Can WhatsApp Counter Misinformation by
Limiting Message Forwarding?
Philipe de Freitas Melo1, Carolina Coimbra Vieira1, Kiran Garimella2,
Pedro O. S. Vaz de Melo1, and Fabr´ıcio Benevenuto1
1Departamento de Ciˆencia da Computa¸c˜ao - UFMG, Belo Horizonte MG, Brazil,
philipe, carolcoimbra, olmo, firstname.lastname@example.org
2Institute for Data, Society and Systems - MIT, USA,
Abstract. WhatsApp is the most popular messaging app in the world.
The closed nature of the app, in addition to the ease of transferring
multimedia and sharing information to large-scale groups make What-
sApp unique among other platforms, where an anonymous encrypted
messages can become viral, reaching multiple users in a short period of
time. The personal feeling and immediacy of messages directly deliv-
ered to the user’s phone on WhatsApp was extensively abused to spread
unfounded rumors and create misinformation campaigns during recent
elections in Brazil and India. WhatsApp has been deploying measures
to mitigate this problem, such as reducing the limit for forwarding a
message to at most ﬁve users at once. Despite the welcomed eﬀort to
counter the problem, there is no evidence so far on the real eﬀectiveness
of such restrictions. In this work, we propose a methodology to evaluate
the eﬀectiveness of such measures on the spreading of misinformation cir-
culating on WhatsApp. We use an epidemiological model and real data
gathered from WhatsApp in Brazil, India and Indonesia to assess the
impact of limiting virality features in this kind of network. Our results
suggest that the current eﬀorts deployed by WhatsApp can oﬀer delays
on the information spread, but are ineﬀective in blocking the propagation
of misinformation campaigns in public groups.
Keywords: WhatsApp, Misinformation, Fake news, Virality, Epidemi-
ological model, Complex network
Messaging applications, such as WhatsApp, Facebook messenger, Telegram and
Viber have gained a signiﬁcant role in the daily lives of smartphone users. What-
sApp is the most popular app, with over 1 billion active users3. Besides being
widely used to keep in touch with friends & family, run businesses, read news
& get informed, WhatsApp has become an important platform for information
dissemination and social mobilization, especially in Brazil, India and Southeast
2 Philipe Melo et al.
There are a few key features that make WhatsApp unique among other plat-
forms. First, WhatsApp allows the connection among like-minded individuals
through chat groups. These chat groups have a limit of 256 users and can be
private or public. In the case of private groups, new members must be added by
a member who assumes the role of group administrator. For public groups, the
access is by invitation links that could be shared to anyone or be available on
the Web. These public groups often come up to discuss hobbies and passions,
but also speciﬁc topics such as health, education, and politics. Although the ma-
jority of groups are private, set up among people who share a social relationship
(e.g., family, friends, workmates) public groups have been a catalyzing feature
for the purpose of information diﬀusion: most of their members are strangers
to each other. This is evident in countries like Brazil, where a survey reported
that 76% of WhatsApp users are part of groups, 58% participate in groups with
people they do not know, and 18% of these groups discuss politics . For this
reason, public groups can act as a shortcut for information to directly traverse
distant parts of the underlying social network structure via a clique of weak ties,
broadening and accelerating information dissemination .
Furthermore, the app has two sharing functions: broadcast, in which a con-
tact list can be created to send messages to up to 256 contacts (users or groups)
at once and forward, that a single message received can be forwarded to other
5 contacts (users or groups). Those characteristics allow the message to travel
long distances by the network, but the end-to-end encryption makes it diﬃcult
to identify the source and track the spread of the messages. Because of these
peculiarities, WhatsApp generated a controversy related to its anonymity and
virality characteristics. This conﬂict is due the fact that we can view WhatsApp
in two diﬀerent ways, such as a technology company, or as a media platform.
As a technology platform, it ensures user anonymity and security by encrypting
your data. As a media platform, it transmits information and disseminates con-
tent in large-scale. Thus, messages sent anonymously reach thousands of people
quickly and without any ethical or legal regulation of this disseminated content,
promoting, for example, disinformation campaigns. The massive spread of mis-
information and rumors  led to requests from both the national governments4
towards altering features that allow the platform to be abused to spread misin-
formation at scale. This resulted in WhatsApp implementing restrictions on the
way messages are forwarded5by reducing the limit for forwarding content to at
most 5 users/groups at a time. However, there are no studies that investigate
the impact of these limitations or whether the numbers chosen are suﬃcient to
deal with the spread of viral content.
