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The echo chamber effect on social media

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Social media may limit the exposure to diverse perspectives and favor the formation of groups of like-minded users framing and reinforcing a shared narrative, that is, echo chambers. However, the interaction paradigms among users and feed algorithms greatly vary across social media platforms. This paper explores the key differences between the main social media platforms and how they are likely to influence information spreading and echo chambers’ formation. We perform a comparative analysis of more than 100 million pieces of content concerning several controversial topics (e.g., gun control, vaccination, abortion) from Gab, Facebook, Reddit, and Twitter. We quantify echo chambers over social media by two main ingredients: 1) homophily in the interaction networks and 2) bias in the information diffusion toward like-minded peers. Our results show that the aggregation of users in homophilic clusters dominate online interactions on Facebook and Twitter. We conclude the paper by directly comparing news consumption on Facebook and Reddit, finding higher segregation on Facebook.
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COMPUTER SCIENCESPSYCHOLOGICAL AND
COGNITIVE SCIENCES
The echo chamber effect on social media
Matteo Cinellia, Gianmarco De Francisci Moralesb, Alessandro Galeazzic, Walter Quattrociocchid,1 ,
and Michele Starninib
aDepartment of Environmental Sciences, Informatics and Statistics, Ca’Foscari Univerity of Venice, 30172 Venice, Italy; bInstitute for Scientific Interchange
(ISI) Foundation, 10126 Torino, Italy; cDepartment of Information Engineering, University of Brescia, 25123 Brescia, Italy; and dDepartment of Computer
Science, Sapienza University of Rome, 00185 Rome, Italy
Edited by Arild Underdal, University of Oslo, Oslo, Norway, and approved January 14, 2021 (received for review November 15, 2020)
Social media may limit the exposure to diverse perspectives and
favor the formation of groups of like-minded users framing and
reinforcing a shared narrative, that is, echo chambers. However,
the interaction paradigms among users and feed algorithms greatly
vary across social media platforms. This paper explores the key dif-
ferences between the main social media platforms and how they
are likely to influence information spreading and echo chambers’
formation. We perform a comparative analysis of more than 100
million pieces of content concerning several controversial topics
(e.g., gun control, vaccination, abortion) from Gab, Facebook, Red-
dit, and Twitter. We quantify echo chambers over social media by
two main ingredients: 1) homophily in the interaction networks
and 2) bias in the information diffusion toward like-minded peers.
Our results show that the aggregation of users in homophilic clus-
ters dominate online interactions on Facebook and Twitter. We
conclude the paper by directly comparing news consumption on
Facebook and Reddit, finding higher segregation on Facebook.
information spreading |echo chambers |social media |polarization
Social media radically changed the mechanism by which
we access information and form our opinions (1–5). We
need to understand how people seek or avoid information and
how those decisions affect their behavior (6), especially when
the news cycle—dominated by the disintermediated diffusion
of information—alters the way information is consumed and
reported on. A recent study (7) limited to Twitter claimed that
fake news travels faster than real news. However, a multitude of
factors affects information spreading on social media platforms.
Online polarization, for instance, may foster misinformation
spreading (1, 8). Our attention span remains limited (9, 10),
and feed algorithms might limit our selection process by sug-
gesting contents similar to the ones we are usually exposed to
(11–13). Furthermore, users show a tendency to favor informa-
tion adhering to their beliefs and join groups formed around
a shared narrative, that is, echo chambers (1, 14–18). We can
broadly define echo chambers as environments in which the
opinion, political leaning, or belief of users about a topic gets
reinforced due to repeated interactions with peers or sources
having similar tendencies and attitudes. Selective exposure (19)
and confirmation bias (20) (i.e., the tendency to seek information
adhering to preexisting opinions) may explain the emergence of
echo chambers on social media (1, 17, 21, 22).
