Characterizing the public perception of WhatsApp through the lens of media
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Characterizing the public perception of WhatsApp
through the lens of media
Josemar Alves Caetano1 , Gabriel Magno1 , Evandro Cunha1,2 ,Wagner Meira Jr.1 ,
Humberto T. Marques-Neto3 , Virgilio Almeida1,4
{josemarcaetano, magno, evandrocunha, meira}@dcc.ufmg.br,
humberto@pucminas.br, virgilio@dcc.ufmg.br
1
Dept. of Computer Science, Universidade Federal de Minas Gerais (UFMG), Brazil
2
Leiden University Centre for Linguistics (LUCL), The Netherlands
3
Dept. of Computer Science, Pontifı́cia Universidade Católica de Minas Gerais (PUC Minas), Brazil
4
Berkman Klein Center for Internet & Society, Harvard University, USA
1 Introduction
The messaging service WhatsApp is, as of 2018, one
Abstract of the most rapidly growing components of the global
information and communication infrastructure, count-
ing with 1.5 billion users who send around 60 billion
WhatsApp is, as of 2018, a significant com- messages per day [Con18]. This tool combines one-to-
ponent of the global information and commu- one, one-to-many and group communication by offer-
nication infrastructure, especially in develop- ing private chats, broadcasts and public group chats,
ing countries. However, probably due to its through which users are able to send text and media
strong end-to-end encryption, WhatsApp be- (audio, image and video), as well as files in various
came an attractive place for the dissemina- formats.
tion of misinformation, extremism and other
According to data published by Statista [Sta18],
forms of undesirable behavior. In this pa-
more than half of the population of Saudi Arabia,
per, we investigate the public perception of
Malaysia, Germany, Brazil, Mexico and Turkey were
WhatsApp through the lens of media. We an-
active WhatsApp users in 2017. Also, the Reuters
alyze two large datasets of news and show the
Institute Digital News Report 2018 [NFK+ 18] shows
kind of content that is being associated with
a rise in the use of messaging applications, including
WhatsApp in different regions of the world
WhatsApp, as sources of news in several parts of the
and over time. Our analyses include the ex-
world. This report indicates that WhatsApp use for
amination of named entities, general vocabu-
news has almost tripled since 2014 and it has surpassed
lary and topics addressed in news articles that
Twitter as a communication system in many countries.
mention WhatsApp, as well as the polarity of
One of the alleged reasons for this is that users are
these texts. Among other results, we demon-
looking for more private and secure spaces to com-
strate that the vocabulary and topics around
municate. In addition to this, WhatsApp turned out
the term “whatsapp” in the media have been
to be an important platform for political propaganda
changing over the years and in 2018 concen-
and election campaigns, having held a central role in
trate on matters related to misinformation,
elections in Brazil, India [Goe18], Kenya, Malaysia,
politics and criminal scams. More generally,
Mexico and Zimbabwe, for instance. Also, WhatsApp
our findings are useful to understand the im-
has been frequently associated with the spread of mis-
pact that tools like WhatsApp play in the con-
information and disinformation [Wat18].
temporary society and how they are seen by
Despite its prominence, continued growth and opac-
the communities themselves.
ity, there has been an insufficient number of studies
exploring the various aspects of WhatsApp and sim-
Copyright © CIKM 2018 for the individual papers by the papers' ilar mobile messaging applications [GWCG18]. Since
authors. Copyright © CIKM 2018 for the volume as a collection WhatsApp provides encrypted end-to-end communi-
by its editors. This volume and its papers are published under
the Creative Commons License Attribution 4.0 International (CC
BY 4.0).cation, it is a great challenge to conduct large-scale and cultural trends through quantitative analyses of
analyses on the behavior of its users. In this work, texts, using sources like large collections of digitized
we take a different approach: instead of looking at books. Several studies explore this method to in-
inside the system, we focus on the public perception vestigate topics such as the dynamics of birth and
of WhatsApp from outside sources. The goals of this death of words [PTHS12], semantic change [GB11],
paper are: emotions in literary texts [ALGB13] and character-
istics of modern societies [Rot14]. Some works pro-
• to characterize how media in different countries pose a complementary approach to culturomics by us-
interpret the role of WhatsApp in society; ing historical news data [Lee11], analyzing European
news media [FTA+ 10] or the writing style and gen-
• to analyze the evolution of the perception of
der bias of particular topics in large corpora of news
WhatsApp over time, from its creation until its
articles [FALW+ 13]. Other works concentrate in spe-
massive popularization;
cific events in history, such as the Fukushima nuclear
• to comprehend how sensitive topics, such as disaster [LWSVC14], by using large datasets of media
politics, crime and extremism, are related to reports to understand aspects such as how the media
WhatsApp in different regions of the world and polarity towards a topic changes over time.