In this work, we evaluate the dynamics of the spread of (mis)information on
a network of public WhatsApp groups. We focus on the mass communication
features of public chat groups and the forwarding/broadcasting of messages.
More speciﬁcally, we study the anatomy of this emerging social network and
comprehend its peculiarities to answer the question of how the forwarding tools
Can WhatsApp Counter Misinformation by Limiting Message Forwarding? 3
contribute to the virality of (mis)information and whether system limitations
are capable of preventing the spread of content. We also propose some hints on
how the problem of large-scale dissemination can be countered.
The rest of the paper is organized as follows. In Section 2 we describe the
related work. In Section 3 we describe the WhatsApp data used in this paper
together with the methodology used to collect it. An initial characterization of
the data is shown in Section 4. In Section 5, we reconstruct a network from the
collected data and we compare its characteristics with other real and synthetic
networks. In Section 6, we execute several experiments to measure the virality of
a potential misinformation within these networks via the Susceptible-Exposed-
Infected (SEI) epidemiological model . Finally, in Section 7, we discuss our
ﬁndings and ﬁnal conclusions from the analysis.
2 Related Work
Recently, there have been numerous research studies reporting misinformation
campaigns on social networks [3, 7]. This includes popular platforms like Face-
book, where, Ribeiro et al.  evaluated the use of the Facebook advertising
platform to carry out political campaigns that exploit targeted marketing as a
means of disseminating false advertisements or on divisible themes. There are
also reports of attempts to manipulate political discourse with the use of social
bots and even state-sponsored trolls [3, 19].
However, only recently, social message applications, such as WhatsApp were
reportedly a means of abuse by misinformation campaigns [15, 14, 11, 5]. Particu-
larly, Resende et al.  analyzed the dissemination of diﬀerent kinds of content
on WhatsApp, such as images, audio and videos, ﬁnding a large amount of mis-
information in the form of memes and fake images. Resende et al.  provide
an in-depth characterization of textual messages, showing that misinformation
tends to be more viral, i.e., these messages are shared more times, by a larger
number of users, and in more public groups. Bursztyn et al. showed that right-
wing WhatsApp groups in Brazil were more active and engaged in spreading
political content in WhatsApp along the 2018 Brazilian elections, in comparison
with left-wing groups. Melo et al.  developed a system to help fact checkers,
providing them a sample of the most popular images, messages, URLs, audios
and videos shared hundreds of public groups in Brazil and India.
On the side of virality, Rushkoﬀ et al.  studies dissemination in digital
media by comparing characteristics of biological and computational viruses, ex-
plaining this phenomenon and covering many eﬀorts using epidemiological mod-
els to analysis viral spreading of disinformation on social networks. Our work is
complementary to the above eﬀorts as we investigate, using SEI model , how
limitations on virality features, such as limits on message forwarding recently
deployed by WhatsApp, are eﬀective in mitigating misinformation campaigns.
Since chat groups on WhatsApp are mostly private, they are much harder to
monitor than Facebook or Twitter discussions. Because of that, we use recent
4 Philipe Melo et al.
tools developed by Garimella and Tyson  to get access to messages posted on
WhatsApp public groups. Given a set of invitation links to public groups, we
automatically join these groups and save all data coming from them. We selected
groups from Brazil, India and Indonesia dedicated to political discussions. These
groups have a large ﬂow of content and are mostly operated by individuals
aﬃliated with political parties, or local community leaders. We monitored the
groups during the electoral campaign period and, for each message, we extracted
the following information: (i) the country where the message was posted, (ii)
name of the group the message was posted, (iii) user ID, (iv) timestamp and,
when available, (v) the attached multimedia ﬁles (e.g. images, audio and videos).
As images usually ﬂow unaltered across the network, they are easier to track
than text messages. Thus, we choose to use the images posted on WhatsApp to
analyse and understand how a single piece of content ﬂows across the network.
To calculate a ﬁngerprint for every image, we follow the same strategy of ,
using the Perceptual Hashing (pHash) algorithm to group together sets of images
with the same content. Since similar images have the same hash value, we can
count its popularity and track its spreading across the network. In total for all
three countries, 784k unique image objects were tracked.