According to group polarization theory (23), an echo chamber
can act as a mechanism to reinforce an existing opinion within
a group and, as a result, move the entire group toward more
extreme positions. Echo chambers have been shown to exist in
various forms of online media such as blogs (24), forums (25),
and social media sites (26–28). Some studies point out echo
chambers as an emerging effect of human tendencies, such as
selective exposure, contagion, and group polarization (13, 23,
29–31). However, recently, the effects and the very existence
of echo chambers have been questioned (2, 27, 32). This issue
is also fueled by the scarcity of comparative studies on social
media, especially concerning news consumption (33). In this
context, the debate around echo chambers is fundamental to
understanding social media’s influence on information consump-
tion and public opinion formation. In this paper, we explore
the key differences between social media platforms and how
they are likely to influence the formation of echo chambers
or not. As recently shown in the case of selective exposure to
news outlets, studies considering multiple platforms can offer
a fresh view on long-debated problems (34). Different plat-
forms offer different interaction paradigms to users, ranging
from retweets and mentions on Twitter to likes and comments in
groups on Facebook, thus triggering very different social dynam-
ics (35). We introduce an operational definition of echo cham-
bers to provide a common methodological ground to explore
how different platforms influence their formation. In particular,
we operationalize the two common elements that character-
ize echo chambers into observables that can be quantified and
empirically measured, namely, 1) the inference of the user’s
leaning for a specific topic (e.g., politics, vaccines) and 2) the
structure of their social interactions on the platform. Then, we
use these elements to assess echo chambers’ presence by looking
at two different aspects: 1) homophily in interactions concern-
ing a specific topic and 2) bias in information diffusion from
like-minded sources. We focus our analysis on multiple plat-
forms: Facebook, Twitter, Reddit, and Gab. These platforms
present similar features and functionalities (e.g., they all allow
social feedback actions such as likes or upvotes) and design
(e.g., Gab is similar to Twitter) but also distinctive features
(e.g., Reddit is structured in communities of interest called sub-
reddits). Reddit is one of the most visited websites worldwide
(https://www.alexa.com/siteinfo/reddit.com) and is organized as
a forum to collect discussions on a wide range of topics, from
politics to emotional support. Gab claims to be a social platform
Significance
We explore the key differences between the main social media
platforms and how they are likely to influence information
spreading and the formation of echo chambers. To assess the
different dynamics, we perform a comparative analysis on
more than 100 million pieces of content concerning controver-
sial topics (e.g., gun control, vaccination, abortion) from Gab,
Facebook, Reddit, and Twitter. The analysis focuses on two
main dimensions: 1) homophily in the interaction networks
and 2) bias in the information diffusion toward like-minded
peers. Our results show that the aggregation in homophilic
clusters of users dominates online dynamics. However, a direct
comparison of news consumption on Facebook and Reddit
shows higher segregation on Facebook.
Author contributions: M.C., G.D.F.M., A.G., W.Q., and M.S. designed research, performed
research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.y
The authors declare no competing interest.y
This article is a PNAS Direct Submission.y
This open access article is distributed under Creative Commons Attribution License 4.0
(CC BY).y
1To whom correspondence may be addressed. Email: walter.quattrociocchi@uniroma1.it.y
This article contains supporting information online at https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.2023301118/-/DCSupplemental.y
Published February 23, 2021.
PNAS 2021 Vol. 118 No. 9 e2023301118 https://doi.org/10.1073/pnas.2023301118 |1 of 8
aimed at protecting freedom of speech. However, low modera-
tion and regulation on content has resulted in widespread hate
speech. For these reasons, it has been repeatedly suspended by
its service provider, and its mobile app has been banned from
both App and Play stores (36). Overall, we account for the inter-
actions of more than 1 million active users on the four platforms,
for a total of more than 100 million unique pieces of content,
including posts and social interactions. Our analysis shows that
platforms organized around social networks and news feed algo-
rithms, such as Facebook and Twitter, favor the emergence of
echo chambers.
We conclude the paper by directly comparing news consump-
tion on Facebook and Reddit, finding higher segregation on
Facebook than on Reddit.
Characterizing Echo Chambers in Social Media
Operational Definitions. To explore the key differences between
social media platforms and how they influence echo chambers’
formation, we need to operationalize a definition for them. First,
we need to identify the attitude of users at a microlevel. On
online social media, the individual leaning of a user itoward a
specific topic, xi, can be inferred in different ways, via the con-
tent produced or the endorsement network among users (37).
