![Growth of the number of authors vs time. Time is expressed in number of days since 1 st Jan 2020 (day 1). Shaded areas represents [5%, 95%] estimates of the models obtained via bootstrapping least square estimates of the EXP model (upper panels, eq. 3) and of the SIR model (lower panels, eq. 3.3).](https://www.researchgate.net/profile/Matteo-Cinelli/publication/339873482/figure/fig1/AS:868103655260165@1583983448633/Growth-of-the-number-of-authors-vs-time-Time-is-expressed-in-number-of-days-since-1-st_Q320.jpg)
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The COVID‑19 social media
infodemic
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 diusion 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 dierential 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
R0
for each social media platform. Moreover, we identify information spreading
from questionable sources, nding dierent volumes of misinformation in each platform. However,
information from both reliable and questionable sources do not present dierent spreading patterns.
Finally, we provide platform‑dependent numerical estimates of rumors’ amplication.
e World Health Organization (WHO) dened 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 diusion 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 outbreaks5–7, since it could even speed up the epidemic process by inuencing 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 ocial
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 aect their behavior10,
particularly when the news cycle—dominated by the disintermediated diusion 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 inuence people’s behavior and alter the eectiveness 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 inuences 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-
specic eect. e denition of “Fake News” may indeed be inadequate since political debate oen resorts
to labelling opposite news as unreliable or fake27. Studying the eect 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 scientic literature from multiple perspectives including the
open
*
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dynamics of hatespeech and conspiracy theories28,29, the eect of bots and automated accounts30, and the threats
of misinformation in terms of diusion 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 diusion 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 (
R0
), 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,
R0
=1 is a threshold parameter. When
R0<1
the disease will die
out in a nite period of time, while the disease will spread for
R0>1
. In social media,
R0>1
will indicate the
possibility of an infodemic.
Finally, coherently with the classication provided by the fact-checking organization Media Bias/Fact Check34
that classies 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 diusion of (mostly) reliable or (mostly) questionable
contents and we characterize the spreading of information regarding COVID-19 relying on this classication.
We nd that users in mainstream platforms are less susceptible to the diusion of information from question-
able sources and that information deriving from news outlets marked either as reliable or questionable do not
present signicant dierence 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 amplication parameters for COVID-19 on each social media platform.
Results
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 dier-
ent environments having dierent 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
Methods.
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 dierences 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 specic timings for content consumption; such patterns may depend upon
the dierence in terms of audience and interaction mechanisms (both social and algorithmic) among platforms.
Information spreading. Eorts to simulate the spreading of information on social media by reproducing
real data have mostly applied variants of standard epidemic models37–40. 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 specic 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
R0
, representing the expected
number of new infectors directly generated by an infected individual for a given time period42. An epidemic
occurs if
R0>1
,—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 aer being exposed to the topic. While in real epidemics
R0>1
highlights the possibility of a pandemic, in our approach
R0>1
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
R0
. As expected, all the values of
R0
are supercritical—even considering condence intervals (Table1)—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.
While
R0
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 complex45–47. 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
R0∼102
that is way beyond what
has been observed in any real-world epidemic.
Figure1. 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 diusion
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 denitions 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 unveriable, 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.
Figure3 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
ρ
(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 eect is observed in Gab: while the volume of posts from questionable
sources is just the
∼70%
of the volume of posts from reliable ones, the volume of reactions for the former ones
is
∼3
times bigger than the volume for the latter ones. Such results hint the possibility that dierent platform
react dierently to information produced by reliable and questionable news outlets.
To further investigate this issue, we dene the amplication factor
E
as the average number of reactions to a
post; hence,
E
is a measure that quanties the extent to which a post is amplied in a social media. We observe
that the amplication
EU
(for unreliable posts posts produced by questionable outlets) and
ER
(for reliable posts
Figure2. 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 condence
R0
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,
R0
assumes unrealistic values
∼102
for the SIR model.
Gab Reddit YouTu b e Instagram Twitter
REXP
0
[1.42, 1.52] [1.44, 1.51] [1.56, 1.70] [2.02, 2.64] [1.65, 2.06]
RSIR
0
[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 dene the coecient of relative amplication
α
=E
U/
E
R
. It is a
measure of whether a social media amplies questionable (
α>1
) or reliable (
α<1
) posts. Results are shown in
Table2. Among mainstream social media, we notice that Twitter is the most neutral (
α∼1
i.e.
