# Regional Differences in Information Privacy Concerns After the Facebook-
Cambridge Analytica Data Scandal111This version corresponds to the accepted
manuscript (29 November 2021). For the final published version and citation
please see https://doi.org/10.1007/s10606-021-09422-3

Felipe González-Pizarro${}^{1^{\dagger}}$https://orcid.org/0000-0002-2735-5747
ID 1Departamento de Informática, Universidad Técnica Federico Santa María,
Chile<EMAIL_ADDRESS><EMAIL_ADDRESS>2Human-
Centered Data Science Lab, University of Washington, Seattle, United States
<EMAIL_ADDRESS><EMAIL_ADDRESS>†Department of Computer Science, University of
British Columbia, Vancouver, Canada<EMAIL_ADDRESS>Andrea Figueroa2
1Departamento de Informática, Universidad Técnica Federico Santa María, Chile
<EMAIL_ADDRESS><EMAIL_ADDRESS>2Human-Centered
Data Science Lab, University of Washington, Seattle, United States
<EMAIL_ADDRESS><EMAIL_ADDRESS>†Department of Computer Science, University of
British Columbia, Vancouver, Canada<EMAIL_ADDRESS>Claudia
López1*https://orcid.org/0000-0003-1699-1999 ID & Cecilia Aragon2
1Departamento de Informática, Universidad Técnica Federico Santa María, Chile
<EMAIL_ADDRESS><EMAIL_ADDRESS>2Human-Centered
Data Science Lab, University of Washington, Seattle, United States
<EMAIL_ADDRESS><EMAIL_ADDRESS>†Department of Computer Science, University of
British Columbia, Vancouver, Canada<EMAIL_ADDRESS>
###### Abstract

While there is increasing global attention to data privacy, most of their
current theoretical understanding is based on research conducted in a few
countries. Prior work argues that people’s cultural backgrounds might shape
their privacy concerns; thus, we could expect people from different world
regions to conceptualize them in diverse ways. We collected and analyzed a
large-scale dataset of tweets about the #CambridgeAnalytica scandal in Spanish
and English to start exploring this hypothesis. We employed word embeddings
and qualitative analysis to identify which information privacy concerns are
present and characterize language and regional differences in emphasis on
these concerns. Our results suggest that related concepts, such as
regulations, can be added to current information privacy frameworks. We also
observe a greater emphasis on data collection in English than in Spanish.
Additionally, data from North America exhibits a narrower focus on awareness
compared to other regions under study. Our results call for more diverse
sources of data and nuanced analysis of data privacy concerns around the
globe.

###### keywords:

Online privacy; Twitter; Word embedding; Content analysis; IUIPC

##  1 Introduction

The right to control one’s personal information has gained significant
importance lately [Lee et al. (2019)]. Indeed, 58% of the countries have data
protection and privacy legislation, while another 10% have drafted legislation
about it [United Nations Conference on Trade and Development (2020)]. This
broad interest is related to the massive amount of personal data collected by
information systems and the risk that such information could be wrongly
distributed online [Lee et al. (2019)].

The study of information privacy has advanced our understanding of
individuals’ concerns regarding organizational practices associated with
collecting and using their personal information [Smith et al. (1996)].
However, a literature review revealed a strong bias towards USA-centered
studies across privacy concerns literature and warned about the limitations to
generalizability this entails [Bélanger and Crossler (2011), Okazaki et al.
(2020)]. The review’s authors hypothesized that individuals from different
world regions have diverse cultures, values, and laws, which can, in turn,
result in different conceptualizations of information privacy and its impacts
[Bélanger and Crossler (2011), Mohammed and Tejay (2017)]. To study these
differences, privacy research has often relied on survey-based studies
[Cockcroft and Rekker (2016)]. For example, a questionnaire was applied to
explore differences in privacy perceptions between Facebook users from Germany
and the USA [Krasnova and Veltri (2010)], and a cross-national survey was
conducted to evaluate information attitudes of consumers in the USA and Brazil
[Markos et al. (2017)]. These multi-country privacy studies have had limited
sample sizes, which makes the results difficult to generalize [Lee et al.
(2019), Huang and Bashir (2016)]. They also tend to be focused on one or two
cultures, usually including the USA [Cockcroft and Rekker (2016)]. Hence,
multi-country information privacy research is still needed to extend our
understanding of this increasingly relevant topic around the globe [Adu et al.
(2019), Zou et al. (2018)].