Concerning content, we can define the leaning as the attitude
expressed by a piece of content toward a specific topic. This
leaning can be explicit (e.g., arguments supporting a narrative)
or implicit (e.g., framing and agenda setting). Let us consider a
user iproducing a number aiof contents, Ci={c1,c2,. . . ,cai},
where aiis the activity of user i, and each content leaning is
assigned a numeric value. Then the individual leaning of user
ican be defined as the average of the leanings of produced
contents,
xiPai
j=1 cj
ai
.[1]
Once individual leanings have been inferred, polarization can
be defined as a state of the system such that the distribution of
leanings, P(x), is concentrated in one or more clusters. A pos-
sible example is the case of a single cluster, distinguishable by
a single, extreme peak in P(x). Another example is the typical
case of topics characterized by positive versus negative stances,
in which a bimodal distribution can describe polarization. For
instance, if opinions are assumed to be embedded in a one-
dimensional space (38), x[1, +1] without loss of generality,
as usual for controversial topics, then polarization is charac-
terized by two well-separated peaks in P(x), for positive and
negative opinions. In contrast, neutral ones are absent or under-
represented in the population. Note that polarization can happen
independently from the structure or the very presence of social
interactions. Homophily in social interactions can be quantified
by representing interactions as a social network and then ana-
lyzing its structure concerning the opinions of the users (18, 39,
40). Social networks can be reconstructed in different ways from
online social media, where links represent social relationships
or interactions. Since we are interested in capturing the possi-
ble exchange of opinions between users, we assume links as the
substrate over which information may flow. For instance, if user
ifollows user jon Twitter, user ican see tweets produced by
user j, and there is a flow of information from node jto node i
in the network. When the reconstructed network is directed, we
assume the link direction points to potential influencers (oppo-
site of information flow). Actions such as mentions or retweets
may convey similar flows. In some cases, direct relations between
users are not available in the data, so one needs to assume some
proxy for social connections, for example, a link between two
users if they comment on the same post on Facebook. Crucially,
the two elements characterizing the presence of echo chambers,
polarization and homophilic interactions, should be quantified
independently.
Implementation on Social Media. This section explains how we
implement the operational definitions defined above on differ-
ent social media. For each medium, we detail 1) how we quantify
users’ leaning, and 2) how we reconstruct how the information
spread.
Twitter. We consider the set of tweets posted by user ithat
contain links to news outlets of known political leaning. Each
news outlet is associated with a political leaning score rang-
ing from extreme left to extreme right following the Materials
and Methods classification. We infer the individual leaning of
a user, i,xi[1, +1], by averaging the news organizations’
scores linked by user iaccording to Eq. 1. We analyze three
different datasets collected on Twitter related to controversial
topics: gun control, Obamacare, and abortion. For each dataset,
the social interaction network is reconstructed using the fol-
lowing relation so that there is a direct link from node ito
node jif user ifollows user j(i.e., the source). Henceforth, we
focus on the dataset about abortion, and others are shown in SI
Appendix.
Facebook. We quantify the individual leaning of users consid-
ering endorsements in the form of likes to posts. Posts are
produced by pages that are labeled in a certain number of cat-
egories, and, to each category, we assign a numerical value (e.g.,
Anti-Vax [+1] or Pro-Vax [–1]). Each like to a post (only one
like per post is allowed) represents an endorsement for that con-
tent, which is assumed to be aligned with the leaning associated
with the page. Thus, the user’s leaning is defined as the average
of the content leanings of the posts liked by the user, according
to Eq. 1.
We analyze three different datasets collected on Facebook
regarding a specific topic of discussion: vaccines, science versus
conspiracy, and news. The interaction network is defined by con-
sidering comments. In such an interaction network, two users are
connected if they cocommented on at least one post. Henceforth,
we focus on the dataset about vaccines and news, and others are
shown in SI Appendix.
Reddit. The individual leaning of users is quantified similarly to
Twitter by considering the links to news organizations in the
content produced by the users, submissions, and comments. We
build the interaction network considering comments and submis-
sions. There exists a direct link from node ito node jif user i
comments on a submission or comment by user j(we assume
that ireads the comment they are replying to, which is written
by j).