For all three countries, we analyzed the data around the election day, 60
days before and 15 after. We kept the same time span for the three countries to
ease the comparison among them. The dataset overview and the total number
of distinct images are described in Table 1. As expected, Brazil and India have
a much larger volume of data shared on WhatsApp compared to Indonesia, as
they have much more groups and users registered in our data collection system.
Data Limitations: Our methodology gathers a large dataset from public
groups, but it is known that most of WhatsApp conversations occurs in private
channels. A key limitation of our work is that our results reﬂect only users and
content that circulate on the public layer of WhatsApp. We note, however, that
there is evidence that suggests that public groups make up the key backbone
of the misinformation campaigns on WhatsApp.6First, they are focused on
political activism, where most of the shared content contain misinformation. For
example, a fact checking agency in Brazil checked the top 61 images shared in
these groups, ﬁnding that only 10% of them are true . There is also evidence
of the use of automatic tools to ﬂood WhatsApp public groups with political
content7. Then, the users in those groups would be responsible to amplify the
misinformation campaign and propagate it to the private part of the network.8
Nevertheless, this project brings a considerable amount of data that can help
to elucidate how WhatsApp is being abused for mass communication and the
ampliﬁcation backbone composed by public groups that distribute messages in
bulk for thousands of users. At the least, our results provide a ‘lower bound’ on
the ability of messages to spread on WhatsApp, since the network we consider
is a subset of the entire WhatsApp network.
Can WhatsApp Counter Misinformation by Limiting Message Forwarding? 5
Table 1: Overview of the datasets.
#Users #Groups Unique
Brazil 17,465 414 258k 416k 2018/08/15 - 2018/11/01
India 362,739 5,839 509k 810k 2019/03/15 - 2019/06/01
Indonesia 8,388 217 16k 21k 15/03/2019 - 2019/06/01
Prob. (X < x)
(a) Total shares.
(b) Total groups
Fig. 1: CDF of sharing coverage and time dynamics metrics of images shared at
least twice on WhatsApp.
4 Spreading Coverage and Dynamics
Since we are able to track all occurrence of a given image, we can see the coverage
and dynamics of spreading of these images in our data. To evaluate spreading
metrics regarding time and coverage, we only consider the images that were
shared at least twice, since we cannot see the eﬀect of spreading of images only
posted a single time. This set consists of 2,384 images in Indonesia, 103,031
images in Brazil and 44,731 images for India, which represents approximately
20% of the images for each country.
First, we calculate the total number of shares of each image and how many
groups they have appeared. Figures 1a,1b show the Cumulative Distribution
Function (CDF) of the total number of shares and the number of distinct groups
each image appeared in. It is possible to note that there are some very popular
images broadly shared more than 500 times in Brazil and a thousand times in
India, moreover, they reached more than 100 groups in both countries. Even
though a large part of images were shared few times, this shows that WhatsApp
can be used not only to particular conversations but also as a mass communica-
tion media with a potential virality of its content.
Time Analysis for WhatsApp Data: Besides looking at the spread of
images on WhatsApp, we also analyze their “lifetimes” in Figure 1c. The lifetime
is given by the diﬀerence between the last and ﬁrst occurrence of the image in
our dataset. In short, while most of the images (80%) last no more than 2 days,
there are images in Brazil and in India that continued to appear even after 2
months of the ﬁrst appearance (105minutes). We can also see that the majority
(60%) of the images are posted before 1000 minutes after their ﬁrst appearance.
Moreover, in Brazil and India, around 40% of the shares were done after a day of
their ﬁrst appearance and 20% after a week. Further analysis, in Figure 1d shows
the distribution of the “inter-event times” between posts of the same image. We
observe that the inter-event time of images in India is much faster than in Brazil
6 Philipe Melo et al.
and Indonesia, i.e., more than 50% of posts are done in intervals of 10 minutes
or less, while just 20% of shares were done in this same time interval in Brazil
and Indonesia. We manually looked for reasons behind the short period of time
between posts and found that in the data from India, there is more automated,
spam-like behavior compared to in Brazil and Indonesia.
In conclusion, these results suggest that WhatsApp is a very dynamic network
and most of its image content is ephemeral, i.e., the images usually appear and
vanish quickly. The linear structure of chats make it diﬃcult for an old content to
be revisited, yet there are some that linger on the network longer, disseminating
over weeks or even months.
5 Network structure
In this section, we investigate the network structure of public WhatsApp groups
and compare its characteristics with other real and synthetic social networks.
