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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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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
( 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
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:
This article contains supporting information online at
Published February 23, 2021.
PNAS 2021 Vol. 118 No. 9 e2023301118 |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
j=1 cj
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
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
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
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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The echo chamber effect on social media
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
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
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
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.
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Twitter Reddit
Facebook Gab
Community ID
Community Size
0 5 10 15 20
Community ID
Community Size
Community ID
Community Size
Pro Vaccines
Anti Vaccines
0 5 10 15 20
Community ID
Community Size
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
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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The echo chamber effect on social media
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; 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.
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
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.
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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
Materials and Methods
Here we provide details about the labeling of news outlets and the datasets
Labeling of Media Sources. The labeling of news outlets is based on
the information reported by Media Bias/Fact Check (MBFC) (https://, 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 ( at this
link: Reddit data are available on the Pushshift
public repository at this link: For what
concerns Facebook and Twitter, we provide data according to their Terms
of Services on the corresponding author institutional page at this link: For news outlet classifi-
cation, we used data from MBFC (, 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 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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... Furthermore, we did not assess the potential presence of social media bots (automated accounts) spreading incorrect information in these studies. We also did not discuss how social media algorithms partake in creating echo chambers [78]. These are well-known challenges in researching data gathered from social media [79]. ...
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Background: The development of COVID-19 vaccines has been crucial in fighting the pandemic. However, misinformation about the COVID-19 pandemic and vaccines is spread on social media platforms at a rate that has made the World Health Organization coin the phrase infodemic. False claims about adverse vaccine side effects, such as vaccines being the cause of autism, were already considered a threat to global health before the outbreak of COVID-19.
... In the literature, groups formed around a shared narrative are frequently called echo chambers. As defined by Cinelli et al. [23], echo chambers are environments in which the opinions or beliefs of people about some topic are reinforced due to repeated interactions with peers or sources having similar tendencies and attitudes. The terms group [28], community [98], gated community [123], filter bubble [115], and echo chamber [23], all reflect a very similar concept in polarization-related publications. ...
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Polarization arises when the underlying network connecting the members of a society is formed by highly connected groups with weak intergroup connectivity. The increasing polarization, the strengthening of echo chambers, and the isolation caused by information filters in social networks are increasingly attracting the attention of researchers from different areas of knowledge such as computer science, economics, social and political sciences. Despite hundreds of publications in this area, there was little effort to systematize or present the knowledge developed in the field in an organized way. This study presents an annotated review of network polarization measures, models used to handle existing polarization, their applications, and case studies. Altogether, 405 scientific articles and conference papers were examined, with 74 filtered for this review. Several approaches for measuring polarization in graphs and networks were identified, including those based on homophily, modularity, random walks, and balance theory. The models used for reducing polarization included methods that propose edge or node editions (including edge insertions or deletions, and edge weight modifications), changes in social network design, or changes in the recommendation systems embedded in these networks. This review will be helpful to researchers investigating polarized social networks from a theoretical and applied perspective.
... The echo chamber effect is well documented in the literature: Users tend to follow those who hold ideas similar to theirs, and are thus rarely exposed to opposing views. This, in turn, leads the users' beliefs to become self-reinforcing, thereby resulting in further polarization [41,42]. Our analysis showcases the pervasiveness of this phenomenon on the platform. ...
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The COVID-19 pandemic brought upon a massive wave of disinformation, exacerbating polarization in the increasingly divided landscape of online discourse. In this context, popular social media users play a major role, as they have the ability to broadcast messages to large audiences, thus influencing public opinion. We make use of publicly available Twitter data to study the behavior of influential users discussing the pandemic, whom we term COVID-19 Twitter elites. We tackle the issue from a network perspective, by considering users as nodes and following relationships as directed edges. The resulting network structure is modeled by embedding the actors in a latent social space, where users closer to one another have a higher probability of forming edges. The results suggest the existence of two distinct communities, which can be interpreted as "generally pro" and "generally against" vaccine mandates. We further focus on a number of exposed users, such as politicians, activists, and news outlets, and discuss their roles in the latent space. Our findings show that the full spectrum of beliefs between the two poles is represented, with more radical users positioned towards the extremes of the space, and more moderate actors in the middle. Our analysis demonstrates how it is possible to provide a nuanced representation of the COVID-19 Twitter ecosystem by only considering follows within the network of elites. This finding corroborates existing evidence on the pervasiveness of echo chamber effects on the platform, and showcases the power of latent space models for studying communication on social media.