We analyze three datasets collected on different subreddits:
the donald, Politics, and News. In the following, we focus on the
dataset collected on the Politics and the News subreddits, and
others are shown in SI Appendix.
Gab. The political leaning xiof user iis computed by consider-
ing the set of contents posted by user icontaining a link to news
outlets of a known political leaning, similarly to Twitter and Red-
dit. To obtain the leaning xiof user i, we averaged the scores of
each link posted by user iaccording to Eq. 1. The interaction
network is reconstructed by exploiting the cocommenting rela-
tionships under posts in the same way as for Facebook. Given
two users iand j, an undirected edge between iand jexists if
and only if they comment under the same post.
Comparative Analysis
In the following, we perform a comparative analysis of four dif-
ferent social media. We select one dataset for each social media:
Abortion (Twitter), Vaccines (Facebook), Politics (Reddit), and
Gab as a whole. Results for other datasets for the same medium
are qualitatively similar, as shown in SI Appendix. We first char-
acterize echo chambers in the networks’ topology, and then look
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COMPUTER SCIENCESPSYCHOLOGICAL AND
COGNITIVE SCIENCES
at their effects on information diffusion. Finally, we directly
compare news consumption on Facebook and Reddit.
Polarization and Homophily in the Interaction Networks. The net-
work’s topology can reveal echo chambers, where users are
surrounded by peers with similar leanings, and thus they get
exposed, with a higher probability, to similar contents. In net-
work terms, this translates into a node iwith a given leaning
ximore likely to be connected with nodes with a leaning close
to xi(18). This concept can be quantified by defining, for each
user i, the average leaning of their neighborhood, as xN
i
1
k
iPjAij xj, where Aij is the adjacency matrix of the interac-
tion network, Aij = 1 if there is a link from node ito node j,
Aij = 0 otherwise, and k
i=PjAij is the out-degree of node
i. Fig. 1 shows the correlation between the leaning of a user i
and the leaning of their neighbors, xN
i, for the four social media
under consideration. The probability distributions P(x)(indi-
vidual leaning) and PN(x)(average leaning of neighbors) are
plotted on the xand yaxes, respectively. All plots are color-
coded contour maps, representing the number of users in the
phase space (x,xN): The brighter the area in the plan, the larger
the density of users in that area. The topics of vaccines and
abortion, on Facebook and Twitter, respectively, show a strong
correlation between the leaning of a user and the average leaning
of their nearest neighbors. Similar behavior is found for differ-
ent topics from the same social media platform; see SI Appendix.
Conversely, Reddit and Gab show a different picture. The cor-
responding plots in Fig. 1 display a single bright area, indicating
that users do not split into groups with opposite leaning but form
a single community, biased to the left (Reddit) or the right (Gab).
Similar results are found for different datasets on Reddit; see
SI Appendix.
The presence of homophilic interactions can be confirmed
by the community structure of the interaction networks. We
detected communities by applying the Louvain algorithm (41),
removing singleton communities with only one user. Then, we
computed each community’s average leaning, determined as the
average of individual leanings of its members. Fig. 2 shows
the communities emerging for each social medium, arranged
by increasing average leaning on the xaxis (color-coded from
blue to red), while the yaxis reports the size of the commu-
nity. On Facebook and Twitter, communities span the whole
spectrum of possible leanings, but users with similar leanings
form each community. Some communities are characterized by a
robust average leaning, especially in the case of Facebook. These
results are in accordance with the observation of homophilic
interactions. Instead, communities on Reddit and Gab do not
cover the whole spectrum, and all show similar average lean-
ing. Furthermore, the almost total absence of communities with
leaning very close to 0 confirms the polarized state of the
systems.
Effects on Information Spreading. Simple models of information
spreading can gauge the presence of echo chambers: Users are
expected to be more likely to exchange information with peers
sharing a similar leaning (18, 42, 43). Classical epidemic mod-
els such as the susceptible–infected–recovered (SIR) model (44)
have been used to study the diffusion of information, such as
rumors or news (45–47). In the SIR model, each agent can
Twitter Reddit
Facebook Gab
AB
CD
Fig. 1. Joint distribution of the leaning of users xand the average leaning of their neighborhood xNN for different datasets. (A) Twitter, (B) Reddit, (C)
Facebook, and (D) Gab. Colors represent the density of users: The lighter the color, the larger the number of users. Marginal distribution P(x) and PN(x) are
plotted on the xand yaxes, respectively. Facebook and Twitter present by homophilic clustering.
