermas (2006) social media plays “a parasitical role of online
communication” (Habermas, 2006)
The disappearance of intermediation fosters the “parasitical roles” of
fake news and deceptions in social media

KNOWLEDGE, INFORMATION, AND TRUTH
17

18
Knowledge: h is known by S =Df h is accepted by S; h is true; and h
is nondefectively evident for S
h: proposition; S: subject
Information: sets dynamically knowledge “into motion”
However, information sets also misinformation and disinformation
into motion
Information is not responsible for truth value (Kuhlen, 1985;
Buckland, 1991)
Therefore, it is terminologically very problematic to speak of
“true/false information,” as only propositions are truth bearers
h is true: theories of truth

19
Theories of truth
Truth: relation between a proposition
and a reference object
(“What is truth?” John 18:38;Nikolai Nikolajewitsch Ge)
Not applicable in
social media
No general
indication on
the
truth

RELEVANCE, PERTINENCE,
AND RANKING ALGORITHMS
20

RELEVANCE, PERTINENCE, AND RANKING ALGORITHMS
21
Relevance: A document (e.g., a blog post, a post on Facebook or
Reddit, or a microblog on Twitter) is relevant for the satisfaction of an
objective (i.e. subject-independent) information need
Pertinence: A document “is pertinent if it is topically relevant and if it
is appropriate for the person” (Soergel, 1994); pertinence leads to
personalized ranking of documents
Case study: Facebook
• Sorting of posts: always pertinence ranking
• Only posts from “friends” or “liked” pages
• Three sorting factors
• Affinity, Weighting, Timeliness

22
Affinity
• user’s previous interactions
with posting page
Weighting
• frequency of the user’s actions
(views, comments, likes, shares)
on the posting page
• position of the creator of the post
• nature of the post (text, image, or video)
Timeliness
• the newer the post, the more important
Other criteria
• posts from people (as opposed to those from companies) are
weighted higher
• spatial proximity between posting and reading user
Only posts from “friends” or
“liked” pages
User‘s information
behavior

23
RQ1: Is the dissemination of fake news and deceptions supported by
machines through the automatic construction of filter bubbles, and
how do such algorithms work?
Algorithms by themselves do not produce filter bubbles or
subsequently echo chambers, they only consolidate the users’
information behavior patterns; algorithms are able to amplify human
information behavior patterns
The users play the leading roles concerning construction and
maintenance of bubbles of (fake) news
The users’ behavior feeds the pertinence ranking algorithms;
therefore, the users (consciously or unintentionally) cooperate with
the service
Indeed, there are filter bubbles; however, they are fed by users’
information behavior and―more important―they are escapable

PATTERNS OF COGNITIVE PROCESSES
IN RESPONSE TO FAKE NEWS
24

PATTERNS OF COGNITIVE PROCESSES IN RESPONSE TO FAKE NEWS
25
Empirical study
• Case study research
• (1) Hillary Clinton selling weapons to the Islamic State
• (2) Alexander van der Bellen was called a cancer patient as
well as demented and therefore unable to become president
of Austria
• Content analysis
• Quantitative and qualitative
• Creation of categories: inductive (or conventional) and
deductive (or directed) measures
• Coding: (intellectually) conducted by two scientists
• After discussions: Krippendorff’s alpha = 1

26
Classes for content analysis
• Confirmation: broad agreement with post, attempt of verification
• Denial: broad disagreement with post, attempt of falsification
• Moral Outrage: questioning the posts, comments, and replies
from a moral point of view
• New Rumor: creation of a new probably false proposition
• Satire: satirical, ironic, or sarcastic text
• Off Topic: ignoring the discussion, arguing on other topics, broad
generalization
• Insult: defamation of other people or groups
• “Meta” Comment/Reply: discussing the style of another post,
offense against a commentator

27
Case (1). Situation: the news is brand-new, it is neither verified nor
falsified; context: U.S. before the presidential elections in 2016
Triggering fake article about Hillary Clinton and her relations to the “Islamic State” on “The Political
Insider”

28
Case (1). a) The Political Insider. Post: “Wikileaks CONFIRMS Hillary Sold Weapons
to ISIS… Then Drops Another BOMBSHELL! Breaking News”
Cognitive Pattern Comments Replies
Confirmation 33.3 % 23.1 %
Denial 3.3 % ---
Moral Outrage 3.3 % ---
New Rumor 13.3 % 15.4 %
Satire --- ---
Off Topic 26.6 % 61.5 %
Insult 20.6 % ---
“Meta” --- ---
Positive Orientation 73.3 % 46.2 %
Negative Orientation 3.3 % ---
Neutral Orientation 23.3 % 53.8 %
N 30 13