The COVID pandemic has sparked fear among many people worldwide and has thus led to the emergence of a variety of conspiracy theories. Individuals believing in these theories come from various social and demographic backgrounds, some of them being mere skeptics, while others are more radical and extreme. The present paper investigates the use of language in a conspiratorial anti-COVID Facebook group with the aim of describing the linguistic features and strategies employed to share and spread conspiracy theories and to form a common identity. The results show that the group uses features typical of extremist groups, such the ideological in-and out-group presentation, the use of agentless passives to create fear, and intertextual references. Further strategies detected in the data are the misrepresentation of scientific knowledge, the use of colloquial language, humor, and irony, among others, which are used to create a bond among the groups' members and to set themselves apart from the government and the rest of the population, whom they perceive as enemies and oppressors.
In this paper, we approach the phenomenon of criminal activity from an infectious perspective by using tailored compartmental agent-based models that include the social flavor of the mechanisms governing the evolution of crime in society. Specifically, we focus on addressing how the existence of competing gangs shapes the penetration of crime. The mean-field analysis of the model proves that the introduction of dynamical rules favoring the simultaneous survival of both gangs reduces the overall number of criminals across the population as a result of the competition between them. The implementation of the model in networked populations with homogeneous contact patterns reveals that the evolution of crime substantially differs from that predicted by the mean-field equations. We prove that the system evolves toward a segregated configuration where, depending on the features of the underlying network, both gangs can form spatially separated clusters. In this scenario, we show that the beneficial effect of the coexistence of two gangs is hindered, resulting in a higher penetration of crime in the population.
Resumen El artículo tiene como objetivo conocer el rol que cumplen los principales grupos políticos en España, en la promoción de contenidos desinformativos en Twitter. El estudio aplica análisis estadísticos y de tópico a la total de tuits publicados en español (n = 40.445 tuits), entre septiembre (2019) y febrero (2020), por las cuentas oficiales de los partidos, líderes y portavoces de cada uno de los principales grupos políticos en España (PSOE, Partido Popular, Unidas Podemos, Vox y Ciudadanos); y los contenidos desinformativos identificados, entre agosto 2019 y marzo de 2020, por dos de los principales proyectos periodísticos de Fact-Checking (n = 2.730 contenidos desinformativos) en España ( y Los datos permiten ver, cómo los grupos políticos analizados presentan un nivel alto de coocurrencia con los contenidos desinformativos identificados por y Lo que estaría confirmando el papel activo de estos actores en la expansión de este tipo de contenidos en Twitter.
Yalan haber olgusu, çağımızın temel problemleri arasında yer almaktadır. Yeni medyanın yaygınlaşması ve yükselişiyle birlikte yalan haber, geleneksel medyanın tekelinden çıkıp kitleselleşmektedir. Enformasyona erişimin kolaylaşması olumlu anlamda bir gelişme olarak değerlendirilirken kitlelerin yalan haberlere maruz kalması demokrasi gibi kurumlar için tehdit olarak algılanmaktadır. Gazeteciler açısından haberin teyit edilmesi geleneksel gazeteciliğin kodları arasında yer alırken, yeni medyada dijital tekniklerin artması gazetecilerin bu alanda yeni rehberler edinmesini gerektirmiştir. Böylelikle gazeteciler için birçok haber doğrulama kitapçıkları üretilmiştir. Öte yandan yalan haber sayısında yaşanan artış “bilgi doğrulayıcıları” veya “haber doğrulayıcıları” olarak yeni bir profesyonelliğin ortaya çıkmasını sağlamıştır. Teknolojikleştirilmiş, rasyonelleştirilmiş ve profesyonelleştirilmiş haber doğrulayıcılığı, yalan haber çağında gazeteciliğe yeni bir işlev kazandırmak amacıyla gelişmektedir. Bu çalışmada, bilgi doğrulayıcılığının hangi düzeylerde geleneksel gazetecilik kimliği sınırları içerisinde yer aldığı sorunsallaştırılmaktadır. Eş deyişle, çalışmada haber doğrulayıcılarının profesyonel kimliklerinin oluşumu incelenmektedir. Aynı zamanda, çalışmada haber doğrulayıcılarının karşılaştığı güçlüklerin profesyonel kimlik üzerindeki etkileri ele alınmıştır. Çalışmada nitel araştırma desenlerinden olgu bilim benimsenmiştir. Bu kapsamda doğrulama platformunda çalışan üç bilgi doğrulayıcı ile görüşülmüştür. Elde edilen bulgulara göre, bilgi doğrulayıcılığı kimliği gerçeklerin aktarılması, objektiflik ve tarafsızlık konularında gazetecilikle benzerlik göstermektedir. Öte yandan bilgi doğrulayıcılığı kimliği, dijital beceriler, çalışma pratikleri ve hız başlıklarında gazetecilikten farklılaşmaktadır.