To create a network from the WhatsApp groups, we connected two groups if
they share a common user. Although WhatsApp is an encrypted personal chat
application, the possibility to create public groups allows multiple and socially
distant users to connect to each other across the network, forming a complex so-
cial structure able to ﬂow high volumes of information. Although the WhatsApp
group network resembles many other social networks, little is known about the
diﬀerences in information dissemination.
In Figure 2, we show the distribution of groups per user and users per group.
In order to compare WhatsApp peculiarities with another popular platforms, we
also use data from Reddit network, modeling the subreddits as groups and users
as members. Note that even though Reddit has the same group characteristic,
we want to evaluate speciﬁc features of WhatsApp that lead to very diﬀerent
network structures. The maximum of 256 members in groups is a determining
element in the network, capable of limiting group size, mainly in India (Figure
2a), where there are over 300k users and more than 5k groups.9
On the other hand, in Reddit, where there is no limit, it is possible to see
that the group size can be as large as 105members, what creates big hubs of
users. As both platforms have no limit on the number of groups users can join,
we expected to see no diﬀerences in the total number of groups users participate.
However, note that in Reddit, the distribution has a exponential decay, with a
limit on ≈100 groups. On the other hand, all WhatsApp curves are similar with
a well behaved power law curve, which naturally yields a larger variance. Note
that in India we have users who participated in more than 300 groups.
In Figure 3 we show these networks for all three countries. The size of the
node is proportional to the number of members in that group. We colored nodes
according to its community in that graph following the modularity algorithm .
Observe that in all graphs there is an evident largest connected component and
some other group clusters. Also, note that some groups position themselves as
bridges and hubs, connecting diﬀerent communities of the network structure.
9In our data, some groups have more than 256 members, because our data is a tem-
poral snapshot and members can leave and join groups during this time.
Can WhatsApp Counter Misinformation by Limiting Message Forwarding? 7
(a) Wpp. India
(b) Wpp. Brazil (c) Wpp. Indonesia (d) Reddit
Total Groups Joined
(e) Wpp. India
(f) Wpp. Brazil (g) Wpp. Indonesia (h) Reddit
Fig. 2: Distributions of the number of members per group and total groups joined
per user in WhatsApp (Wpp) and in Reddit.
(a) Brazil (b) India (c) Indonesia
Fig. 3: WhatsApp Public Groups Network for each country. Each node is a group
and edges represents members in common.
Next, we compare the characteristics of the WhatsApp group network and
other social network graphs: (i) random generated graphs using the Barabasi-
Albert scale free model, the Erd˝os–R´enyi model, the small world model  and
the Forest Fire network model , for which we used the same number of nodes
in the Indian dataset in order to create a comparable network; (ii) the network
of subreddits from Reddit , and, (iii) the Flickr network , which, diﬀerent
from the WhatsApp and Reddit group networks, the Flickr graph represents the
network of images shared by users on the platform. The results are shown in
Table 2. We observe that WhatsApp shares common characteristics with other
real-world social networks: high clustering coeﬃcient, giant largest connected
component, and small average path length, which are all typical properties of a
social network. The only aberration is the slightly higher diameter than others
graphs analyzed. WhatsApp also shows a higher Pearson coeﬃcient, in which
nodes tend to be connected with other nodes with similar degree values. In
epidemic analyses, it can help to understand the spreading of infection across
the network, as a misinformation campaign targeting high degree groups is likely
to spread to other high degree nodes.
8 Philipe Melo et al.
Table 2: Network metrics for the public groups network from WhatsApp com-
pared to other networks.
#Nodes #Edges Mean
Coeﬃcient Diameter APL∗Density LCC∗∗ Pearson
Wapp. India 5,839 407,081 69.71 0.59 11 3.17 0.0239 92.6% 0.295
Wapp. Brazil 414 1,400 6.76 0.32 8 3.19 0.0164 65.2% 0.346
Wapp. Indonesia 217 699 6.44 0.38 9 3.09 0.0298 55.3% 0.290
Bar.-Albert 5,839 792,300 271.38 0.10 3 1.95 0.0465 100% 0.008
Erdos-Renyi 5,839 1,534,952 525.76 0.09 2 1.91 0.0901 100% -0.001
Smallworld 5,839 604,250 206.97 0.34 3 1.98 0.0355 100% 0.007
ForestFire 5,839 12,930 4.43 0.42 17 5.25 0.0008 100% -0.066
Reddit 15,122 4,520,054 597.81 0.82 6 2.03 0.0395 99,8% -0.045
Flickr 105,938 2,316,948 43.74 0.09 9 4.8 0.0004 99.8% 0.247
*Average Path Length
**Largest Connected Component
6 Impact of forwarding limitations on information spread
We use the epidemiological model of Susceptible-Exposed-Infected (SEI)  to
estimate the virality of malicious messages in WhatsApp groups by assuming
misinformation as an infection that spreads to users through the group network.