EU∼ER
), w hile
YouTube amplies questionable sources less (
α∼4/10
). Among less popular social media, Reddit reduces the
impact of questionable sources (
α∼1/2
), while Gab strongly amplies them (
α∼4
).
erefore, we conclude that the main drivers of information spreading are related to specic peculiarities of
each platform and depends upon the group dynamics of individuals engaged with the topic.
Figure3. 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 coecients
ρ
, representing the ratio among the volumes of questionable and reliable posts (
ρpost
)
and engagements (
ρeng
). 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 eect
is observed in Gab: while the volume of questionable posts is just the
∼70%
of the volume of reliable ones, the
volume of engagements for questionable posts is
∼3
times bigger than the volume for reliable ones. Further
details concerning the regression coecients 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 amplied in each social media platform. e average engagement
EU
(for unreliable post)
and
ER
(for reliable post) vary from platform to platform, and are the largest in Twitter and the lowest in Gab.
e coecient of relative amplication
α
=E
U/
E
R
measures whether a social media amplies more unreliable
(
α>1
) or reliable (
α<1
) posts. Among more popular social media platforms, we notice that Twitter is the
most neutral (
α∼1%
i.e.
EU∼ER
), while YouTube amplies unreliable sources less (
α
∼
4/10
). Among less
popular social media platforms, Reddit reduces the impact of unreliable sources (
α
∼
1/2
) while Gab strongly
amplies them (
α∼4
).
EU
ER
α
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
0.35
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Conclusions
In this work we perform a comparative analysis of users’ activity on ve dierent 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 diusion 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 signicant dierences in their
its spreading patterns. Our analysis suggests that information spreading is driven by the interaction paradigm
imposed by the specic social media or/and by the specic interaction patterns of groups of users engaged with
the topic. We conclude the paper by computing rumor’s amplication 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 ecient epidemic models accounting
for social behavior and to design more eective and tailored communication strategies in time of crisis.
Methods
Data collection. Table3 reports the data breakdown of the ve social media platforms. Dierent 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 ocial 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 ocial 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 classication provided by MediaBias/FactCheck (MBFC). MBFC provides a clas-
sication determined by ranking bias in four dierent 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 classication, 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
classied 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 unveriable, 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 classied as Questionable 1837 outlets classied as Reliable. Using such a classication we quantify our
overall ability to match and label domains of posts containing URLs, as reported in Table4.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 Table5.
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
Rn
bringing similar words closer to each other. ey perform signicantly better than the well-known
Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) for preserving linear regularities among
words and computational eciency 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
words
w1,w2,...,wT
, 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,
uw
and
vw
, respect ively.
e probability of correctly predicting the word
wi
given the word
wj
is dened as
where V is the number of words in the corpus vocabulary. Two major parameters aect 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.
(1)
1
T
T
�
t=1
k
�
j=−k
log p(wt+j|wt)
(2)
p
(wi|wj)=
exp
uT
wivwj
V
l=1
exp
uT
lvwj
Table 4. Number of posts containing a URL, matching ability and classication 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 dene the distribution over topics
c
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 identied as a single topic each. Table6 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
R0
. 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 dened by the following single equation:
Here, I is incidence, t is the number of days,
R0
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
Cauth
of
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
β
by coming into contact with infected individuals; however, infected individuals can
recover with a rate
γ
. e model is described by a set of dierential 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
I+R
as the number
Cauth
of authors that have published a post on the subject.
In the SIR model, the basic reproduction number
R0=β/γ
corresponds to the ration among the rate of
infection by contact
β
and the rate of recovery
γ
. Notice that for the SIR model, vaccination strategies correspond
to bringing the system in a situation where
S<N/R0
; in such a way, both the number of infected will decrease.
To estimate the basic reproduction numbers
REXP
0
and
RSIR
0
for the EXP and the SIR model, we use least square
estimates of the models’ parameters42. e range of parameters is estimated via bootstrapping41,52.
Linear regression coecients. Table7 reports the regression coecient
ρ
, the intercept and the
R2
values
for the linear t of Fig.3. High values of
R2
conrm the linear relationship between reliable and questionable
sources in information diusion.
(3)
I
=
R0
(1+d)
t
t
(4)
∂
t
S=−βS·I/N
∂tI=βS·I/N−γI
∂tR=γI
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
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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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