We propose an alternative approach to study information privacy concerns over
a large geographical scope. This work combines word embeddings, open coding,
and content analysis to examine tweets related to a large data breach scandal.
We seek to characterize similarities and differences in privacy terms across
people who tweet about this issue in different languages and from different
world regions. Inspired by [Rho et al. (2018)], where text analysis was used
to analyze answers about individuals’ privacy concerns, we analyze the
semantic context in which privacy-related terms were used in tweets written by
different groups of people.

We focus on the Facebook–Cambridge Analytica data scandal. In 2018, the firm
Cambridge Analytica was accused of collecting and using the personal
information of more than 87 million Facebook users without their authorization
[Venturini and Rogers (2019), Isaak and Hanna (2018), Lapaire (2018)]. The
scandal sparked multiple conversations over technology’s societal impact and
risks to citizens’ privacy and well-being worldwide. Opinions, facts, and
stories related to it took place on different social media platforms such as
Twitter, where the hashtag #DeleteFacebook became a trending topic for several
days [Lin (2018), Mirchandani (2018)].

We analyze more than a million public tweets in Spanish or English that use
hashtags or keywords related to the scandal. We divide the dataset by language
(Spanish and English) and regions (Latin America, Europe, North America, and
Asia) and create word-embeddings for each subset. Then, we systematically
analyze and compare the semantic context of four keywords, such as data,
privacy, user, and company, across the embeddings. We contrast our results
with one of the most used information privacy concerns framework to find terms
and tweets matching different concerns. Then, we test a null hypothesis that
there is no difference in emphasis on information privacy terms across
languages and world regions. In this process, we discover the presence of
related concepts that could be integrated into information privacy frameworks,
such as regulations. We also observe statistically significant language
differences in emphasis on data collection and significant regional
differences in emphasis on awareness. Finally, we discuss the implications of
our results.

We summarize prior work on information privacy concerns in Section 2. Section
3 introduces our research question and hypothesis. Section 4 details our
research method, while Section 5 reports on our findings. Section 6 offers a
discussion of our results, limitations, and future work. Finally, Section 7
provides our conclusions.

##  2 Information privacy concerns

Information privacy concerns emerge when an individual “feels threatened by a
perceived unfair loss of control over their privacy by an information-
collecting body”[Lee et al. (2015)]. Previous research argues that information
privacy concerns are a multidimensional construct[Jozani et al. (2020),
Correia and Compeau (2017), Heravi et al. (2018)]. A multidimensional approach
allows identifying to what extent users are concerned about different aspects
of information privacy [Yun et al. (2019), Hong and Thong (2013), Hong and
Thong (2013)]. Different authors have proposed alternative conceptualizations
to measure information privacy concerns. We briefly review the most adopted
ones in the following subsection. Then, we summarize prior work on differences
in privacy concerns across countries, regions, and other characteristics.

Prior research has examined privacy concerns from other perspectives as well
[Heravi et al. (2018), Gerber et al. (2018), Yun et al. (2019)]. A vast
portion of privacy research on social networking sites has focused on
examining users’ privacy behaviors, such as the intention to provide personal
information or transact online [Wisniewski et al. (2017), Kokolakis (2017),
Gerber et al. (2018), Oghazi et al. (2020), Chai (2020), Heravi et al. (2018),
Markos et al. (2017)]. In a similar direction, several studies have
investigated the use of privacy setting configurations [Wisniewski et al.
(2015), Vitak et al. (2015), Wisniewski et al. (2017), Li et al. (2020)].
Rather than centering on behavior or behavioral intention, we focus our review
on information privacy concerns that characterize general personal
dispositions [C. Sipior et al. (2013)]. We think that this part of the
literature aligns better with what people can say, in a declarative way, about
data privacy on social media.

###  2.1 Assessing information privacy concerns

Two questionnaires have been widely used to evaluate individuals’ information
privacy concerns [Bélanger and Crossler (2011), Cockcroft and Rekker (2016),
Morton and Sasse (2014)]: Concerns for Information Privacy (CFIP) and Internet
Users’ Information Privacy Concerns (IUIPC).

####  2.1.1 CFIP: Concerns for Information Privacy

The CFIP framework [Smith et al. (1996)] focuses on individuals’ perceptions
of how organizations use and protect personal information [Van Slyke et al.
(2006)]. CFIP identifies four dimensions:

  * •

Collection: concerns about personal data that is collected over time;

  * •

Unauthorized secondary use: concerns about organizations using personal data
for another purpose without the individual’s authorization;

  * •

Improper access: concerns about unauthorized people having access to personal
data;

  * •

Errors: concerns about adequate protections from deliberate and accidental
errors in personal data.