in distinct periods of time. Employing methods similar to the ones presented
here, [CMC+ 18] investigate the perception and the
To achieve these goals, we explore different techniques: conceptualization of the term “fake news” in the me-
analysis of Web search behavior, co-occurring named dia, showing that contextual changes around this ex-
entities and vocabulary, co-occurrence networks, top- pression might be observed after the United States
ics addressed and textual polarity. According to our presidential election of 2016. However, as far as we are
understanding, each of these methods is able to pro- concerned, this is the first work that uses these meth-
vide additional information about the perception of ods to examine in detail how the term “whatsapp” is
WhatsApp in the news articles investigated. As a being reported by news media in different parts of the
whole, our results indicate that the media has sig- world, making us able to analyze how important top-
nificantly changed its perception and portrayal of ics, such as misinformation, manipulation and extrem-
WhatsApp: while in the period before 2013 the focus ism, might be associated with WhatsApp by societies.
of the news was on WhatsApp features, in the follow-
ing years the tool started to be more associated with
On WhatsApp
social issues, including the dissemination of misinfor-
mation. Despite the increasing use of WhatsApp in the world,
This paper is organized as follows: in Section 2, we few quantitative and large-scale studies about this
review a selection of works on WhatsApp and, more instant messaging application are currently avail-
generally, on the use of textual datasets to under- able. [GT18] propose a data collection methodology for
stand social phenomena; in Section 3, we describe our this application and perform a statistical exploration
methodology of data collection and the overall char- to indicate how data from WhatsApp public groups
acterization of the datasets used in this investigation; can be collected and analyzed. Also, [MGB17] collect
next, in Section 4, we characterize the vocabulary, an- WhatsApp messages to monitor critical events during
alyze the topics addressed and evaluate the polarity of Ghana’s 2016 presidential election, and [CdO13] an-
the news articles contained in our datasets; finally, in alyze differences between WhatsApp and SMS mes-
Section 5, we conclude the paper and present future saging system using a large-scale survey. [FCSD15]
directions of work. investigate Facebook and WhatsApp traces collected
from an European national wide mobile network and
2 Related Work characterize the usage of both applications. The work
of [SHS+ 16] surveys users to investigate the usage of
On the use of textual datasets to understand social WhatsApp groups and, more specifically, its implica-
phenomena tions for mobile network traffic, while [RSS+ 18] collect
Analyzing how a term is used over time and in a personal information and messages from one hundred
geographic location is important to help in the un- WhatsApp users with the aim of understanding their
derstanding of how cultural values, societal issues usage patterns.
and customs are perceived by society and expressed All of these works investigate a limited part of
through language [Cam13, Mat53]. Culturomics, for WhatsApp, therefore offering a restricted understand-
example, is a concept proposed by [MSA+ 11] refer- ing of how this application is used. Nevertheless, here
ring to a method for the study of human behavior we study this tool using large datasets of externaldata provided by news articles containing the term indicates the most common associated terms and the
“whatsapp” in different regions of the world and cov- countries from which the highest volume of searches
ering the whole WhatsApp history, thus shedding light are originated from. It is also possible to filter these
not exactly on its usage, but on how it is viewed from results for given periods. For our investigations, we
outside sources. collected data from searches made between 2010 and
2018, and use this information in Section 4.1.
3 Data Collection
4 Analyses and Results
We use two large datasets of news articles in this study.
The first one is a collection of texts from the Corpus In this section, we discuss the outcomes of differ-
of News on the Web (NOW Corpus), which contains ent analyses aimed to understand the perception of
articles from online newspapers and magazines writ- WhatsApp in the media. Each characterization is in-
ten in English in 20 different countries from 2010 to troduced by a description of how it may contribute
the present time [Dav13]. This corpus is available for to accomplish our goals, followed by the methodology
download and online exploration1 and, according to employed and, finally, by a presentation and discussion
its author, it is, at the moment of our data collection, of the results found.
the largest corpus available in full-text format. In 31
May 2018, we gathered all the news articles containing 4.1 Web search behavior
the 33,185 occurrences of the term “whatsapp” in the
Before analyzing the public perception of WhatsApp
NOW Corpus. These news articles cover every year
through the lens of news articles from different regions
in the corpus (from 2010 to 2018) and comprise all
of the world, we investigate whether it is possible to
20 countries represented. These countries were then
observe a change in the Web search behavior regard-
grouped into six regions based on their geographic
ing the term “whatsapp” through time. We use data
locations (Africa, British Isles, Indian subcontinent,
collected from Google Trends to perform this analysis.
