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The COVID-19 social media infodemic


Abstract and Figures

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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Scientific RepoRtS | (2020) 10:16598 | 
The COVID‑19 social media
Matteo Cinelli1,2, Walter Quattrociocchi1,2,3*, Alessandro Galeazzi4, Carlo Michele Valensise5,
Emanuele Brugnoli1, Ana Lucia Schmidt2, Paola Zola6, Fabiana Zollo1,2,7 & Antonio Scala1,3
We address the diusion 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 dierential assessment on the evolution of the discourse on a global scale for each
platform and their users. We t information spreading with epidemic models characterizing the basic
reproduction number
for each social media platform. Moreover, we identify information spreading
from questionable sources, nding dierent volumes of misinformation in each platform. However,
information from both reliable and questionable sources do not present dierent spreading patterns.
Finally, we provide platform‑dependent numerical estimates of rumors’ amplication.
e World Health Organization (WHO) dened the SARS-CoV-2 virus outbreak as a severe global threat1. As
foreseen in 2017 by the global risk report of the World Economic forum, global risks are interconnected. In
particular, the case of the COVID-19 epidemic (the infectious disease caused by the most recently discovered
human coronavirus) is showing the critical role of information diusion in a disintermediated news cycle2.
e term infodemic3,4 has been coined to outline the perils of misinformation phenomena during the man-
agement of disease outbreaks57, since it could even speed up the epidemic process by inuencing and frag-
menting social response8. As an example, CNN has recently anticipated a rumor about the possible lock-down
of Lombardy (a region in northern Italy) to prevent pandemics9, publishing the news hours before the ocial
communication from the Italian Prime Minister. As a result, people overcrowded trains and airports to escape
from Lombardy toward the southern regions before the lock-down was put in place, disrupting the government
initiative aimed to contain the epidemics and potentially increasing contagion. us, an important research
challenge is to determine how people seek or avoid information and how those decisions aect their behavior10,
particularly when the news cycle—dominated by the disintermediated diusion of information—alters the way
information is consumed and reported on.
e case of the COVID-19 epidemic shows the critical impact of this new information environment. e
information spreading can strongly inuence people’s behavior and alter the eectiveness of the countermeas-
ures deployed by governments. To this respect, models to forecast virus spreading are starting to account for
the behavioral response of the population with respect to public health interventions and the communication
dynamics behind content consumption8,11,12.
Social media platforms such as YouTube and Twitter provide direct access to an unprecedented amount of
content and may amplify rumors and questionable information. Taking into account users’ preferences and atti-
tudes, algorithms mediate and facilitate content promotion and thus information spreading13. is shi from the
traditional news paradigm profoundly impacts the construction of social perceptions14 and the framing of narra-
tives; it inuences policy-making, political communication, as well as the evolution of public debate15,16, especially
when issues are controversial17. Users online tend to acquire information adhering to their worldviews18,19, to
ignore dissenting information20,21 and to form polarized groups around shared narratives22,23. Furthermore, when
polarization is high, misinformation might easily proliferate24,25. Some studies pointed out that fake news and
inaccurate information may spread faster and wider than fact-based news26. However, this might be platform-
specic eect. e denition of “Fake News” may indeed be inadequate since political debate oen resorts
to labelling opposite news as unreliable or fake27. Studying the eect of the social media environment on the
perception of polarizing topics is being addressed also in the case of COVID-19. e issues related to the cur-
rent infodemics are indeed being tackled by the scientic literature from multiple perspectives including the
               
    
 *
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dynamics of hatespeech and conspiracy theories28,29, the eect of bots and automated accounts30, and the threats
of misinformation in terms of diusion and opinions formation31,32.
In this work we provide an in-depth analysis of the social dynamics in a time window where narratives and
moods in social media related to the COVID-19 have emerged and spread. While most of the studies on misin-
formation diusion focus on a single platform17,26,33, the dynamics behind information consumption might be
particular to the environment in which they spread on. Consequently, in this paper we perform a comparative
analysis on ve social media platforms (Twitter, Instagram, YouTube, Reddit and Gab) during the COVID-19
outbreak. e dataset includes more than 8 million comments and posts over a time span of 45 days. We analyze
user engagement and interest about the COVID-19 topic, providing an assessment of the discourse evolution
over time on a global scale for each platform. Furthermore, we model the spread of information with epidemic
models, characterizing for each platform its basic reproduction number (
), i.e. the average number of second-
ary cases (users that start posting about COVID-19) an “infectious” individual (an individual already posting
on COVID-19) will create. In epidemiology,
=1 is a threshold parameter. When
the disease will die
out in a nite period of time, while the disease will spread for
. In social media,
will indicate the
possibility of an infodemic.
Finally, coherently with the classication provided by the fact-checking organization Media Bias/Fact Check34
that classies news sources based on the truthfulness and bias of the information published, we split news outlets
into two groups. ese groups are either associated to the diusion of (mostly) reliable or (mostly) questionable
contents and we characterize the spreading of information regarding COVID-19 relying on this classication.
We nd that users in mainstream platforms are less susceptible to the diusion of information from question-
able sources and that information deriving from news outlets marked either as reliable or questionable do not
present signicant dierence in the way it spreads.
Our ndings suggest that the interaction patterns of each social media combined with the peculiarity of the
audience of each platform play a pivotal role in information and misinformation spreading. We conclude the
paper by measuring rumor’s amplication parameters for COVID-19 on each social media platform.
We analyze mainstream platforms such as Twitter, Instagram and YouTube as well as less regulated social media
platforms such as Gab and Reddit. Gab is a crowdfunded social media whose structure and features are Twitter-
inspired. It performs very little control on content posted; in the political spectrum, its user base is considered
to be far-right. Reddit is an American social news aggregation, web content rating, and discussion website based
on collective ltering of information.