29
Case (1). b) r/The_Donald. Post: “Breaking Assange: Obama & Clinton not only
supplied ISIS with a billion dollars worth of weapons annually, they paid these
mercenaries salaries! Obama employed ISIS… let it sink in. Obama was the real
leader of ISIS!”
Denial --- 4.0 %
Moral Outrage --- ---
New Rumor 5.3 % 5.0 %
Satire 1.3 % 2.0 %
Off Topic 47.4 % 78.2 %
Insult 5.3 % 3.0 %
“Meta” --- ---
Negative Orientation --- 5.0 %
N 76 101

30
Case (1). c) r/worldpolitics. Post: “Julian Assange: ‘1,700 emails’ proves Hillary Clinton
sold weapons to ISIS in Syria.”
Denial 29.2 % 6.1 %
Moral Outrage --- 1.0 %
New Rumor 2.1 % 0.5 %
Satire 4.2 % 0.5 %
Off Topic 43.8 % 72.2 %
Insult 2.1 % 0.5 %
“Meta” 6.3 % 10.1 %
Negative Orientation 31.3 % 6.6 %
N 48 198

31
Case (1). Qualitative content analysis
Most frequent cognitive pattern: Off topic
In all cases (a to c): (much) more off topic replies than off topic comments
not the same topic, but the same tendency
Examples:
r/worldpolitics: “time to put up or shut up”
r/The_Donald: “Trump was right all along”
r/The_Donald: “LOL who knew”
r/The_Donald: “Holy shit!!”
Case Comments Replies Source
a) 27.0% 62.0% The Political Insider
b) 47.0% 78.0% r/The_Donald
c) 44.0% 72.0% r/worldpolitics

32
Cognitive pattern: Confirmation
In all cases (a to c): less confirmation replies than confirmation comments
Examples:
The Political Insider: “I don’t know what we’d do without Wikileaks?”
The Political Insider: “Julian Rules. Thank you Wiki Leaks!”
a) 33.0% 23.0% The Political Insider
b) 41.0% 8.0% r/The_Donald
c) 13.0% 9.0% r/worldpolitics

33
Meta position
Example: “I’m really not interested in engaging in a totally off-topic argument with you”
(r/worldpolitics)
Insult
Example: “Aw, come on. Whadya expect from a fuckin’ Kenyan ‘born’ in Hawaii,
raised in Indonesia, programmed and sponsored by the Saudi Manchurian School for
Gifted Leftists?” (r/The_Donald)
New rumor
Example: “They (Obama and Clinton, a/n) wanted this war in Syria, they wanted the
refugee influx” (r/The_Donald)
Denial
Example: “1700 mails about Libya proof that Hillary sold weapons to Isis in Syria? I
don’t mean to comment on the allegations but I hate it when headlines are clearly
bullshit” (r/worldpolitics)

34
Case (2). Situation: the fake is falsified; context: Austria before the
presidential elections in 2016
Disclosure of the fake news on van der Bellen’s health on Vienna.at’s Facebook page

35
Case (2). Post: Der Standard: “Der is super beinand;” Vienna.at: “Es besteht kein
Grund zur Sorge.” [“He is super healthy;” “There is no need to worry”.]
Denial 9.6 % 7.2 %
Moral Outrage 28.2 % 8.4 %
New Rumor 7.8 % 2.4 %
Satire 12.6 % 3.6 %
Off Topic 35.9 % 59.0 %
Insult 1.9 % 6.0 %
“Meta” --- 7.2 %
Negative Orientation 14.6 % 7.2 %
N 103 83

36
As in case (1), most frequent cognitive pattern: Off topic
Here as well: (much) more off topic replies than off topic comments
not the same topic, but the same tendency
Examples:
“Lately, I remember Jörg Haider again and again. His personal preferences were
known to politicians and journalists, but were NOT made public”
“Even a politician is a human being”
a) 36.0% 59.0% Standard/Vienna.at

37
Second most frequent cognitive pattern: Moral outrage
Examples:
“It is really sad that people acting in public have to justify their state of health because
every effort is made to make them look bad”
“It is embarrassing for Austria that you have to break the legal doctor-patient
confidentiality in order to be able to resist malicious accusations”
“I think it is embarrassing (zum Fremdschämen) that such measures need to be
taken. ... There are limits, and to claim that someone is sick to death, only to gain a
political advantage, is disgraceful (letztklassig)!”
a) 28.0% 8.0% Standard/Vienna.at