Oceania, Southeast Asia and the Americas).
Our results show that, unsurprisingly, the number
Our second dataset includes articles collected from
of queries on the Google Search engine for the term
Brazilian online newspapers and magazines, all written
“whatsapp” is constantly growing since the release of
in Portuguese, also containing the term “whatsapp”.
this tool for Android devices in 2010, as indicated in
We searched for articles starting from 2010, but did
Figure 1. Also, Table 2 lists the five most frequent
not find any from 2010 and 2011 containing the term
search terms employed by users who also searched for
“whatsapp”, so our second dataset contains news from
“whatsapp” from 2010 to 2018. Here, we notice a shift
2012 to 2018. To build this dataset, we used the tool
in the related terms through the years: in the first two
Selenium2 to automate Web searches with the term
years, most of the words are concerned with the down-
“whatsapp” in the following ten major Brazilian news
load of the app (“download”, “descargar”) and de-
websites: Exame, Folha de S. Paulo, Gazeta do Povo,
vice compatibility (“blackberry”, “iphone”, “nokia”);
G1, O Estado de S. Paulo, R7, Terra, Universo On-
then, from 2012 onwards, queries for “whatsapp” start
line (UOL), Valor Econômico and Veja. The total
to be linked to different topics, especially features of
number of occurrences of “whatsapp” extracted from
the tool (“status unavailable”, “whatsapp encryption”,
these websites on 31 May 2018 is 4,047. Finally, we
“video status download”), but also content shared in
used the Python library newspaper3 to collect the full
WhatsApp (“imagens para whatsapp”, “el negro del
texts of these news articles.
whatsapp”).
In Sections 4.2 to 4.6, we analyze the news texts
from the two previously described datasets. Table
1 shows the number of news containing the term 4.2 Co-occurring named entities
“whatsapp” in our two datasets, according to the geo- In natural language processing, named entity recog-
graphical origin of the corresponding news media and nition is the task of extracting mentions of named
the year of publication of the news article. entities – that is, definite noun phrases referring to
In addition to these datasets, we also collected data individuals, organizations, dates, locations – in a
from Google Trends4 , an online tool that indicates the text [BLK09]. We here extract the most mentioned
frequency of particular terms in the total volume of named entities in our NOW Corpus dataset for each
searches in the Google Search engine. This tool also region and year of publication of the articles in order
1 https://corpus.byu.edu/now/
to understand who are the main actors related to the
2 https://www.seleniumhq.org/ tool WhatsApp according to the media. In this paper,
3 https://pypi.org/project/newspaper/ the co-occurrence is computed on a document level,
4 https://trends.google.com/trends/ so we consider all the entities that are mentioned inTable 1: (a) Number of news articles containing the term “whatsapp” in our NOW Corpus dataset according
to the geographical origin of the corresponding news media; (b) Number of news articles containing the term
“whatsapp” in both NOW Corpus and Brazilian news articles datasets according to the year of publication.
(a) Geographical origin of news articles in our NOW Corpus dataset
Region Country Occurrences
United States 1,244
The Ameri- Canada 507
cas Jamaica 151
Total: 5.73% / 1,902
Singapore 2,889
Southeast Malaysia 2,578
Asia Philippines 253
Hong Kong 124
Total: 17.61% / 5,844
Great Britain 2,251
British Isles
Ireland 2,152
Total: 13.27% / 4,403
Region Country Occurrences
South Africa 5,274
Nigeria 1,607
Africa Kenya 1,585
Ghana 754
Tanzania 3
Total: 27.79% / 9,223
Australia 895
Oceania
New Zealand 306
Total: 3.62% / 1,201
India 8,991
Indian subconti- Pakistan 1,353
nent Sri Lanka 186
Bangladesh 82
Total: 31.98% / 10,612
(b) Year of publication of news articles in both NOW Corpus and Brazilian news articles datasets
Year 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total
Occurrences in NOW Corpus 4 41 145 393 1,101 1,642 7,266 11,677 14,636 33,185
Occurrences in Brazilian news articles 0 0 4 91 427 785 904 888 948 4,047
our news articles as co-occurring with the key-term ties accompanying the term “whatsapp” are usually
“whatsapp”. other social media companies (“Facebook”, “Twit-
To perform the named entity recognition, we use the ter”), countries (“US”, “India”), cities (“Dublin”,
Natural Language Toolkit (NLTK)5 classifier trained “Delhi”) and demonyms (“African”, “Australian”).