We perform a comparative analysis of information spreading dynamics around the same argument in dier-
ent environments having dierent interaction settings and audiences. We collect all pieces of content related to
COVID-19 from the 1st of January to the 14th of February. Data have been collected ltering contents accord-
ingly to a selected sample of Google Trends’ COVID-19 related queries such as: coronavirus, coronavirusout-
break, imnotavirus, ncov, ncov-19, pandemic, wuhan. e deriving dataset is then composed of 1,342,103 posts
and 7,465,721 comments produced by 3,734,815 users. For more details regarding the data collection refer to
Interaction patterns. First, we analyze the interactions (i.e., the engagement) that users have with COVID-
19 topics on each platform. e upper panel of Fig.1 shows users’ engagement around the COVID-19 topic.
Despite the dierences among platforms, we observe that they all display a rather similar distribution of the
users’ activity characterized by a long tail. is entails that users behave similarly for what concern the dynamics
of reactions and content consumption. Indeed, users’ interactions with the COVID-19 content present attention
patterns similar to any other topic35. e highest volume of interactions in terms of posting and commenting can
be observed on mainstream platforms such as YouTube and Twitter.
en, to provide an overview of the debate concerning the disease outbreak, we extract and analyze the topics
related to the COVID-19 content by means of Natural Language Processing techniques. We build word embed-
ding for the text corpus of each platform, i.e. a word vector representation in which words sharing common
contexts are in close proximity. Moreover, by running clustering procedures on these vector representations, we
separate groups of words and topics that are perceived as more relevant for the COVID-19 debate. For further
details refer to Methods. e results (Fig.1, middle panel) show that topics are quite similar across each social
media platform. Debates range from comparisons to other viruses, requests for God blessing, up to racism, while
the largest volume of interaction is related to the lock-down of ights.
Finally, to characterize user engagement with the COVID-19 on the ve platforms, we compute the cumulative
number of new posts each day (Fig.1, lower panel). For all platforms, we nd a change of behavior around the
20th of January, that is the day that the World Health Organization (WHO) issued its rst situation report on the
COVID-1936. e largest increase in the number of posts is on the 21st of January for Gab, the 24th January for
Reddit, the 30th January for Twitter, the 31th January for YouTube and the 5th of February for Instagram. us,
social media platforms seem to have specic timings for content consumption; such patterns may depend upon
the dierence in terms of audience and interaction mechanisms (both social and algorithmic) among platforms.
Information spreading. Eorts to simulate the spreading of information on social media by reproducing
real data have mostly applied variants of standard epidemic models3740. Coherently, we analyze the observed
monotonic increasing trend in the way new users interact with information related to the COVID-19 by using
epidemic models. Unlike previous works, we do not only focus on models that imply specic growth mecha-
nisms, but also on phenomenological models that emphasize the reproducibility of empirical data41.
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Most of the epidemiological models focus on the basic reproduction number
, representing the expected
number of new infectors directly generated by an infected individual for a given time period42. An epidemic
occurs if
,—i.e., if an exponential growth in the number of infections is expected at least in the initial
phase. In our case, we try to model the growth in number of people publishing a post on a subject as an infec-
tive process, where people can start publishing aer being exposed to the topic. While in real epidemics
highlights the possibility of a pandemic, in our approach
indicates the emergence of an infodemic. We
model the dynamics both with the phenomenological model of43 (from now on referred to as the EXP model)
and with the standard SIR (Susceptible, Infected, Recovered) compartmental model44. Further details on the
modeling approach can be found in Methods.
As shown in Fig.2, each platform has its own basic reproduction number
. As expected, all the values of
are supercritical—even considering condence intervals (Table1)—signaling the possibility of an infodemic. is
observation may facilitate the prediction task of information spreading during critical events. Indeed, according
to this result we can consider information spreading patterns on each social media to predict social response
when implementing crisis management plans.
is a good proxy for the engagement rate and a good predictor for epidemic-like information spread-
ing, social contagion phenomena might be in general more complex4547. For instance, in the case of Instagram,
we observe an abrupt jump in the number of new users that cannot be explained with continuous models like
the standard epidemic ones; accordingly, the SIR model estimates a value of
that is way beyond what
has been observed in any real-world epidemic.
Figure1. Upper panel: activity (likes, comments, reposts, etc.) distribution for each social media. Middle panel:
most discussed topics about COVID-19 on each social media. Lower panel: cumulative number of content
(posts, tweets, videos, etc.) produced from the 1st of January to the 14th of February. Due to the Twitter API
limitations in gathering past data, the rst data point for Twitter is dated January 27th.
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Questionable VS reliable information sources. We conclude our analysis by comparing the diusion
of information from questionable and reliable sources on each platform. We tag links as reliable or question-
able according to the data reported by the independent fact-checking organization Media Bias/Fact Check34. In
order to clarify the limits of an approach that is based on labelling news outlets rather than single articles, as for
instance performed in33,48, we report the denitions used in this paper for questionable and reliable information
sources. In accordance with the criteria established by MBFC, by questionable information source we mean a
news outlet systematically showing one or more of the following characteristics: extreme bias, consistent promo-
tion of propaganda/conspiracies, poor or no sourcing to credible information, information not supported by
evidence or unveriable, a complete lack of transparency and/or fake news. By reliable information sources we
mean news outlets that do not show any of the aforementioned characteristics. Such outlets can anyway produce
contents potentially displaying a bias towards liberal/conservative opinion, but this does not compromise the
overall reliability of the source.
Figure3 shows, for each platform, the plots of the cumulative number of posts and reactions related to reliable
sources versus the cumulative number of posts and interactions referring to questionable sources. By interactions
we mean the overall reactions, e.g. likes or other form or endorsement and comments, that can be performed
with respect to a post on a social platform. Surprisingly, all the posts show a strong linear correlation, i.e., the
number of posts/reactions relying on questionable and reliable sources grows with the same pace inside the same
social media platform. We observe the same phenomenon also for the engagement with reliable and questionable
sources. Hence, the growth dynamics of posts/interactions related to questionable news outlets is just a re-scaled
version of the growth dynamics of posts/reactions related to reliable news outlets; however, the re-scaling factor
(i.e., the fraction of questionable over reliable) is strongly dependent on the platform.