Cinelli et al.
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https://doi.org/10.1073/pnas.2023301118
Twitter Reddit
Facebook Gab
1
10
100
1000
12345
Community ID
Community Size
Against
Abortion
Pro
Abortion
1
10
100
1000
10000
0 5 10 15 20
Community ID
Community Size
Extreme
Left
Extreme
Right
101
103
105
0204060
Community ID
Community Size
Pro Vaccines
Anti Vaccines
1
10
100
1000
10000
0 5 10 15 20
Community ID
Community Size
Extreme
Left
Extreme
Right
AB
CD
Fig. 2. Size and average leaning of communities detected in different datasets. Aand Cshow the full spectrum of leanings related to the topics of abortions
and vaccines with regard to communities in Band D, where the political leaning is less sparse.
be in any of three states: susceptible (unaware of the circu-
lating information), infectious (aware and willing to spread it
further), or recovered (knowledgeable but not ready to transmit
it anymore). Susceptible (unaware) users may become infectious
(aware) upon contact with infected neighbors, with a specific
transmission probability β. Infectious users can spontaneously
become recovered with probability ν. To measure the effects of
the leaning of users on the diffusion of information, we run the
SIR dynamics on the interaction networks, by starting the epi-
demic process with only one node iinfected, and stopping it
when no more infectious nodes are left.
The set of nodes in a recovered state at the end of the dynam-
ics started with user ias a seed of infection, that is, those that
become aware of the information initially propagated by user i,
form the set of influence of user i,Ii(48). Thus, the set of influ-
ence of a user represents those individuals that can be reached
by a piece of content sent by him/her, depending on the effective
infection ratio β/ν. One can compute the average leaning of the
set of influence of user i,µi, as
µi≡ |Ii|1X
j∈Ii
xj.[2]
The quantity µiindicates how polarized the users are that can be
reached by a message initially propagated by user i(18).
Fig. 3 shows the average leaning hµ(x)iof the influence
sets reached by users with leaning x, for the different datasets
under consideration. The recovery rate νis fixed at 0.2 for
every dataset. In contrast, the ratio between the infection rate
βand average degree hkidepends on the specific dataset and is
reported in the caption of each figure.
Again, one can observe a clear distinction between Facebook
and Twitter, on one side, and Reddit and Gab on the other side.
For the topics of vaccines and abortion, on Facebook and Twit-
ter, respectively, users with a given leaning are much more likely
to be reached by information propagated by users with similar
leaning, that is, hµ(x)i ≈ x. Similar behavior is found for differ-
ent topics from the same social media platform; see SI Appendix.
Conversely, Reddit and Gab show a different behavior: The aver-
age leaning of the set of influence, hµ(x)i, does not depend on
the leaning x. As expected, the average leaning in these media
is not zero. Still, it assumes negative (positive) values in Reddit
(Gab), indicating that the users of this platform are more likely
to receive left (right)-leaning content.
These results indicate that information diffusion is biased
toward individuals who share a similar leaning in some social
media, namely Twitter and Facebook. In contrast, in others—
Reddit and Gab in our analysis—this effect is absent. Such a
latter configuration may depend upon two factors: 1) Gab and
Reddit are not bursting the echo chamber effects, or 2) we are
observing the dynamic inside a single echo chamber.
Our results are robust for different values of the effective
infection ratio β/ν; see SI Appendix. Furthermore, Fig. 3 shows
that the spreading capacity, represented by the average size of
the influence sets (color-coded in Fig. 3), depends on the lean-
ing of the users. On Twitter, proabortion users are more likely
to reach larger audiences. The same is true for antivax users on
Facebook, left-leaning users on Reddit, and right-leaning users
on Gab (in this dataset, left-leaning users are almost absent).