to recognize named entities. Since this tool does not When we analyze the continuation of the lists (not
support texts in Portuguese, we do not include the displayed here due to space constraints), we also find
dataset containing the Brazilian news articles in this that US-American individuals like Mark Zuckerberg
analysis. and Donald Trump are highly mentioned across the
Table 3 lists the ten most mentioned entities in globe. However, local entities are also mentioned in
each different region considered in this investigation. their respective regions: among the entities not dis-
Overall, we observe that the most mentioned enti- played in the table, the most mentioned persons or
5 http://www.nltk.org/ organized groups in each region are Mark ZuckerbergTable 3: Most mentioned named entities in each region
(the entity “whatsapp” is excluded from the lists)
Region Entities
1RUPDOL]HG9ROXPHRI6HDUFKHV
Facebook, Google, US,
The Americas Twitter, Instagram, Apple,
Android, American, Europe, China
Facebook, Malaysia, India,
Southeast Asia Singapore, US, Malaysian,
Google, Indian, China, Chinese
Facebook, Ireland, US,
British Isles London, Irish, Google,
British, Android, Dublin, TwitterTable 4: Most mentioned named entities in each year ● Africa ● Ind. subc. ● S.E. Asia
region
(the entity “whatsapp” is excluded from the lists) ● Brit. Ils. ● Oceania ● America
crime government law
Year Entities
2
Africa
Android, BlackBerry Messenger, BlackBerry, 1 ● ● ●
● ● ● ● ● ●
● ● ●
● ● ● ●
2010 Kik, iPhone, Nokia, WiFi, ● ●
●
● ●
●
●
●
0
Nokia N8, Symbian, India
2
BlackBerry, iPhone, Facebook,
Brit. Ils.
●
● ●
2011 Android, Skype, SMS, 1 ●
●
● ●
●
● ●
●
● ●
● ●
● ●
●
●
● ●
Google, Nokia, Apple, US 0
●
● ●
Facebook, SMS, Android,
Ind. subc.
2 ●
● ●
2012 India, iPhone, BlackBerry, ●
●
● ● ● ● ●
Words (avg. %)
● ● ●
1 ● ●
US, Nokia, Skype, Twitter ● ●
● ● ●
● ●
●
● ●
●
0 ●
Facebook, Android, Twitter,
2013 Google, Apple, India, 2
Oceania
● ● ● ●
Skype, SMS, BlackBerry, Indian 1
●
●
●
●
● ● ●
●
● ● ●
● ● ●
●
●
● ● ● ● ●
Facebook, Google, Twitter, 0 ● ●
2014 US, Android, India, Apple,
S.E. Asia
2 ●
WeChat, China, Instagram ●
● ●
●
● ●
●
●
●
●
●
1 ● ●
●
● ●
Facebook, India, Twitter, 0 ●
● ● ● ●
● ●
2015 Google, US, South Africa, Android,
Instagram, Skype, Apple 2
America
●
● ●
● ● ●
● ● ● ●
1
Facebook, India, Twitter, ●
● ●
●
● ●
● ● ●
●
●
● ● ●
2016 US, Google, Indian, Android, 0 ● ● ●
Instagram, Apple, iPhone 20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
10
12
14
16
18
10
12
14
16
18
10
12
14
16
18
Publish Year
Facebook, India, Twitter,
2017 US, Indian, Instagram, Google,
London, South Africa, China Figure 2: Average percentage of use of words from the
semantic fields crime, government and law in different
Facebook, India, Twitter,
2018 US, Indian, Google, Instagram,
regions and years (bars indicate standard errors of the
Delhi, South African, Telegram mean values)
4.4 Co-occurrence networks
matized words that appeared in each one of the Em- Another possible analysis on the vocabulary accom-
path categories. In this phase, instead of using the panying a key-term in a corpus can be made through
absolute frequency of words, we normalized it by di- the observation of co-occurrence networks. In our case,
viding the frequency of words in each category by the this method enables the visualization of the most rel-
total number of categorized words. evant words that appear in the same news articles as
the term “whatsapp” through the means of graphs. In
Since analyzing all the 194 Empath categories is this section, we consider both NOW Corpus and the
impractical, we manually selected three relevant and Brazilian news articles dataset.