To measure them, smith1996information proposed and validated a 15-item
questionnaire. The CFIP questionnaire was validated by surveying 355 consumers
from the USA and applying confirmatory factor analysis (CFA) [Stewart and
Segars (2002)]. So far, this questionnaire had been considered as one of the
most established methods to measure quantitatively information privacy
concerns [Harborth and Pape (2018)] and had been widely used in the literature
[Harborth and Pape (2017), Stewart and Segars (2002)]. However, the CFIP and
its measurement instrument were originally defined for users in an offline
context [Palos-Sanchez et al. (2017)]. As the Internet enabled new ways to
collect and process data, it was expected that new concerns about information
privacy might emerge [Malhotra et al. (2004)], and a new framework was
proposed: the IUIPC.

####  2.1.2 IUIPC: Internet Users’ Information Privacy Concerns

malhotra2004internet introduced the IUIPC framework and conceptualized
Internet users’ concerns about information privacy from a perspective of
fairness. Drawing from social contract theory [C. Sipior et al. (2013)],
malhotra2004internet argue that personal data collection is perceived to be
fair when a user has control over their personal data and is informed about
the intentions that organizations have about how to use it. The IUIPC includes
three constructs:

  * •

Collection: concerns about the amount of personal data owned by others
compared to the perceived benefits [Malhotra et al. (2004)]. It is related to
the perceived fairness of the outcomes one receives. Users provide information
if they expect to obtain something of value after a cost-benefit analysis of a
transaction.

  * •

Control: concerns about control over personal information, including approval,
modification of collected data, and opportunity to opt-in or opt-out from data
collection [Malhotra et al. (2004)]. It is related to the perceived fairness
of the procedures that maintain personal data.

  * •

Awareness: concerns about personal awareness of organizational information
practices [Malhotra et al. (2004)]. It relates to issues of transparency of
the procedures and specificity of information to be used.

A 10-item questionnaire to assess these constructs was validated in [Malhotra
et al. (2004)]. The questionnaire has been widely used to this day [Yun et al.
(2019), Raber and Krüger (2018)] because it considers the Internet context,
and it can explain more variance in a person’s willingness to transact than
CFIP [Rowan and Dehlinger (2014)]. Recent work has explored text mining as an
alternative research method to identify IUIPC dimensions. raber2018privacy
found that IUIPC dimensions can be derived from written text. They observed a
correlation between IUIPC concerns, as measured by the questionnaire, and LIWC
language features of social media posts from a sample of 100 users.

####  2.1.3 Other instruments of assessment

The Westin-Harris Privacy Segmentation Index measures individuals’ attitudes
and concerns about privacy and how they vary over time [Kumaraguru and Cranor
(2005)] based on answers to three questions [Egelman and Peer (2015a),
Woodruff et al. (2014)]. It categorizes individuals into three groups
[Kumaraguru and Cranor (2005), Da Veiga (2018), Motiwalla et al. (2014)]:
Fundamentalists are highly concerned about sharing their data, protect their
personal information, prefer privacy controls over consumer-service benefits,
and are in favor of new privacy regulations; Pragmatists tend to seek a
balance between the advantages and disadvantages of sharing personal
information before arriving at a decision; Unconcerned users believe there is
a greater benefit to be derived from sharing their personal information, trust
organizations that collect their personal data and are the least protective of
their privacy.

The Westin-Harris’ index was introduced as a way to meaningfully classify
internet users based on their attitude toward privacy and their motivations to
disclose personal information [Torabi and Beznosov (2016)]. It has been used
for several decades. However, recent studies have raised questions about its
validity [Egelman and Peer (2015a)]. Prior work has failed to establish a
significant correlation between the Westin-Harris’ segmentation and context-
specific, privacy-related actual or intended behaviors [Consolvo et al.
(2005), Woodruff et al. (2014), Egelman and Peer (2015b)].

The existence of a mismatch between privacy concerns and privacy behaviors,
known as the “privacy paradox” [Kokolakis (2017), Dienlin and Trepte (2015)],
motivated the creation of a new measurement instrument. Buchanan’s Privacy
Concern scale aims to capture different aspects of the paradox.
buchanan2007development developed three privacy scales: two of them assess
privacy behavior, and the third one measures information privacy concerns.
However, some limitations have been identified. Their scales are not able to
identify different privacy dimensions, but only one, which appears to map onto
the general concept of privacy concern. Thus, a more fine-grained examination
is desirable to improve the design of this scale [Buchanan et al. (2007)].