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Numerous polls suggest that COVID-19 is a profoundly partisan issue in the United States. Using the geotracking data of 15 million smartphones per day, we found that US counties that voted for Donald Trump (Republican) over Hillary Clinton (Democrat) in the 2016 presidential election exhibited 14% less physical distancing between March and May 2020. Partisanship was more strongly associated with physical distancing than numerous other factors, including counties’ COVID-19 cases, population density, median income, and racial and age demographics. Contrary to our predictions, the observed partisan gap strengthened over time and remained when stay-at-home orders were active. Additionally, county-level consumption of conservative media (Fox News) was related to reduced physical distancing. Finally, the observed partisan differences in distancing were associated with subsequently higher COVID-19 infection and fatality growth rates in pro-Trump counties. Taken together, these data suggest that US citizens’ responses to COVID-19 are subject to a deep—and consequential—partisan divide.
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The social brain hypothesis approximates the total number of social relationships we are able to maintain at 150. Similar cognitive constraints emerge in several aspects of our daily life, from our mobility to the way we communicate, and might even affect the way we consume information online. Indeed, despite the unprecedented amount of information we can access online, our attention span still remains limited. Furthermore, recent studies have shown that online users are more likely to ignore dissenting information, choosing instead to interact with information adhering to their own point of view. In this paper, we quantitatively analyse users’ attention economy in news consumption on social media by analysing 14 million users interacting with 583 news outlets (pages) on Facebook over a time span of six years. In particular, we explore how users distribute their activity across news pages and topics. On the one hand, we find that, independently of their activity, users show a tendency to follow a very limited number of pages. On the other hand, users tend to interact with almost all the topics presented by their favoured pages. Finally, we introduce a taxonomy accounting for users’ behaviour to distinguish between patterns of selective exposure and interest. Our findings suggest that segregation of users in echo chambers might be an emerging effect of users’ activity on social media and that selective exposure—i.e. the tendency of users to consume information adhering to their preferred narratives—could be a major driver in their consumption patterns.
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We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.
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Immense amounts of information are now accessible to people, including information that bears on their past, present and future. An important research challenge is to determine how people decide to seek or avoid information. Here we propose a framework of information-seeking that aims to integrate the diverse motives that drive information-seeking and its avoidance. Our framework rests on the idea that information can alter people’s action, affect and cognition in both positive and negative ways. The suggestion is that people assess these influences and integrate them into a calculation of the value of information that leads to information-seeking or avoidance. The theory offers a framework for characterizing and quantifying individual differences in information-seeking, which we hypothesize may also be diagnostic of mental health. We consider biases that can lead to both insufficient and excessive information-seeking. We also discuss how the framework can help government agencies to assess the welfare effects of mandatory information disclosure. Sharot and Sunstein propose a framework of information-seeking, whereby individuals decide to seek or avoid information based on combined estimates of the potential impact of information on their action, affect and cognition.