noteworthy categories to scrutinize: crime, govern- For this analysis, we first extracted all the words
ment and law. In Figure 2, we present the average pro- from the articles and removed stop words using the
portion of words belonging to these categories in news lists provided by the Natural Language Toolkit for
articles representing different regions across the years. English and Portuguese. Then, we extracted the
On the whole, we observe an overall increase in the most relevant words from each document by using
proportion of words belonging to the three analyzed the term frequency-inverse document frequency (tf-idf)
categories, with most of the peaks (such as the ones technique, that reflects how important a word is to a
of 2013 in Oceania) probably due to political events document in a corpus [RU11]. We calculated the tf-
(e.g. Australian federal election of 2013). This finding idf for each pair (document, word) and extracted from
indicates that words from the semantic fields crime, the document the top 50 words with the highest tf-idf
government and law are being gradually more asso- scores.
ciated with WhatsApp in news from different regions In the following step, we counted the number of
of the world, corroborating the finding of Section 4.2 co-occurrences of the pairs of words. For each doc-
that shows an increase in the association of WhatsApp ument, we obtained the list with its 50 most relevant
with social and political situations in recent years. words (according to tf-idf) and incremented by one the(a) Africa (b) The Americas
(c) British Isles (d) Indian subcontinent
(e) Oceania (f) Southeast Asia
Figure 3: Co-occurrence networks for NOW Corpus news articles
counter relative to each pair of words in this list (com- a graph in which vertices represent words and edges
bination two by two). Instead of using the absolute indicate their co-occurrence in the same texts.
count of articles in which two words co-occur, we nor-
malized this value by dividing it by the total number Since there is a considerable number of documents
of articles. At the end of this process, we obtained and news articles can be relatively long, the number
of vertices and edges is large. For this reason, and dueto the fact that our goal is to identify the most rele- (LDA) [BNJ03] to automatically discover topics dis-
vant relationships, we selected only the top 200 edges cussed in texts. For this task, we first lowercased and
with the highest weights. Finally, we calculated the tokenized all the words in the datasets. Then, we re-
maximum spanning tree out of the remaining graph, moved stop words using, once again, the lists provided
generating a graph that depicts the most relevant re- by the Natural Language Toolkit (after having added
lationships in the format of a tree. the word “whatsapp” to the lists, since it appears in
The final networks for the news written in En- all texts). Finally, we ran the LDA algorithm using
glish are presented in Figure 3 and clearly show some the Python library spaCy7 for topic modeling. We
clusters that generally represent different themes or used topic coherence score [NLGB10] to choose the
specific events. Some of the most relevant ones are: optimum number of topics k to be returned by the al-
the “data” clusters, related to privacy, regulation and gorithm. For each region and year, the LDA model
data protection, containing words like “privacy” and returned these k topics containing terms ordered by
the name of information technology companies; the importance in the corresponding text. We then se-
“encryption” clusters, related to the discussion to- lected the most important topic as the representative
wards WhatsApp’s end-to-end encryption and con- of each region and year.
taining words like “security” and “message”; and the Table 5 shows the top-ranked ten terms produced
“crime” clusters, with words like “police”, “attack” by our LDA model representing the main topic for each
and “arrested”. region in each year. Here, for the Brazilian articles, we
The network regarding Brazilian news articles, pre- translated the terms from Portuguese to English.
sented in Figure 4, also shows two of the afore- It is interesting to observe that, between years 2010
mentioned clusters: the “data” cluster (“dados”, and 2013, the main topic in almost all regions was re-
“usuários”, “mensagens”) and the “crime” cluster lated to WhatsApp features, device compatibility and
(“polı́cia”, “civil”). Besides that, it presents at least differences between this application and other tech-
two other particularly interesting clusters. The first nologies, like SMS. In the Indian subcontinent, how-
one is related to the government blocking WhatsApp ever, the main topic of 2013 was about riots and poli-
in Brazil, with words like “bloqueio”, “justiça” and tics.
“operadoras”; and the second one is the “truck drivers’ In the Americas, in years 2016-2017, the main topics
strike” cluster, represented by the words “camin- of the news articles were also related to WhatsApp
honeiros”, “greve” and “governo”. features. However, in 2014 and 2015 we can observe
words like “refugee”, “libya” and “jihadist”, probably
associated with events in the Arab world. In 2018, the
main topic is related to the royal British wedding.