In particular, we observe that in mainstream social media the number of posts produced by questionable
sources represents a small fraction of posts produced by reliable ones; the same thing happens in Reddit. Among
less regulated social media, a peculiar eect is observed in Gab: while the volume of posts from questionable
sources is just the
of the volume of posts from reliable ones, the volume of reactions for the former ones
times bigger than the volume for the latter ones. Such results hint the possibility that dierent platform
react dierently to information produced by reliable and questionable news outlets.
To further investigate this issue, we dene the amplication factor
as the average number of reactions to a
post; hence,
is a measure that quanties the extent to which a post is amplied in a social media. We observe
that the amplication
(for unreliable posts posts produced by questionable outlets) and
(for reliable posts
Figure2. Growth of the number of authors versus time. Time is expressed in number of days since 1st January
2020 (day 1). Shaded areas represents [5%, 95%] estimates of the models obtained via bootstrapping least square
estimates of the EXP model (upper panels) and of the SIR model (lower panels). For details the SIR and the EXP
model, see SI.
Table 1. [5%, 95%] interval of condence
as estimated from bootstrapping the least square ts parameter
of the EXP and of the SIR model. Notice that, due to the steepness of the growth of the number of new authors
in Instagram,
assumes unrealistic values
for the SIR model.
Gab Reddit YouTu b e Instagram Twitter
[1.42, 1.52] [1.44, 1.51] [1.56, 1.70] [2.02, 2.64] [1.65, 2.06]
[2.2, 2.5] [2.4, 2.8] [3.2, 3.5]
[1.1 ×102, 1.6 ×102]
[4.0, 5.1]
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posts produced by reliable outlets) vary from social media platform to social media platform and that assumes
the largest values in YouTube and the lowest in Gab. To measure the permeability of a platform to posts from
questionable/reliable news outlets, we then dene the coecient of relative amplication
. It is a
measure of whether a social media amplies questionable (
) or reliable (
) posts. Results are shown in
Table2. Among mainstream social media, we notice that Twitter is the most neutral (
), w hile
YouTube amplies questionable sources less (
). Among less popular social media, Reddit reduces the
impact of questionable sources (
), while Gab strongly amplies them (
erefore, we conclude that the main drivers of information spreading are related to specic peculiarities of
each platform and depends upon the group dynamics of individuals engaged with the topic.
Figure3. Upper panels: plot of the cumulative number of posts referring to questionable sources versus
the cumulative number of posts referring to reliable sources. Lower panel: plot of the cumulative number
of engagements relatives to questionable sources versus the cumulative number of engagements relative
to reliable sources. Notice that a linear behavior indicates that the time evolution of questionable posts/
engagements is just a re-scaled version of the time evolution of reliable posts/engagements. Each plot indicates
the regression coecients
, representing the ratio among the volumes of questionable and reliable posts (
and engagements (
). In more popular social media, the number of questionable posts represents a small
fraction of the reliable ones; same thing happens in Reddit. Among less popular social media, a peculiar eect
is observed in Gab: while the volume of questionable posts is just the
of the volume of reliable ones, the
volume of engagements for questionable posts is
times bigger than the volume for reliable ones. Further
details concerning the regression coecients are reported in Methods.
Table 2. e average engagement of a post is the number of reactions expected for a post and is a measure of
how much a post is amplied in each social media platform. e average engagement
(for unreliable post)
(for reliable post) vary from platform to platform, and are the largest in Twitter and the lowest in Gab.
e coecient of relative amplication
measures whether a social media amplies more unreliable
) or reliable (
) posts. Among more popular social media platforms, we notice that Twitter is the
most neutral (
), while YouTube amplies unreliable sources less (
). Among less
popular social media platforms, Reddit reduces the impact of unreliable sources (
) while Gab strongly
amplies them (
Gab 5.6 1.4 3.9
Reddit 22.7 40.1 0.55
Twitter 15.1 15.6 0.97
YouTub e
1.4 ×104
3.9 ×104
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In this work we perform a comparative analysis of users’ activity on ve dierent social media platforms during
the COVID-19 health emergency. Such a timeframe is a good benchmark for studying content consumption
dynamics around critical events in a times when the accuracy of information is threatened. We assess user
engagement and interest about the COVID-19 topic and characterize the evolution of the discourse over time.
Furthermore, we model the spread of information using epidemic models and provide basic growth param-
eters for each social media platform. We then analyze the diusion of questionable information for all channels,
nding that Gab is the environment more susceptible to misinformation dissemination. However, information
deriving from sources marked either as reliable or questionable do not present signicant dierences in their
its spreading patterns. Our analysis suggests that information spreading is driven by the interaction paradigm
imposed by the specic social media or/and by the specic interaction patterns of groups of users engaged with
the topic. We conclude the paper by computing rumor’s amplication parameters for social media platforms.
We believe that the understanding of social dynamics between content consumption and social media plat-
forms is an important research subject, since it may help to design more ecient epidemic models accounting
for social behavior and to design more eective and tailored communication strategies in time of crisis.
Data collection. Table3 reports the data breakdown of the ve social media platforms. Dierent data col-
lection processes have been performed depending on the platform. In all cases we guided the data collection by
a set of selected keywords based on Google Trends’ COVID-19 related queries such as: coronavirus, pandemic,
coronaoutbreak, china, wuhan, nCoV, IamNotAVirus, coronavirus_update, coronavirus_transmission, corona-
virusnews, coronavirusoutbreak.
e Reddit dataset was downloaded from the Pushi .io archive, exploiting the related API. In order to lter
contents linked to COVID-19, we used our set of keywords.
In Gab, although no ocial guides are available, there is an API service that given a certain keyword, returns
a list of users, hashtags and groups related to it. We queried all the keywords we selected based on Google
Trends and we downloaded all hashtags linked to them. We then manually browsed the results and selected a
set of hashtags based on their meaning. For each hashtag in our list, we downloaded all the posts and comments
linked to it.