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COGNITIVE SCIENCES
AB
CD
Fig. 3. Average leaning hµ(x)iof the influence sets reached by users with leaning x, for different datasets under consideration. Size and color of each point
represent the average size of the influence sets. The parameters of the SIR dynamics are set to (A)β=0.10hki1, (B)β=0.01hki1, (C)β=0.05hki1, and
(D)β=0.05hki1, while νis fixed at 0.2 for all simulations.
News Consumption on Facebook and Reddit. The striking differ-
ences observed across social media, in terms of homophily in
the interaction networks and information diffusion, could be
attributed to the different topics taken into account. For this
reason, here we compare Facebook and Reddit on a common
topic, news consumption. Facebook and Reddit are particularly
apt to a cross-comparison since they share the definition of indi-
vidual leaning (computed by using the classification provided
by mediabiasfactcheck.org; see Materials and Methods for fur-
ther details) and the rationale in creating connections among
users that is based on an interaction network. Fig. 4 shows a
direct comparison of news consumption on Facebook and Red-
dit along the metrics used in the previous sections to quantify
the presence of echo chambers: 1) the correlation between the
leaning of a user xand the average leaning of neighbors xN
(Fig. 4, Top), 2) the average leaning of communities detected in
the networks (Fig. 4, Middle), and 3) the average leaning hµ(x)i
of the influence sets reached by users with leaning x, by run-
ning SIR dynamics (Fig. 4, Bottom). One can see that all three
measures confirm the picture obtained for other datasets: On
Facebook, we observe a clear separation among users depend-
ing on their leaning, while, on Reddit, users’ leanings are more
homogeneous and show only one peak. In the latter social media,
even users displaying a more extreme leaning (noticeable in the
marginal histogram of Fig. 4 B,Top) tend to interact with the
majority. Moreover, on Facebook, the seed user’s leaning affects
who the final recipients of the information are, therefore indi-
cating the presence of echo chambers. On Reddit, this effect
is absent.
Conclusions
Social media platforms provide direct access to an unprece-
dented amount of content. Platforms originally designed for
user entertainment changed the way information spread. Indeed,
feed algorithms mediate and influence the content promotion
accounting for users’ preferences and attitudes. Such a paradigm
shift affected the construction of social perceptions and the
framing of narratives; it may influence policy making, political
communication, and the evolution of public debate, especially
on polarizing topics. Indeed, users online tend to prefer infor-
mation adhering to their worldviews, ignore dissenting infor-
mation, and form polarized groups around shared narratives.
Furthermore, when polarization is high, misinformation quickly
proliferates.
Some argued that the veracity of the information might be
used as a determinant for information spreading patterns. How-
ever, selective exposure dominates content consumption on
social media, and different platforms may trigger very differ-
ent dynamics. In this paper, we explore the key differences
between the leading social media platforms and how they are
likely to influence the formation of echo chambers and infor-
mation spreading. To assess the different dynamics, we perform
a comparative analysis on more than 100 million pieces of
content concerning controversial topics (e.g., gun control, vac-
cination, abortion) from Gab, Facebook, Reddit, and Twitter.
The analysis focuses on two main dimensions: 1) homophily in
the interaction networks and 2) bias in the information diffusion
toward like-minded peers. Our results show that the aggrega-
tion in homophilic clusters of users dominates online dynamics.
Cinelli et al.
The echo chamber effect on social media
PNAS |5 of 8
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AB
Fig. 4. Direct comparison of news consumption on (A) Facebook and (B) Reddit. Joint distribution of the leaning of users xand the average leaning of
their nearest neighbor xN(Top), size and average leaning of communities detected in the interaction networks (Middle), and average leaning hµ(x)iof the
influence sets reached by users with leaning x, by running SIR dynamics (Bottom) with parameters β=0.05hkifor A,β=0.006hkifor B, and ν=0.2 for
both. Facebook presents a highly segregated structure with regard to Reddit.
However, a direct comparison of news consumption on Face-
book and Reddit shows higher segregation on Facebook. Fur-
thermore, we find significant differences across platforms in
terms of homophilic patterns in the network structure and biases
in the information diffusion toward like-minded users. A clear-
cut distinction emerges between social media having a feed
algorithm tweakable by the users (e.g., Reddit) and social media
that don’t provide such an option (e.g., Facebook and Twitter).