Because our study focuses on people’s comments about a specific information
privacy scandal (and not their privacy behavior), our work will mostly build
upon the information privacy concerns frameworks, particularly the IUIPC.

###  2.2 Differences on information privacy concerns

Information privacy concerns can vary across individuals based on peoples’
perceptions and values [Buchanan et al. (2007)]. People may have different
concerns even if they experience the same situation [Lee et al. (2015)]. It
has been argued that information privacy concerns can be influenced by
different factors [Smith et al. (2011)], such as national culture [Cho et al.
(2009), Huang and Bashir (2016), Cao and Everard (2008), Krasnova and Veltri
(2010)], and individuals’ demographics (e.g., age, gender) [Zukowski and Brown
(2007), Lee et al. (2019), Jai and King (2016), Rowan and Dehlinger (2014),
Cho et al. (2009), Markos et al. (2017)]. We review these factors below.

####  2.2.1 National Culture

While there are similarities in what privacy means across cultures [Cockcroft
and Rekker (2016)], there is no universal consensus on its definition
[Cannataci (2009)]. According to newell1995perspectives, several cultures do
not possess an equivalent term to the English’ privacy definition in their own
language, e.g., Arabic, Dutch, Japanese, and Russian. Nevertheless, this does
not mean that these cultures lack a sense of privacy [Newell (1995)]. Every
society appreciates privacy in some way, but the expression of it varies [Cho
et al. (2009)].

The concept of national culture has been studied as one of the factors related
to information privacy concerns [Nov and Wattal (2009), Malhotra et al.
(2004), Bellman et al. (2004)]. National culture can be defined as “the
collective mindset distinguishing the member of one nation from another”[Cho
et al. (2009)]. Hofstede’s cultural dimensions theory [Hofstede (1983)] has
been the most used conceptual model to study cultural differences in this
context. This trend is expected since Hofstede’s theory has been widely used
to study the relationship between culture and technology [Leidner and Kayworth
(2006)], even though there are a number of criticisms of this theory
[Terlutter et al. (2006)]. The latest version of this theory proposes six
cultural dimensions [Hofstede (2011)]. Among them, the
individualism/collectivism dimension has been found relevant to information
privacy concerns. Individualism/collectivism refers to the extent to which
individuals are part of groups beyond their immediate families.

Differences in information privacy concerns have been explained using some
cultural dimensions at a country and regional level (see Table 1).
Participants from individualistic countries (Australia and United States)
exhibited a higher level of online privacy concerns than individuals from
collectivist countries [Cho et al. (2009)]. The authors’ rationale is that
high individualism is associated with an emphasis on private life and
independence from the collective; thus, people from individualist countries
are more worried about privacy intrusions. In the same direction,
bellman2004international found that controlling for internet experience and
privacy regulations, people from countries with high individualism show deeper
concern about two CFIP dimensions: unauthorized secondary use and improper
access.

On the other hand, no regional differences in privacy concerns were found
through online surveys with 226 English-fluent crowd workers from six regions
(Africa, Asia, Western Europe, Eastern Europe, North America, and Latin
America). The authors argued that it is unclear if their finding is due to
true similarities or a lack of enough power in measuring privacy concerns
[Huang and Bashir (2016)].

Table 1: Culture and information privacy concerns Independent variables |  Method |  # participants and origin |  Key findings
---|---|---|---
National culture |  5-item questionnaire [Cho et al. (2009)], based on a unidimensional conceptualization of online privacy concerns. Items were comprehensive enough to measure general concerns about online privacy. |  1261 from Seoul, Singapore, Bangalore, Sydney, New York |  Participants from individualistic countries exhibited higher concern about online privacy [Cho et al. (2009)]
National culture |  15-item questionnaire (CFIP) [Smith et al. (1996)], based on a multidimensional constructive model of privacy concerns (collection, unauthorized secondary use, improper access, errors). |  534 from 38 countries |  Participants from individualistic countries showed higher concern about improper access and secondary use [Bellman et al. (2004)]
Regional culture |  4-item questionnaire [Dinev and Hart (2006)], based on a unidimensional conceptualization of privacy concerns, which is defined as apprehension about how online personal information is used by others. |  226 from Africa, Asia, Western and Eastern Europe, North and Latin America |  No regional differences in privacy concerns were found [Huang and Bashir (2016)]

####  2.2.2 Language

Relatedly, the Sapir-Whorf hypothesis suggests that the structure of anyone’s
native language influences the world-views they will acquire [Kay and Kempton
(1984)]. Depending on the language, a message is coded and decoded differently
based on standardized language norms and culture [Zarifis et al. (2019)].
Thus, individuals who speak different native languages could think, perceive
reality and organize the world around them in different ways [Hussein (2012)].