In Brazil, in 2014, we observe a topic shift to news
related to criminal scams in WhatsApp. It is interest-
ing to note that the main topic of 2015 is related to
a Brazilian court decision to block WhatsApp in the
whole country (because the company did not cooper-
ate in a criminal investigation). In 2016, year of the
impeachment of president Dilma Rousseff, the main
topic contains words like “dilma”, “impeachment” and
“lula”, while the main topic in 2017 is also about
politics, but containing more generic terms, such as
“politics” and “government”. In 2018, however, we
observe a clear dominance of terms related to Brazil
truck drivers’ strike, considered the biggest strike in
Figure 4: Co-ccurrence network for Brazilian news ar- the history of the country [Phi18]. In this occasion,
ticles WhatsApp played an essential role in the organization
of the strike, differently from previous protests that
were mostly coordinated through Facebook and Twit-
4.5 Topics addressed ter. This result reinforces the claims that WhatsApp
is a valuable tool to communicate and also to share
In addition to investigate the vocabulary present in political ideas in Brazil.
news articles mentioning WhatsApp, it is also possible In the Indian subcontinent, we note that, between
to find the main topics addressed in the texts included
in our datasets. We used latent Dirichlet allocation 7 https://spacy.io/Table 5: Main topics for each year in each region
Year The Americas Southeast Asia British Isles Africa Oceania Indian subcontinent Brazil
land, asli,
app, phone, free,
kik, service, federal, right, community, nokia, download, top,
message, text,
user, message, customary, smartphone,
iphone, major,
2010 say, livingston, indigenous, — — mobile, —
blackberry,
rim, blackberry, malaysian, skype, america,
service,
growth, contact government, india, ovi, store
unlimited
recognition
charge, dutch, message, phone,
business, small, free, text, mxit, knott craig, skype, iphone,
net neutrality, handset, android,
wilton, owner, call, app, market, platform, call, viber,
extra, mobile, service,
2011 blackberry,patricio, viber, iphone, cheap, attention, free, phone, —
internet, law, blackberry,
product, plan, network, phone, charge, buy, facebok, mobile, platform,
issue, state, iphone, symbiam,
entepreneur, service service mobile, smartphone android, text
kpn, skype text, app
world, system,
mobile, app, call, client, message,
hutterite, wipf, hong kong, customer, app, year, game, stop, late, phone, nokia,
nigerian, app, reconquer,
colony, website, data, free, service, iphone, apple malware, asha, service,
telecom, telefonica,
2012 waldner,help, people, messaging, communication, ransomware, price, launch,
constitution, europe, launch,
life, medium, roam, hutchison, social, lync, sonicwall, company, internet,
growth, industry, viber, intensify,
social application, lead facebook, network prevent,learn cost, news
service, call, send instantaneous
wannacry
communal, market, technology,
screen, small, science, kelemu, privacy, user,
blackberry, bbm, lau, speak, india, people, brazil, china,
blackberry, feel, agricultural, woman, data,
user, company, mobile, tencent, indian, blood, consume,
keyboard, camera, research, african, message,
2013 device, playbook, commercial,chat, muslim, riot, difficulty,
application, woman, school, dutch, policy,
service, app, application, martin, politician, emerging, attempt,
distraction, fibre, international, server, agency,
popularity, release ltd, conference secular, domesticate,
carbon, design develop, award address book
muzaffarnagar invade
minister,
party, johnson, party, leader, pakistan, indian,
canada, refugee, government, right, criminal, victim,
cohen, birmingham, show, music, event, government, terrorist,
game, family, party, state, page, victim,
former, leader, african, art prime minister, attack, muslim
2014 libya, trip, political, law, false, security,
britain, secretary, host, competition, election, kill, freedom,
help, team, country, leader, click, virus,
prime minister, world, winner, black national, senator, kashmir, army,
furniture, huddle election browser, federal
vote, election republican, journalist
president
geeta, pakistan,
jihadist, cabinet, terrorist, security, carriers,
social media, job, burundi, man, white, refugee, camp, woman, girl,
edward, plausible, attack, intelligence, application,
linkedin, online, election, protest, boat, data, web, ansar burney,
outrage, nisman, law, business, service,
2015 twitter, facebook, nkurunziza, president, australian, karachi,
nude, csec, communication, block, justice, brazil,
company, professional, police, party, service, phone, sushma swaraj,
guido, chamber, cameron, gchq, decision,
jobseeker, network bujumbura use, communication police, comissioner,
trove, inherently encryption voice, president
raghavan
business, canada, refugee, syrian, government,
hong kong, china, scotland yard, nakuru, group, police, karachi,
chera, border, aleppo, alkhuder, year, president,
chow, market, religious, muslim, political, youth, punjab, ranger,
cbsa, trade, homescreen, fear, lula,
2016 hktdc, president, country, british, party, member, arrest, pakistan,
red tape, cfib, syria, earthquake, dilma, work,
wechat, product, authority, murder, jubilee, nyamira, kashmir, kill,
government, agency, greece, aircraft, impeachment,
fair, party cafe, pope, bbc governor, leadership medium, protest
raise assad brazil, police
police, politics,