For YouTube, we collected videos by using the YouTube Data API by searching for videos that matched our
keywords. en an in depth search was done by crawling the network of videos by searching for more related
videos as established by the YouTube algorithm. From the gathered set, we ltered the videos that matched
coronavirus, nCov, corona virus, corona-virus, corvid, covid or SARS-CoV in the title or description. We then
collected all the comments received by those videos.
For Twitter, we collect tweets related to the topic coronavirus by using both the search and stream endpoint
of the Twitter API. e data derived from the stream API represent only 1% of the total volume of tweets, further
ltered by the selected keywords. e data derived from the search API represent a random sample of the tweets
containing the selected keywords up to a maximum rate limit of 18000 tweets every 10 minutes.
Since no ocial API are available for Instagram data, we built our own process to collect public contents
related to our keywords. We manually took notes of posts, comments and populated the Instagram Dataset.
Matching ability. We consider all the posts in our dataset that contain at least one URL linking to a website
outside the related social media platfrom (e.g., tweets pointing outside Twitter). We separate URLs in two main
categories obtained using the classication provided by MediaBias/FactCheck (MBFC). MBFC provides a clas-
sication determined by ranking bias in four dierent categories, one of them being Factual/Sourcing. In that
category, each news outlet is associated to a label that refers to its reliability as expressed in three labels, namely
Conspiracy-Pseudoscience, Pro-Science or Questionable. Noticeably, also the Questionable set include a wide
range of political bias, from Extreme Le to Extreme Right.
Using such a classication, we assign to each of these outlets a binary label that partially stems from the
labelling provided by MBFC. We divide the news outlets into Questionable and Reliable. All the outlets already
classied as Questionable or belonging to the category Conspiracy-Pseudoscience are labelled as Questionable,
the rest is labelled as Reliable. us, by questionable information source we mean a news outlet systematically
showing one or more of the following characteristics: extreme bias, consistent promotion of propaganda/con-
spiracies, poor or no sourcing to credible information, information not supported by evidence or unveriable, a
Table 3. Data breakdown of the number of posts, comments and users for all platforms.
Posts Comments Users Period
Gab 6,252 4,364 2,629 01/01–14/02
Reddit 10,084 300,751 89,456 01/01–14/02
YouTub e 111,709 7,051,595 3,199,525 01/01–14/02
Instagram 26,576 109,011 52,339 01/01–14/02
Twitter 1,187,482 – 390,866 27/01–14/02
Tot a l 1,342,103 7,465,721 3,734,815
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complete lack of transparency and/or fake news. By reliable information sources we mean news outlets that do not
show any of the aforementioned characteristics. Such outlets can anyway produce contents potentially displaying
a bias towards liberal/conservative opinion, but this does not compromise the overall reliability of the source.
Considering all the 2637 news outlets that we retrieve from the list provided by MBFC we end up with 800
outlets classied as Questionable 1837 outlets classied as Reliable. Using such a classication we quantify our
overall ability to match and label domains of posts containing URLs, as reported in Table4.e matching ability
that is low doesn’t refer to the ability of identifying known domain but to the ability of nding the news outlets
that belong to the list provided by MBFC. Indeed in all the social networks we nd a tendency towards linking
to other social media platforms, as shown in Table5.
Text analysis. To provide an overview of the debate concerning the virus outbreak on the various platforms,
we extract and analyze all topics related to COVID-19 by applying Natural Language Processing techniques to
the written content of each social media platform. We rst build word embedding for the text corpus of each
platform, then, to assess the topics around which the perception of the COVID-19 debate is concentrated, we
cluster words by running the Partitioning Around Medoids (PAM) algorithm on their vector representations.
Word embeddings, i.e., distributed representations of words learned by neural networks, represent words as
vectors in
bringing similar words closer to each other. ey perform signicantly better than the well-known
Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) for preserving linear regularities among
words and computational eciency on large data sets49. In this paper we use the Skip-gram model50 to construct
word embedding of each social media corpus. More formally, given a content represented by the sequence of
, we use stochastic gradient descent with gradient computed through backpropagation
rule51 for maximizing the average log probability
where k is the size of the training window. erefore, during training the vector representations of closely related
words are pushed to be close to each other.
In the Skip-gram model, every word w is associated with its input and output vectors,
, respect ively.
e probability of correctly predicting the word
given the word
is dened as
where V is the number of words in the corpus vocabulary. Two major parameters aect the training quality: the
dimensionality of word vectors, and the size of the surrounding words window. We choose 200 as vector dimen-
sion—that is typical value for training large dataset—and 6 words for the window.
log p(wt+j|wt)
Table 4. Number of posts containing a URL, matching ability and classication for each of the ve platforms.
Gab Reddit YouTu b e Instagram Twitter
Posts containing a URL 3,778 10,084 351,786 1,328 356,448
Matched 0.47 0.55 0.035 0.09 0.27
Questionable 0.38 0.045 0.064 0.05 0.10
Reliable 0.62 0.955 0.936 0.95 0.90
Table 5. Fraction of URLs pointing to social media. Table should be read as entries in each row link to entries
in each column. For example, Gab links to Reddit 0.003.
Gab Reddit YouTu b e Instagram Twitter Facebook
Gab 0.003 0.002 0.001 0.002 0.138 0
Reddit 0.043 0.006 0.009 0.001 0 0
YouTub e 00 0.292 0 0.088 0.081
Instagram 0 0 0.003 0 0.001 0.001
Twitter 0.059 0.001 0.257 0.003 0 0
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Before applying the tool, we reduced the contents to those written in English as detected with cld3. en we
cleaned the corpora by removing HTML code, URLs and email addresses, user mentions, hashtags, stop-words,
and all the special characters including digits. Finally, we dropped words composed by less than three characters,
words occurring less than ve times in all the corpus, and contents with less than three words.