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Abstract Echo chambers in online social networks, in which users prefer to interact only with ideologically-aligned peers, are believed to facilitate misinformation spreading and contribute to radicalize political discourse. In this paper, we gauge the effects of echo chambers in information spreading phenomena over political communication networks. Mining 12 million Twitter messages, we reconstruct a network in which users interchange opinions related to the impeachment of the former Brazilian President Dilma Rousseff. We define a continuous political leaning parameter, independent of the network’s structure, that allows to quantify the presence of echo chambers in the strongly connected component of the network. These are reflected in two well-separated communities of similar sizes with opposite views of the impeachment process. By means of simple spreading models, we show that the capability of users in propagating the content they produce, measured by the associated spreading capacity, strongly depends on their attitude. Users expressing pro-impeachment leanings are capable to transmit information, on average, to a larger audience than users expressing anti-impeachment leanings. Furthermore, the users’ spreading capacity is correlated to the diversity, in terms of political position, of the audience reached. Our method can be exploited to identify the presence of echo chambers and their effects across different contexts and shed light upon the mechanisms allowing to break echo chambers.
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The World Economic Forum listed massive digital misinformation as one of the main threats for our society. The spreading of unsubstantiated rumors may have serious consequences on public opinion such as in the case of rumors about Ebola causing disruption to health-care workers. In this work we target Facebook to characterize information consumption patterns of 1.2 M Italian users with respect to verified (science news) and unverified (conspiracy news) contents. Through a thorough quantitative analysis we provide important insights about the anatomy of the system across which misinformation might spread. In particular, we show that users’ engagement on verified (or unverified) content correlates with the number of friends having similar consumption patterns (homophily). Finally, we measure how this social system responded to the injection of 4,709 false information. We find that the frequent (and selective) exposure to specific kind of content (polarization) is a good proxy for the detection of homophile clusters where certain kind of rumors are more likely to spread.
Social Media and Democracy - edited by Nathaniel Persily September 2020
This paper investigates online propaganda strategies of the Internet Research Agency (IRA)—Russian “trolls”—during the 2016 U.S. presidential election. We assess claims that the IRA sought either to (1) support Donald Trump or (2) sow discord among the U.S. public by analyzing hyperlinks contained in 108,781 IRA tweets. Our results show that although IRA accounts promoted links to both sides of the ideological spectrum, “conservative” trolls were more active than “liberal” ones. The IRA also shared content across social media platforms, particularly YouTube—the second-most linked destination among IRA tweets. Although overall news content shared by trolls leaned moderate to conservative, we find troll accounts on both sides of the ideological spectrum, and these accounts maintain their political alignment. Links to YouTube videos were decidedly conservative, however. While mixed, this evidence is consistent with the IRA’s supporting the Republican campaign, but the IRA’s strategy was multifaceted, with an ideological division of labor among accounts. We contextualize these results as consistent with a pre-propaganda strategy. This work demonstrates the need to view political communication in the context of the broader media ecology, as governments exploit the interconnected information ecosystem to pursue covert propaganda strategies.
Echo chambers and opinion polarization recently quantified in several sociopolitical contexts and across different social media raise concerns on their potential impact on the spread of misinformation and on the openness of debates. Despite increasing efforts, the dynamics leading to the emergence of these phenomena remain unclear. We propose a model that introduces the dynamics of radicalization as a reinforcing mechanism driving the evolution to extreme opinions from moderate initial conditions. Inspired by empirical findings on social interaction dynamics, we consider agents characterized by heterogeneous activities and homophily. We show that the transition between a global consensus and emerging radicalized states is mostly governed by social influence and by the controversialness of the topic discussed. Compared with empirical data of polarized debates on Twitter, the model qualitatively reproduces the observed relation between users’ engagement and opinions, as well as opinion segregation in the interaction network. Our findings shed light on the mechanisms that may lie at the core of the emergence of echo chambers and polarization in social media.
Users’ polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this article, we introduce a framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users’ behavior on online social media such as Facebook, making a first, important step towards the mitigation of misinformation phenomena by supporting the identification of potential misinformation targets and thus the design of tailored counter-narratives.