attack, school, immigrant, afghanistan,
market, report, group, lam, chat, president, nasa, government,
police, masood, country, kashmir, india,
company, service, chat group, kenyan, raila, woman,
westminster immigration, taliban, trump,
2017 user, include, help, responsible, content, leader, election, demonstration,
isis, terrorist, trump, employee, policy, war,
facebook, mobile, campaign, china, political, party, security, geddel,
arrest, kill, visa, ban, refugee, obama, american,
information service, team iebc, court prisoner, regime,
birmingham policy, president troop
arming
facebook, people, china, wechat, government,
meghan, harry, national, party,
facebook, zuckerberg, world, internet, student, school, tencent, president,
church, england, government,
data, scandal, company, university, high, newsguard, app, truck drivers,
ceremony leader, state,
2018 mistake, platform, technology, education, parent, chinese, company, strike, minister,
wedding, support, michael,
prevent, ad, social media, water, health, traffic, military coup,
prince, kate, australian,
authority, issue online, disease, study mall road, group, support,
vow, royal wedding election, member
change, new authority deputy, world cup
years 2013 and 2017, the main topics were related to and “protest” are related to protests that occurred
political themes. In the year 2013, for example, words during the Burundian election. In this occasion, the
like “riot”, “muslim” and “muzaffarnagar” are associ- government temporarily blocked messaging services,
ated with the riots in Muzaffarnagar, when some riot- including Facebook, WhatsApp and Twitter [Vir16].
ers used WhatsApp to promote violence. In the years In 2017, words like “election” and “president” are as-
2014-2016, rumors on terrorist attacks were dissemi- sociated with the suspicion that disinformation and
nated through WhatsApp. In 2017, the main topic fake news were being used to influence Kenyans dur-
seems to be associated to the decision of US presi- ing the elections [Sam17].
dent Donald Trump to not withdraw its troops from
Afghanistan. There is also a clear dominance of words associ-
ated with terrorist attacks in 2015 and 2017 in the
In Africa, in 2014, the words “burundi”, “election” British Isles. These words are related to the use ofWhatsApp to organize these acts [BBC17]. In South- Figure 5 depicts the average polarity of the news
east Asia, news on WhatsApp are generally associated articles in each region and in each year, both in NOW
with comparisons with WeChat and, in the year 2018, Corpus and in the dataset of Brazilian articles. We
news in this region were associated with the Facebook– observe a major dominance of negative polarities in
Cambridge Analytica data scandal. In Oceania, in almost all regions and years, but especially after 2013.
the years 2015-2017, the main topics were associated News articles containing the term “whatsapp” are be-
with refugees and immigration. WhatsApp played an coming more negative over time probably because of
important role during the Syrian Civil War in these the nature of the news articles themselves: in Africa,
years, since journalists and individuals living there for instance, the term “whatsapp” occasionally ap-
used WhatsApp to communicate with people of for- peared in news articles about refugees8 ; in India, in
eign countries [Boh17]. articles about the spread of fake news that resulted
These results show that WhatsApp usage is highly in violence9 ; in Southeast Asia and the Americas, in
associated with important political events in several news about the promotion of violence10 ; in Brazil, in
regions of the world – particularly in Africa, Brazil and news concerning criminal scams11 .
India. The shift in the main topics addressed in the
regions before 2013 (that were related to WhatsApp 4.7 Summary of results
features and device compatibility) to, in the following The most relevant findings presented in this section
years, political and criminal themes confirms results can be summarized as follows:
(presented in previous sections) that indicate a grad-
ual increase in the association of this application with • the interest for the term “whatsapp” is constantly
social and political situations. increasing over the years, as indicated by the rise
of news about this tool and of Google Search
4.6 Polarity queries for this term (Section 4.1);
Our final investigation sheds light in another di- • this interest is being accompanied by a change of
mension of the news articles containing the term framing around the term “whatsapp” in the me-
“whatsapp”: now, we analyze the polarities of the ar- dia – from topics regarding WhatsApp features
ticles – that is, whether the expressed opinions in the and technology to those related to misinforma-
texts are mostly positive, negative or neutral. Here, we tion, politics and criminal scams (Sections 4.1,
are interested in analyzing how the polarity of news ar- 4.3, 4.4, 4.5);
ticles related to WhatsApp changes over time and in • the polarity of news articles containing the term
different regions. “whatsapp” is becoming more negative over time,
To do this, we performed sentiment analy- probably due to the fact that this tool is being
sis in each of the articles in our datasets using gradually more associated with crimes, violence
SentiStrength [TBP+ 10], a tool that estimates the and fake news (Section 4.6).
strength of positive and negative polarities in texts.