To analyze the topics related to COVID-19, we cluster words by PAM and using as proximity metric the cosine
distance matrix of words in their vector representations. In order to select the number of clusters, k, we calculate
the average silhouette width for each value of k. Moreover, for evaluating the cluster stability, we calculate the
average pairwise Jaccard similarity between clusters based on 90% sub-samples of the data. Lastly, we produce
word clouds to identify the topic of each cluster. To provide a view about the debate around the virus outbreak,
we dene the distribution over topics
for a given content c as the distribution of its words among the word
clusters. us, to quantify the relevance of each topic within a corpus, we restrict to contents c with
max c>0.5
and consider them uniquely identied as a single topic each. Table6 shows the results of the text cleaning and
topic analysis for all the data.
Epidemiological models. Several mathematical models can be used to analyse potential mechanisms that
underline epidemiological data. Generally, we can distinguish among phenomenological models that emphasize
the reproducibility of empirical data without insights in the mechanisms of growth, and more insightful mecha-
nistic models that try to incorporate such mechanisms41.
To t our cumulative curves, we rst use the adjusted exponential model of43 since it naturally provides an
estimate of the basic reproduction number
. is phenomenological model (from now on indicated as EXP) has
been successfully employed in data-scarce settings and shown to be on-par with more traditional compartmental
models for multiple emerging diseases like Zika, Ebola, and Middle East Respiratory Syndrome43.
e model is dened by the following single equation:
Here, I is incidence, t is the number of days,
is the basic reproduction number and d is a damping factor
accounting for the reduction in transmissibility over time. In our case, we interpret I as the number
authors that have published a post on the subject.
As a mechanistic model, we employ the classical SIR model44. In such a model, a susceptible population can
be infected with a rate
by coming into contact with infected individuals; however, infected individuals can
recover with a rate
. e model is described by a set of dierential equations:
where S is the number of susceptible, I is the number of infected and R is the number of recovered. In our case,
we interpret the number
as the number
of authors that have published a post on the subject.
In the SIR model, the basic reproduction number
corresponds to the ration among the rate of
infection by contact
and the rate of recovery
. Notice that for the SIR model, vaccination strategies correspond
to bringing the system in a situation where
; in such a way, both the number of infected will decrease.
To estimate the basic reproduction numbers
for the EXP and the SIR model, we use least square
estimates of the modelsparameters42. e range of parameters is estimated via bootstrapping41,52.
Linear regression coecients. Table7 reports the regression coecient
, the intercept and the
for the linear t of Fig.3. High values of
conrm the linear relationship between reliable and questionable
sources in information diusion.
Table 6. Results of text cleaning and analysis for all the corpora.
Cleaned contents Vocabulary size Topics Contents with
max �>0.5
Instagram 21,189 posts 15,324 17 4,467
Twitter 638,214 posts 22,587 21 369,131
Gab 5,853 posts 3,024 19 2,986
Reddit 10,084 posts 1,968 34 6,686
YouTub e 815,563 comments 35,381 30 679,261
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Data availability
e datasets generated during and/or analysed during the current study are available from the corresponding
author on reasonable request.
Received: 11 April 2020; Accepted: 15 September 2020
1. Organization, W.H. Naming the coronavirus disease (COVID-19) and the virus that causes it. https :// encie
s/disea ses/novel -coron aviru s-2019/techn ical-guida nce/namin g-the-coron aviru s-disea se-(covid -2019)-and-the-virus -that-cause
s-it (2020 (accessed April 9, 2020)).
2. Quattrociocchi, W. Part 2-social and political challenges: 2.1 western democracy in crisis? In Global Risk Report World Economic
Forum (2017).
3. WHO Situation Report 13. https :// lt-sourc e/c o ron aviru se/situa tion-repor ts/20200 202-sitre p-13-ncov-v3.
pdf?sfvrs n=195f4 010_6. Accessed: 2010-09-30.
4. Zarocostas, J. How to ght an infodemic. Lancet 395, 676 (2020).
5. Organization, W.H. Director-general’s remarks at the media brieng on 2019 novel Coronavirus on 8 February 2020. https ://www. hes/detai l/direc tor-g ener al-s-r em ar ks-at-the-media -brie ng-o n-2019-n o v el - co r o n a viru s---8-fe b ru ary-2020 (2020
(accessed April 9, 2020)).
6. Mendoza, M., Poblete, B. & Castillo, C. Twitter under crisis: Can we trust what we RT?. Proceedings of the rst workshop on social
media analytics 71–79 (2010).
7. Starbird, K., Maddock, J., Orand, M., Achterman, P. & Mason, R.M. Rumors, false ags, and digital vigilantes: Misinformation on
twitter aer the 2013 boston marathon bombing. IConference 2014 Proceedings (2014).
8. Kim, L., Fast, S. M. & Markuzon, N. Incorporating media data into a model of infectious disease transmission. PLoS ONE 14, 1
9. John, T. & BenWedeman, C. Italy prohibits travel and cancels all public events in its northern region to contain Coronavirus. https
://editi e/italy -coron aviru s-lockd own-europ e-intl/index .html (2020 (accessed April 9, 2020)).
10. Sharot, T. & Sunstein, C. R. How people decide what they want to know. Nat. Hum. Behav. 2020, 1–6 (2020).
11. Shaman, J., Karspeck, A., Yang, W., Tamerius, J. & Lipsitch, M. Real-time inuenza forecasts during the 2012–2013 season. Nat.
Commun. 4, 1–10 (2013).
12. Viboud, C. & Vespignani, A. e future of inuenza forecasts. Proc. Natl. Acad. Sci. 116, 2802–2804 (2019).
13. Kulshrestha, J. etal. Quantifying search bias: Investigating sources of bias for political searches in social media. In Proceedings of
the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 417–432 (2017).