Previous work has explored how user-generated content can reveal different
views about the same issues among people who write in different languages.
jiang2017mapping conducted a semantic network analysis to examine the semantic
differences that emerge from the Wikipedia articles about China. Results
suggest that Chinese-speaking and English-speaking contributors framed
articles about China in different and even opposite ways, which were aligned
to their national cultures and values. The Chinese version framed them from
perspectives of authority respect, emphasizing harmony and patriotism.
Articles in English were written from the point of view that is distinctive of
many Western societies: the core value of democracy.

A potential role of the spoken language in the information privacy context has
also been studied. li2017cross created a cross-cultural privacy prediction
model. The model applies supervised machine learning to predict users’
decisions on the collection of their personal data. Using answers from an
online survey of 9,625 individuals from 8 countries on four continents:
Canada, China, Germany, United States, United Kingdom, Sweden, Australia and,
India,they found that the model’s prediction accuracy improved when adding
individual’s language (English, Chinese, French, Swedish, and German) or
Hofstede’s cultural dimensions. Our work will build upon this line of
reasoning to deepen our understanding of information privacy concerns across
the globe.

####  2.2.3 Other individual characteristics

Even though our work will not address the relationship between demographics
and information privacy concerns, we will briefly review the literature about
this topic.

Prior studies suggest that older Internet users are more concerned about
online information privacy than younger ones [Cho et al. (2009)]. Older
participants were more sensitive to privacy issues and exhibited a greater
desire to control the amount of information collected about them [Zukowski and
Brown (2007)]. In contrast, younger users declared themselves to be more
willing to share their personal information with third parties [Jai and King
(2016)].

The relation between privacy concerns and gender has also been studied [Cho et
al. (2009)]. jai2016privacy found that women were less willing than men to
permit third parties to share their personal information. Similarly,
rowan2014observed observed that women reported greater information privacy
concerns than their male counterparts. Both studies considered gender as
binary.

Another relevant factor is participants’ internet experience. As users grow in
internet experience, concerns for online information privacy may decrease
[Zukowski and Brown (2007)]. bellman2004international concluded that
participants with more internet experience were less concerned about online
privacy overall, and in particular, were less worried about improper access
and secondary use. This could be explained by increased familiarity with
online privacy practices [Zukowski and Brown (2007)].

##  3 Research Questions

Overall, while concepts around information privacy concerns have been
extensively investigated, some limitations are shared among the studies that
assess differences in these concerns worldwide. Most research has been
conducted through surveys and has focused only on a few geographic regions,
with a notable exception of [Li et al. (2017)]. Many studies have had a
limited sample size [Vitkauskaite (2010), Ur and Wang (2013), Ebert et al.
(2020), Chai (2020), Li et al. (2020), Oghazi et al. (2020), Krasnova and
Veltri (2010)]; thus, their findings’ generalizability has been questioned
[Lee et al. (2019)]. Moreover, when information privacy concerns
questionnaires are delivered in English to speakers of other languages, key
differences among countries may be obscured, as has happened with other cross-
national research [Harzing and Maznevski (2002), Harzing (2006)].
Unfortunately, conducting larger-scale, multi-country, and multi-language
surveys can be quite expensive [Harzing (2005), Li et al. (2020)]. Yet, large-
scale research to deepen our understanding of information privacy concerns
worldwide is still needed [Vitkauskaite (2010), Chai (2020), Li et al. (2020),
Zou et al. (2018), Oghazi et al. (2020), Okazaki et al. (2020)].