This tool receives as input pieces of text and returns a 5 Concluding Remarks
score that varies from -4 (negative) to +4 (positive).
In this paper, we present a quantitative analysis on the
Africa British Isles Oceania The Americas
Brazil Indian subcontinent Southeast Asia public perception of the messaging tool WhatsApp in
1.0 news articles. For conducting our analyses, we used
two datasets that cover the whole history of the ap-
plication since its release for Android devices in 2010
0.5
until May 2018. The first of these datasets is a cor-
pus of news articles written in English and published
Average polarity
0.0 from 2010 to 2018 in 20 countries, while the second one
contains Brazilian news articles published from 2012 to
2018. We also used data collected from Google Trends
−0.5
in one of our analyses.
Here, we investigated how media sources from dif-
−1.0 ferent parts of the world have been reporting stories re-
lated to WhatsApp and whether the rise of the public
10
12
4
16
8
1
1
20
20
20
20
20
Year
8 https://bit.ly/2GSTlo7
9 https://bit.ly/30qdcmn
Figure 5: Average polarity of news articles from differ- 10 https://nyti.ms/2EehjIJ
ent regions containing the term “whatsapp” over time 11 https://bit.ly/2VNnoGYinterest in this application over time was accompanied [Boh17] Lauren Bohn. Syrian history is unfold-
by changes on its perception by the media. We ob- ing on WhatsApp. Retrieved from https:
served changes in the vocabulary, in the mentioned en- //bit.ly/2Va1VaU. Accessed on May 16,
tities, in the addressed topics and in the polarity of the 2019, 2017.
articles mentioning the tool WhatsApp in our datasets.
In particular, we noticed a shift on media perception [Cam13] César Nardelli Cambraia. Da lexicologia
in almost all analyzed regions from the period before social a uma lexicologia sócio-histórica:
2013 – when the focus was on WhatsApp features and caminhos possı́veis. Revista de Estudos
device compatibility – to the following years – when da Linguagem, 21(1):157–188, 2013.
the application started to be gradually more associated [CdO13] Karen Church and Rodrigo de Oliveira.
with misinformation, manipulation and extremism, as What’s up with whatsapp?: Compar-
well as with political and criminal activities. ing mobile instant messaging behaviors
The techniques and approaches proposed here can with traditional sms. In Proceedings
be used to measure the media perception of any com- of the 15th International Conference on
pany (or entity in general), but WhatsApp was cho- Human-computer Interaction with Mo-
sen due to its influence in information (and misinfor- bile Devices and Services, MobileHCI ’13,
mation) dispersion and to the fact that it has been pages 352–361, New York, NY, USA,
related to topics such as extremism, corruption and 2013. ACM.
political propaganda. In future works, we intend to
add more analyses, use news articles from others re- [CMC+ 18] Evandro Cunha, Gabriel Magno, Josemar
gions of the world where WhatsApp is popular (e.g. Caetano, Douglas Teixeira, and Virgilio
Germany, Indonesia, Malaysia) and compare the per- Almeida. Fake news as we feel it: per-
ception of WhatsApp in the media with the perception ception and conceptualization of the term
of it in other sources, like social networks and news ar- “fake news” in the media. In Proceedings
ticles comments. Also, we plan to compare the public of the 10th International Conference on
perception of WhatsApp with the one of similar tools Social Informatics (SocInfo 2018), 2018.
(e.g. Telegram, Facebook Messenger, WeChat) in or-
der to understand which of them are more likely to be [CMG+ 14] Evandro Cunha, Gabriel Magno, Mar-
mentioned in certain types of news – for instance, in cos André Gonçalves, César Cambraia,
political or crime-related news. and Virgilio Almeida. How you post is
who you are: Characterizing Google+
6 Acknowledgements status updates across social groups. In
Proceedings of the 25th ACM Conference
This work was partially supported by CNPq, CAPES, on Hypertext and Social Media (HT’14),
FAPEMIG and the projects InWeb, MASWEB and pages 212–217, New York, NY, USA,
INCT-Cyber. September 2014. Association for Comput-
ing Machinery (ACM).
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