14. Schmidt, A. L. et al. Anatomy of news consumption on Facebook. Proc. Natl. Acad. Sci. 114, 3035–3039 (2017).
15. Starnini, M., Frasca, M. & Baronchelli, A. Emergence of metapopulations and echo chambers in mobile agents. Sci. Rep. 6, 31834
16. Schmidt, A. L., Zollo, F., Scala, A., Betsch, C. & Quattrociocchi, W. Polarization of the vaccination debate on Facebook. Vaccine
36, 3606–3612 (2018).
17. Del Vicario, M. et al. e spreading of misinformation online. Proc. Natl. Acad. Sci. 113, 554–559 (2016).
18. Bessi, A. et al. Science vs. conspiracy: collective narratives in the age of misinformation. PLoS ONE 10, e0118093 (2015).
19. Cinelli, M. et al. Selective exposure shapes the Facebook news diet. PLoS ONE 15, e0229129 (2020).
20. Zollo, F. et al. Debunking in a world of tribes. PLoS ONE 12, 1 (2017).
21. Baronchelli, A. e emergence of consensus: a primer. R. Soc. Open Sci. 5, 172189 (2018).
22. Del Vicario, M. et al. Echo chambers: emotional contagion and group polarization on Facebook. Sci. Rep. 6, 37825 (2016).
23. Bail, C. A. et al. Exposure to opposing views on social media can increase political polarization. Proc. Natl. Acad. Sci. 115,
9216–9221 (2018).
24. Vicario, M. D., Quattrociocchi, W., Scala, A. & Zollo, F. Polarization and fake news: early warning of potential misinformation
targets. ACM Trans. Web (TWEB) 13, 1–22 (2019).
25. Wardle, C. & Derakhshan, H. Information disorder: Toward an interdisciplinary framework for research and policy making.
Council of Europe report 27 (2017).
26. Vosoughi, S., Roy, D. & Aral, S. e spread of true and false news online. Science 359, 1146–1151 (2018).
27. Ruths, D. e misinformation machine. Science 363, 348–348 (2019).
28. Schild, L. etal. “ go eat a bat, chang!”: An early look on the emergence of sinophobic behavior on web communities in the face of
covid-19. arXiv preprintarXiv :2004.04046 (2020).
29. Velásquez, N. etal. Hate multiverse spreads malicious COVID-19 content online beyond individual platform control. Preprint arXiv
:2004.00673 (2020).
30. Ferrara, E. What types of COVID-19 conspiracies are populated by twitter bots? First Monday (2020).
31. Alam, F. etal. Fighting the COVID-19 infodemic: modeling the perspective of journalists, fact-checkers, social media platforms, policy
makers, and the society. Preprint arXiv :2005.00033 (2020).
Table 7. Coecients and
of the linear regressions displayed in Fig.3.
Dataset Type Intercept Coecient (
Gab Posts − 22.321 0.695 0.996
Reddit Posts − 4.111 0.047 0.997
Youtube Posts 4.529 0.073 0.998
Twitter Posts − 151.44 0.110 0.998
Gab Reactions 74.577 2.721 0.981
Reddit Reactions − 70.677 0.026 0.990
Youtube Reactions − 8854.33 0.025 0.986
Twitter Reactions − 2136.978 0.107 0.987
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Scientific RepoRtS | (2020) 10:16598 | 
32. Shahi, G.K., Dirkson, A. & Majchrzak, T.A. An exploratory study of COVID-19 misinformation on twitter. Preprint arXiv
:2005.05710 (2020).
33. Bovet, A. & Makse, H. A. Inuence of fake news in twitter during the 2016 us presidential election. Nat. Commun. 10, 1–14 (2019).
34. (MBFC), M. B.C. Media bias/fact check, the most comprehensive Meida bias check resource. https ://media biasf actch
(2020 (accessed April 9, 2020)).
35. Romero, D.M., Meeder, B. & Kleinberg, J. Dierences in the mechanics of information diusion across topics: idioms, political
hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, 695–704 (2011).
36. Organization, W.H. Novel Coronavirus (2019-NCOV) situation report-1 21 January 2020. https ://www.who .int/docs/defau lt-sourc
e/coron aviru se/situa tion-repor ts/20200 121-sitre p-1-2019-ncov.pdf?sfvrs n=20a99 c10_4 (2020 (accessed April 9, 2020)).
37. Pellis, L. et al. Eight challenges for network epidemic models. Epidemics 10, 58–62 (2015).
38. Liu, Y. et al. Characterizing super-spreading in microblog: an epidemic-based information propagation model. Physica A 463,
202–218 (2016).
39. Skaza, J. & Blais, B. Modeling the infectiousness of twitter hashtags. Physica A 465, 289–296 (2017).
40. Davis, J. T., Perra, N., Zhang, Q., Moreno, Y. & Vespignani, A. Phase transitions in information spreading on structured popula-
tions. Nat. Phys. 2020, 1–7 (2020).
41. Chowell, G. Fitting dynamic models to epidemic outbreaks with quantied uncertainty: a primer for parameter uncertainty,
identiability, and forecasts. Infect. Dis. Model. 2, 379–398 (2017).
42. Ma, J. Estimating epidemic exponential growth rate and basic reproduction number. Infectious Disease Modelling (2020).
43. Fisman, D. N., Hauck, T. S., Tuite, A. R. & Greer, A. L. An idea for short term outbreak projection: nearcasting using the basic
reproduction number. PLoS ONE 8, 1 (2013).
44. Bailey, N.T. etal. e mathematical theory of infectious diseases and its applications (Charles Grin & Company Ltd, 5a Crendon
Street, High Wycombe, Bucks HP13 6LE, 1975).
45. Centola, D. e spread of behavior in an online social network experiment. Science 329, 1194–1197 (2010).
46. Del Vicario, M., Scala, A., Caldarelli, G., Stanley, H. E. & Quattrociocchi, W. Modeling conrmation bias and polarization. Sci.
Rep. 7, 40391 (2017).