In our scenario, the nodes are members of various groups and the infected nodes
can spread the infection to a entire group at once, exposing all their participants.
In this model, Susceptible (S) is the initial condition in which the user did not
have any contact with the infection; Exposed (E) are those who received the
misinformation through any of the groups they participate, but didn’t share it;
Infected (I) is the ﬁnal stage in which a user who was exposed to the content
shares this message in the network. This model has two basic parameters: virality
(α) and exposition (β). We also implemented a third parameter forward limit
(ϕ) to test the restrictions on sharing by WhatsApp.
The virality (α) of malicious content controls the rate of infected users. This
indicates the probability of an infected user to share the content to neighbors. We
consider that users are infected when they forward or broadcast this content, as
it indicates a degree of belief in the shared message. The exposition parameter
(β) refers to the rate at which exposed users become infected. It represents
the probability of an exposed user to transform in an infected one. Lastly, the
forward limit (ϕ) of infection is a speciﬁc parameter we use to restrict the
spread of the infection, to simulate the actual conditions on WhatsApp. This
parameter indicates the maximum amount of groups an infected node can spread
the infection to. The simulation starts by selecting one user randomly to be the
initial infected node to start the spreading. For each user exposed, they have
a probability given by αto share the malicious message. When these infected
nodes decide to forward, there is a limitation given by ϕ, the maximum number
of groups they will send the content to. After that, each user in the groups that
received the message are exposed. Then, each exposed user has also a probability
βof becoming an infected node and sharing the content. This iteration repeatedly
continues until all users are infected.
We perform several experiments using our SEI model comparing the dissem-
ination in diﬀerent scenarios by enforcing limits of broadcast and forward. Since
Can WhatsApp Counter Misinformation by Limiting Message Forwarding? 9
(a) Brazil (b) India (c) Indonesia
Fig. 4: SEI model varying the forward limit (ϕ). α=β= 0.1.
(a) Brazil (b) India (c) Indonesia
Fig. 5: Time to infect all user in the network on simulations of SEI model varying
the virality (α) from 0.001 up to 1.0 .
it would not be possible to reach isolated nodes using the whole structure, only
the largest connected component was considered. Figure 4 shows the fraction
of users infected over time for all countries when the forward limit (ϕ) is var-
ied, i.e., how the restrictions implemented by WhatsApp can interfere with the
spread. We considered the limit of forwarding to 5 groups (the real scenario), 20
groups (the previous limit), and 256 groups (the current limit for broadcasting).
Notice that the rate of users exposed in the network grows very fast, regardless
of forwarding limits, 60 iterations is enough to infect the entire network. Also,
limitations on forwarding slightly diminish the velocity of spreading, but does
not stop it completely, especially for exposed users.
We also evaluate the time needed for (mis)information with diﬀerent poten-
tial viralities to infect all users. Figure 5 shows the time needed to infect 100%
of the users by varying αfrom 0.001 up to 1.0, with diﬀerent forwarding lim-
its. Observe that in situations of mass dissemination (high α), it is diﬃcult to
stop the infection because of the strong connections between groups. However,
note that the limits in forwarding and broadcasting help to slow the propaga-
tion, mainly in larger networks, as in India. In short, limits on forwarding
and broadcasting can reduce velocity of dissemination by one order
of magnitude for any of αvirality.
In reality, users may lose interest in some topics through time, so it is nat-
ural for a time limit on the content spread, i.e., content circulates until it loses
attention and stagnates. We add this time limit to our SEI model, calling this
period “lifetime”, which denotes the maximum duration of an infection in the
simulation before it is entirely extinguished. Figure 6 shows the percentage of
users infected by increasing the lifetime of the infection. Each data point in the
plot indicates a simulation where we ﬁxed the values α, β and increased the life-
time an infection could last. We observe that for all three countries, an infectious
10 Philipe Melo et al.
0 50 100 150 200 250
Infection Lifetime (iterations)
0 50 100 150 200 250
(b) India (c) Indonesia
Fig. 6: Users infected by time in simulations of the SEI model using max lifetime
for infections. (α)=(β)=0.1. Forward limit (ϕ) = 5.