38
Satire
Example: “Apparently, many previous findings are wrong … Being a chain-smoker is
not unhealthy .”
“Uh-oh. Now many new lies have to come up.”
New rumor
Example: “VdB (van der Bellen, a/n) did not get cancer—obviously a member of the
illuminati”
Denial
Example: “I’m glad that he (van der Bellen, a/n) is very healthy. However, he should
get his brain checked. If he thinks that ‘Islamization in Europe’ does not bother him,
then he has an injury in his head ;) (Das freut mich, dass er pumperlgesund ist. Nur
sollt er sich das Hirn untersuchen lassen. Wenn er meint ‘die Islamisierung in Europa’
stört ihn nicht, dann hat er fix a Della in da Jodldosn).”

39
RQ2: Are echo chambers of fake news man-made, and, if yes, what are the
information behavior patterns of those individuals reacting on deceptions and fake
news?
1st attempt (narrow definition of “echo chamber”)
“Community with high confirmation rates (in our case: for fake news) in combination
with high degrees of positive topic-specific orientation (and further with the creation of
new rumors with the same direction as the original fake)”
A third of the commentators of The Political Insider and about two fifth of the
commenting audience of r/The_Donald seem to argue inside their echo chamber
Even when the fake in case study no. 2 is disclosed, still 10% of the analyzed Austrian
audience refuses to believe that it really was a false proposition

40
RQ2: Echo chamber? Selective exposure of information?
2nd attempt (broad definition of “echo chamber”)
As we know from the texts that off-topic comments and most of the neutral-orientation
texts argue in the same direction as the entire community, the filter bubble may
include most of these comments and replies: The content of the specific (false)
proposition is entirely clear and taken for granted, so users lose the specific thread
(from the triggering post); however, they do not lose the (ideological or political)
direction.
about 90 % of comments (sum of confirmations and off topic comments) in
r/The_Donald
about 60 % in The Political Insider
about 55 % in r/worldpolitics

CONCLUSION
42
Truth
Only propositions are truth bearers
Fake news (disinformation / misinformation) includes false
propositions
Correspondence theory of truth: not applicable in mediated contexts
The other truth theories heavily depend on the community
(consensus theory) and on the coherence of propositions
(coherence theory), but do not point to reality

CONCLUSION
43
Algorithms and Filter Bubbles
Algorithms (and their mechanisms to form filter bubbles) applied in
social media do not form communities alone; however, they amplify
users’ information behavior
Filter bubbles exist because algorithms are trained by their users:
the information behavior determines what content they see, and
therefore, what fake news they see as well

CONCLUSION
44
Users and Echo Chambers
People form their own echo chambers. Furthermore, they get
reinforced through “their” filter bubbles
Reading (fake) news and eventually drafting a comment or a reply
may be the result of users’ selective exposure to information leading
to prefer news (including fake news) fitting their pre-existing opinions
The most common information behavior pattern in commenting fake
news is arguing off topic (which leads into the same direction as the
fake news)

CONCLUSION
45
However, selective exposure of information is not the only cognitive
pattern. Additionally, we observed cognitive patterns outside of echo
chambers as
• denial
• moral outrage
• satire
There is different information behavior concerning writing comments
and writing replies to comments:
• more off topic replies than off topic comments
• less confirmation replies than confirmation comments

CONCLUSION
46
Depending on the concrete operationalization of “echo chamber,”
about one third to two fifth (narrow definition) and more than half of
all analyzed comments and replies (broad definition) can be located
inside an echo chamber of fake news
Everyone needs to be aware of their information behavior, and their
cognitive patterns

CONCLUSION
47
Echo Chambers and Filter Bubbles of Fake News in
Social Media: Man-Made or Produced by Algorithms?
The creation and maintenance of filter bubbles and echo chambers
of fake news is mostly on the individual user’s critical literacy,
information literacy, digital literacy, and media literacy
There are no “bad” algorithms; however, social media services‘
software amplifies user behavior

48
Thank you and Aloha!
Stock@phil.hhu.de
Best wishes from Düsseldorf and Kerpen, Germany

49
You will find the full paper (including references) here:
http://www.isi.hhu.de/fileadmin/redaktion/Fakultaeten/Phil
osophische_Fakultaet/Sprache_und_Information/Informa
tionswissenschaft/Dateien/Wolfgang_G._Stock/2019/Zim
mer_Scheibe_Stock_Stock_Fake_News.pdf

Echo chambers and filter bubbles

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