47. Baumann, F., Lorenz-Spreen, P., Sokolov, I. M. & Starnini, M. Modeling echo chambers and polarization dynamics in social net-
works. Phys. Rev. Lett. 124, 048301 (2020).
48. Grinberg, N., Joseph, K., Friedland, L., Swire-ompson, B. & Lazer, D. Fake news on Twitter during the 2016 us presidential
election. Science 363, 374–378 (2019).
49. Mikolov, T., Yih, W.-T. & Zweig, G. Linguistic regularities in continuous space word representations. In Proceedings of the Con-
ference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 746–751
(Association for Computational Linguistics 2013 (Georgia, Atlanta, 2013).
50. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. & Dean, J. Distributed representations of words and phrases and their compo-
sitionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems—Volume 2, NIPS’13,
3111–3119 (Curran Associates Inc., Red Hook, NY, USA, 2013).
51. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536. https
:// 3a0 (1986).
52. Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (CRC Press, London, 1994).
Author contributions
M.C., A.G., C.M.V., A.L.S., P.Z. collected and prepared the data. All authors conceived the experiments. M.C.,
A.G., C.M.V., A.L.S., E.B., and A.S. conducted the experiments. All authors analysed the results, wrote and
reviewed the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to W.Q.
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... In accordance with this idea, Cinealli et al. (2021, p. 1) states that 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". Furthermore, algorithms limit our selection process by always suggesting contents that comply with our previous consumption (Schmidt et al., 2017;Cinelli et al., 2020). This concept of content personalization in social media by algorithms is known as Filter Bubbles (Kuehn et al., 2020). ...
Conference Paper
The dissemination of fake news intensified during the COVID-19 pandemic. Thus, in this paper we propose a process-based model grounded on the diffusion of innovations theory to investigate how fake news was shared via social media. Accordingly, data was categorized according to the taxonomy of process, namely: input, output, players and activities. We found that individual decision-making traits, fake news perceived features and communication channel attributes are determinants of a personal attitude vis-à-vis a received fake news. The attitude then leads to the decision of whether or not to believe in the fake news, while confirmation conveys to the spread of same. In this way, social media, mediated by the power of algorithms and echo chambers, increase the chance of spreading misinformation.
... Various communication channels such as official government websites [6], health organizations such as the Centers for Disease Control and Prevention (CDC) [7], mass media [8], and social media [9] were used to raise awareness and disseminate information about the pandemic. Moreover, social media applications such as Twitter (X) [10] and news outlets [11] facilitated public discussions around the pandemic, thus enabling the analysis of public attention to the disease. ...
Social media platforms are valuable data sources in the study of public reactions to events such as natural disasters and epidemics. This research assesses for selected countries around the globe the time lag between daily reports of COVID-19 cases and GDELT (Global Database of Events, Language, and Tone) and Twitter (X) COVID-19 mentions between February 2020 and April 2021 using time series analysis. Results show that GDELT articles and tweets preceded COVID-19 infections in Australia, Brazil, France, Greece, India, Italy, the U.S., Canada, Germany, and the U.K., while for Poland and the Philippines, tweets preceded and GDELT articles lagged behind COVID-19 disease incidences, respectively. This shows that the application of social media and news data for surveillance and management of pandemics needs to be assessed on a case-by-case basis for different countries. It also points towards the applicability of time series data analysis for only a limited number of countries due to strict data requirements (e.g., stationarity). A deviation from generally observed lag patterns in a country, i.e., periods with low COVID-19 infections but unusually high numbers of COVID-19-related GDELT articles or tweets, signals an anomaly. We use the seasonal hybrid extreme Studentized deviate test to detect such anomalies. This is followed by text analysis of news headlines from NewsBank and Google on the date of these anomalies to determine the probable event causing an anomaly, which includes elections, holidays, and protests.
Objective Alarmingly, the individuals’ reach and coverage to get vaccinated in developing regions during the pandemic is a massive challenge for concerned authorities. This study aimed to demonstrate how cyberchondria play a significant role in a classical health belief model. Cyberchondria may influence cognitive factors (e.g. self-efficacy), which may contribute to an increase in attitude–behavior gap. Especially in the context of a health-centric scenario, it may discourage individuals to take protective measures. Method By using the cross-sectional research design, the authors conducted a quantitative survey in Pakistan and collected 563 responses from 303 male respondents (rural = 91; urban = 212) with (Urban M:35.5, standard deviation (SD):13.4) and rural M:37.5, SD:8.4). Result The findings indicate that decision self-efficacy among males is stronger than that in females. It dominates other determinants, which can dampen the individuals’ intentions to get vaccinated. For instance, the effect of conspiracies and perceived seriousness was noted nonsignificant and weak. In females, perceived seriousness was stronger determinant than in males. In addition, the negative effect of decision self-efficacy was noted in the case of females, and conspiracy and cyberchondria had a negative role. Conclusion This study highlights valuable implications for future research in infodemic, health communication and health literacy, and practical implications for regulatory bodies and public administration.
The article discusses the role of parliamentary oversight of emergency measures and policies in increasing democratic resilience and recovery from the COVID‐19 pandemic. The study on the Finnish Parliament is conducted by analyzing the statements of the Constitutional Law Committee, whose role is to conduct a parliamentary constitutional review of governmental bills. The main focus of the analysis is on the Committee's reviews of the constitutionality of the emergency measures and the procedures of law drafting. The research indicates that the committee considered the restrictions and exceptions of fundamental rights as proportional and necessary to prevent the overburdening of the healthcare system in most cases. However, the justifications for the emergency measures were often lacking, and the parliament's right to receive information was compromised. These deficits undermined the Parliament's capacity to oversee emergency measures and policies. The parliamentary constitutional review during the pandemic could still serve a critical complementary function by protecting fundamental rights and democratic values.