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Table 1. Dataset details
Media Dataset T0T C N nc
Twitter Gun control June 2016 14 d 19 million 3,963 0.93
Obamacare June 2016 7 d 39 million 8,703 0.90
Abortion June 2016 7 d 34 million 7,401 0.95
Facebook Sci/Cons January 2010 5 y 75,172 183,378 1.00
Vaccines January 2010 7 y 94,776 221,758 1.00
News January 2010 6 y 15,540 38,663 1.00
Reddit Politics January 2017 1 y 353,864 240,455 0.15
the donald January 2017 1 y 1.234 million 138,617 0.16
News January 2017 1 y 723,235 179,549 0.20
Gab Gab November 2017 1 y 13 million 165,162 0.13
For each dataset, we report the starting date of collection T0, time span Texpressed in days (d) or years (y),
number of unique contents C, number of users N, and coverage nc(fraction of users with classified leaning). For
Twitter, Trepresents the window to sample active users, of which we retrieve all of the tweets related to the
topic via the Application Programming Interface (API) (more information in SI Appendix). Sci/Cons, Scientific
and Conspiracy content.
Our work provides important insights into the understanding of
social dynamics and information consumption on social media.
The next envisioned step addresses the temporal dimension of
echo chambers, to understand better how different social feed-
back mechanisms, specific to distinct platforms, can impact their
formation.
Materials and Methods
Here we provide details about the labeling of news outlets and the datasets
considered.
Labeling of Media Sources. The labeling of news outlets is based on
the information reported by Media Bias/Fact Check (MBFC) (https://
mediabiasfactcheck.com), an independent fact-checking organization that
rates news outlets on the basis of the reliability and of the political bias
of the contents they produce and share. The labeling provided by MBFC,
retrieved in June 2019, ranges from Extreme Left to Extreme Right for polit-
ical bias. The total number of media outlets for which we have a political
label is 2,190. A detailed description of the source labeling process and
political bias distribution can be found in SI Appendix.
Data Availability. For what concerns Gab, all data are available on the
Pushshift public repository (https://pushshift.io/what-is-pushshift-io/) at this
link: https://files.pushshift.io/gab/. Reddit data are available on the Pushshift
public repository at this link: https://search.pushshift.io/reddit/. For what
concerns Facebook and Twitter, we provide data according to their Terms
of Services on the corresponding author institutional page at this link:
https://walterquattrociocchi.site.uniroma1.it/ricerca. For news outlet classifi-
cation, we used data from MBFC (https://mediabiasfactcheck.com), an inde-
pendent fact-checking organization. Anonymized data have been deposited
in Open Science Framework (10.17605/OSF.IO/X92BR) (49). For further details
about data, refer to the following section.
Empirical Datasets. Table 1 reports summary statistics of the datasets under
consideration. Due to the structural differences among platforms, each
dataset has different features. For Twitter, we used tweets regarding three
topics collected by Garimella et al. (16), namely Gun control, Obamacare,
and abortion. Tweets linking to a news source with a known bias are clas-
sified based on MBFC. Facebook datasets were created by using Facebook
Graph API and were previously explored in ref. 50 (Science and Conspiracy),
(51) (Vaccines) and (11) (News). For the two datasets Science and Conspiracy
and Vaccines, data were labeled in a binary way, respectively, provac-
cines/antivaccines and proscience/conspiracy, based on the page where they
were posted. Posts in the dataset News were instead classified based on
MBFC labeling. Reddit datasets have been obtained by downloading com-
ments and submissions posted in the subreddit Politics, the donald, and
News and labeled according to the classification obtained from MBFC. The
Gab dataset has been collected from https://files.pushshift.io/gab and con-
tains posts, replies, and quotations. Posts were labeled according to MBFC
classification. Further details can be found in SI Appendix.
ACKNOWLEDGMENTS. We thank Fabiana Zollo and Antonio Scala for pre-
cious insights for the development of this paper. We are grateful to
Geronimo Stilton and the Hypnotoad for inspiring the data analysis and
result interpretation.
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Cinelli et al.
The echo chamber effect on social media
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Social Media and Democracy - edited by Nathaniel Persily September 2020
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