(a) Brazil (b) India (c) Indonesia
Fig. 7: Real Time SEI model using “incubation time” before spread infection and
each iteration equals 1 minute (log). (α) = (β)=0.1. Forward limit (ϕ) = 5.
content that lasts 100 iterations or more is powerful enough to expose more than
half population. When this content persists in the network for at least 150 itera-
tions, it usually infects almost 100% of the users. Note that there is a window of
possibility to identify infectious misinformation already spreading (say, around
50 iterations), where a large enough sample of the users were exposed to the
content but were not infected and nullify its virality (e.g. disabling forwarding
on that piece of content), thus preventing further contagion.
In the SEI model, the spread of information was measured in terms of the
number of iterations. In this experiment, we use real data to adapt the SEI
model and measure the spread in terms in terms of minutes. For this, we add an
“incubation time” based on the time real data takes to spread over the network.
In this version of the model, each iteration represents 1 minute, but when an
infected node intends to spread, it has to wait a speciﬁc amount of time before
doing it. This time is sampled from a distribution of “waiting times”, which
can be: (i) Random: a uniform distribution with domain between 1 and 1440
minutes (1 day); (ii) Inter-event Time: the empirical distribution of inter-event
times computed in Figure 1d; (iii) Group Time: this strategy is based on the
following idea – it usually takes longer for a message to reach 100 groups than
to reach 2 groups. To implement this, in this strategy, we make the incubation
time on initial steps smaller than in the subsequent steps. During the simulation,
we track the number of times the infection has already spread and, for each
step, we have a diﬀerent time distribution according to how long it took for the
actual images in WhatsApp to reach those number of groups in our data. Figure
7 shows experiments considering the three strategies to compute the time to
spread. In India, where we have the bursty inter-event times, we see that with
the inter-event time strategy 60% of users are exposed to the content in the ﬁrst
200 minutes of infection. In Brazil, group time is faster than inter-event time
Can WhatsApp Counter Misinformation by Limiting Message Forwarding? 11
and infected around half of user in the ﬁrst 2 day (3000 minutes). Finally, in
Indonesia all three strategies have very similar behavior, taking over 2 weeks to
infect more than 80% of the users. Nevertheless, a content is still viral when all
three strategies are considered, i.e., a misinformation can spread in most of the
network before one month of infection.
The closed nature of WhatsApp and the ease of transferring multimedia and
sharing information to large-scale groups makes WhatsApp an extremely hard
environment for the deployment of countermeasures to combat misinformation.
WhatsApp opens a paradoxical use of its platform, allowing at the same time
the viral spread of a content and encrypted personal chat. Together those two
features can be widely abused by misinformation campaigns.
Our results show that a content can spread quite fast through the network
structure of public groups in WhatsApp, reaching later the private groups and
individual users. Our empirical observations about the network of WhatsApp
public groups in three diﬀerent countries provides a means of inferring the infor-
mation velocity in terms of minutes related to real-world scenarios. We veriﬁed
that most of the images (80%) last no more than 2 days in WhatsApp which, in
India, can be already enough to infect half of users in public groups, although
there are still 20% of messages with a time span suﬃcient to be viral in the three
countries using any of our strategies to estimate time of infection.
Using a SEI model we investigate a set of what-if questions about the lim-
its that WhatsApp can impose in the information propagation. While the limit
on the number of users per groups can prohibit the creation of giant hubs to
spread information through the network, this limit, however, is not able to pre-
vent a content to reach a large portion of entire platform. More important, our
analysis show that low limits imposed on message forwarding and broadcasting
(e.g. up to ﬁve forwards) oﬀer a delay in the message propagation of up to two
orders of magnitude in comparison with the original limit of 256 used in the
ﬁrst version of WhatsApp. We note, however, that depending on the virality
of the content, those limits are not eﬀective in preventing a message to reach
the entire network quickly. Misinformation campaigns headed by professional
teams with an interest in aﬀecting a political scenario might attempt to create
very alarming fake content, that has a high potential to get viral . Thus,
as a counter-measurement, WhatsApp could implement a quarantine approach
to limit infected users to spread misinformation. This could be done by tem-
porarily restricting the virality features of suspect users and content, especially
during elections, preventing coordinated campaigns to ﬂood the system with
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