We investigate relationships between online self-disclosure and received social support and user engagement during the COVID-19 crisis. We crawl a total of 2,399 posts and 29,851 associated comments from the r/COVID19_support subreddit and manually extract fine-grained personal information categories and types of social support sought from each post. We develop a BERT-based ensemble classifier to automatically identify types of support offered in users’ comments. We then analyze the effect of personal information sharing and posts’ topical, lexical, and sentiment markers on the acquisition of support and five interaction measures (submission scores, the number of comments, the number of unique commenters, the length and sentiments of comments). Our findings show that: 1) users were more likely to share their age, education, and location information when seeking both informational and emotional support, as opposed to pursuing either one; 2) while personal information sharing was positively correlated with receiving informational support when requested, it did not correlate with emotional support; 3) as the degree of self-disclosure increased, information support seekers obtained higher submission scores and longer comments, whereas emotional support seekers’ self-disclosure resulted in lower submission scores, fewer comments, and fewer unique commenters; 4) post characteristics affecting audience response differed significantly based on types of support sought by post authors. These results provide empirical evidence for the varying effects of self-disclosure on acquiring desired support and user involvement online during the COVID-19 pandemic. Furthermore, this work can assist support seekers hoping to enhance and prioritize specific types of social support and user engagement.
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Several studies have explored the causes and consequences of public engagement with misinformation and, more recently, COVID-19 misinformation. However, there is still a need to understand the mechanisms that cause misinformation propagation on social media. In addition, evidence from non-Western societies remains rare. This study reports on survey evidence from eight countries to examine whether social media fatigue can influence users to believe misinformation, influencing their sharing intentions. Our insights also build on prior cognitive and personality literature by exploring how this mechanism is conditional upon users’ cognitive ability and narcissism traits. The results suggest that social media fatigue can influence false beliefs of misinformation which translates into sharing on social media. We also find that those with high levels of cognitive ability are less likely to believe and share misinformation. However, those with low cognitive ability and high levels of narcissism are most likely to share misinformation on social media due to social media fatigue. This study is one of the first to provide cross-national comparative evidence highlighting the adverse effects of social media fatigue on misinformation propagation and establishing that the relationship is not universal but dependent on both cognitive and dark personality traits of individuals.
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The universal and widespread use of social media platforms has revolutionized communication and connectivity, especially among adolescents and this widespread and globalization use of social media platform can affect the mental, psychological and physical health of the adolescents. This study aim to assess the effect of social media use on body image perception among femal adolescents. A descriptive correlational design conducted in Baghdad city at high school female of Directorate of Education Rusafa second and third from November 2022 to April 2023. A probability sample of (560) females students from 20 schools selected The instrument of the study was designed by using questionnaire which included informations aboute students a Socio- demographic characteristics, Social Media Engagement Scale for adolescents (SMES-A), and Body Image Perception Scale. The study results showed that (52%) of high femal students had moderate levels of social media use and (74.5%) had normal assessment of body image with a significant correlation between the two. However, there is significant difference was found between age, grade, monthly income and, social media use .and a significant difference was found between monthly income and body image . The study concludes that the social media use had negative effect on body image and this possible negative effects of social media use on body image perception related to how much time female students spends on social media platforms, the more time spent on social social media platform, the greater the negative impact of social media use platform on the body image perception among female students. The researcher recommendes that the ministry of education to support the school to holding courses for students on effect of social media use and activating the role of media censorship on content that published on social media platforms.
Online misinformation promotes distrust in science, undermines public health, and may drive civil unrest. During the coronavirus disease 2019 pandemic, Facebook—the world’s largest social media company—began to remove vaccine misinformation as a matter of policy. We evaluated the efficacy of these policies using a comparative interrupted time-series design. We found that Facebook removed some antivaccine content, but we did not observe decreases in overall engagement with antivaccine content. Provaccine content was also removed, and antivaccine content became more misinformative, more politically polarized, and more likely to be seen in users’ newsfeeds. We explain these findings as a consequence of Facebook’s system architecture, which provides substantial flexibility to motivated users who wish to disseminate misinformation through multiple channels. Facebook’s architecture may therefore afford antivaccine content producers several means to circumvent the intent of misinformation removal policies.
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During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1500 tweets relating to 1274 false and 226 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating(new tweets) or spreading(retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.
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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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Mathematical models of social contagion that incorporate networks of human interactions have become increasingly popular, however, very few approaches have tackled the challenges of including complex and realistic properties of socio-technical systems. Here, we define a framework to characterize the dynamics of the Maki–Thompson rumour spreading model in structured populations, and analytically find a previously uncharacterized dynamical phase transition that separates the local and global contagion regimes. We validate our threshold prediction through extensive Monte Carlo simulations. Furthermore, we apply this framework in two real-world systems, the European commuting and transportation network and the Digital Bibliography and Library Project collaboration network. Our findings highlight the importance of the underlying population structure in understanding social contagion phenomena and have the potential to define new intervention strategies aimed at hindering or facilitating the diffusion of information in socio-technical systems. The mathematical modelling of how information spreads in social networks has latterly gained fresh urgency. A study of realistic structured populations now identifies the threshold at which the propagation of rumours becomes contagious, thereby inducing a phase transition.
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While the historical impact of rumours and fabricated content has been well documented, efforts to better understand today’s challenge of information pollution on a global scale are only just beginning. Concern about the implications of dis-information campaigns designed specifically to sow mistrust and confusion and to sharpen existing sociocultural divisions using nationalistic, ethnic, racial and religious tensions is growing. The Council of Europe report on “Information Disorder: Toward an interdisciplinary framework for research and policy making” is an attempt to comprehensively examine information disorder and to outline ways to address it.
With people moving out of physical public spaces due to containment measures to tackle the novel coronavirus (COVID-19) pandemic, online platforms become even more prominent tools to understand social discussion. Studying social media can be informative to assess how we are collectively coping with this unprecedented global crisis. However, social media platforms are also populated by bots, automated accounts that can amplify certain topics of discussion at the expense of others. In this paper, we study 43.3M English tweets about COVID-19 and provide early evidence of the use of bots to promote political conspiracies in the United States, in stark contrast with humans who focus on public health concerns.
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.