We seek to assess the feasibility of using social media data to identify
information privacy concerns and characterize language and regional
differences. Twitter is a popular micro-blogging service where individuals
from different world regions who speak diverse languages share opinions,
information, and experiences [Yaqub et al. (2017), Shen and Kuo (2014)].
Mining text from this platform has been used as a fast and inexpensive method
to gather opinions from individuals [O’Connor et al. (2010)], which can
complement findings obtained from traditional polls or other research methods.
Prior research has found a significant correlation between tweets and public
opinion in diverse domains [O’Connor et al. (2010), Tumasjan et al. (2010),
Ilyas et al. (2020), Tavoschi et al. (2020)]. Following this trend of
research, we aim to investigate whether Twitter data can reveal people’s
information privacy concerns. Thus, our first research question is as follows:

  * •

RQ1: Which information privacy concerns are present over social media content
about a data-breach scandal?

As we have reviewed in the prior section, there are arguments and evidence to
support that information privacy concerns can vary across culture, language,
and demographics [Chai (2020), Li et al. (2020), Oghazi et al. (2020), Oghazi
et al. (2020), González et al. (2019a)]. If information privacy concerns are
present in a Twitter dataset, we could explore how they differ across people
who live in different parts of the world and those who speak different
languages. As we do not expect any specific trend of differences, we propose
to test the following null hypotheses:

  * •

H0a. There are no differences in information privacy concerns by language

  * •

H0b. There are no differences in information privacy concerns by world region.

##  4 Data & Methods

To answer our research question and test the hypotheses, we implemented a
four-step methodology (see Fig 1). We retrieved tweets associated with data
privacy during a specific period (4.1. data collection). We filtered the data,
removing retweets and excluding tweets likely to be generated by bots (4.2.
data pre-processing). We created word-embeddings (a multi-dimensional
representation of a corpus) for the remaining tweets according to their
language and world region (4.3. text mining). Finally, we conducted an
analysis to identify similarities and differences in the semantic contexts of
privacy keywords in the word embeddings (4.4. coding and analysis). Details
about each of these steps are presented below.

Figure 1: Methodology flow chart

###  4.1 Data collection

We retrieved tweets related to the Facebook and Cambridge Analytica scandal
between April 1st and July 10th, 2018. We focused on tweets in Spanish and
English.

On March 17, 2018, it was revealed that the data firm Cambridge Analytica used
personal data of 87 million Facebook users for political advertising purposes
without their consent [Schneble et al. (2018), Oghazi et al. (2020)]. This
scandal caused the closure of Cambridge Analytica [Solon and Laughland (2018)]
and numerous lawsuits against Facebook in the USA and the European Union. On
Twitter, a #DeleteFacebook campaign started as a response to this scandal [Lin
(2018)]. As the Cambridge Analytica scandal triggered Twitter users from
different world regions (who speak diverse languages) to spontaneously share
their opinions, experiences, and perspectives about data privacy, we decided
to use a sample of these tweets to answer our research question and test our
hypotheses.

We used Tweepy222http://www.tweepy.org/ to collect relevant tweets. Tweepy is
a Python library for accessing the standard real-time streaming Twitter
API,333https://developer.twitter.com/en/docs/tweets/filter-
realtime/guides/basic-stream-parameters.html which allows to freely retrieve
tweets that match a given query. If the query is too broad that it includes
over 1% of the total number of tweets posted at that time worldwide, the
query’s response is sampled [Aghababaei and Makrehchi (2017), Morstatter et
al. (2014)]. The way in which Twitter samples the data is unpublished.
Nevertheless, studies have shown that as more data from the API is retrieved,
a more representative sample of the Twitter stream is obtained [Leetaru
(2019), Morstatter et al. (2013)].

To obtain relevant tweets, we used Tweepy’s language filter to retrieve tweets
in Spanish or English. We manually crafted a list of hashtags and keywords
related to the Cambridge Analytica scandal. We collected tweets that had at
least one of these terms. Examples of these terms are: “#DeleteFacebook”,
“#CambridgeAnalytica”, “#Mark Zuckerberg”,“Facebook”, “Facebook Cambridge”,
and “Facebook data breach”. Additionally, when appropriate, we added
translations to Spanish of these terms to build the Spanish dataset.444The
authors are fairly confident of the quality of these translations because some
of them are Spanish native speakers while others are English native speakers
In this way, if a tweet in Spanish had a hashtag in English, the tweet was
collected and added to the Spanish dataset. A full list of the terms used to
retrieve our data is available online555https://github.com/gonzalezf/Regional-
Differences-on-Information-Privacy-Concerns.

Following this procedure, we retrieved more than $470,000$ tweets in Spanish
and more than 7.4 million tweets written in English (see Table 2). The tweets
in Spanish were produced by approximately 220,000 users while tweets in
English were generated by about 1.8 million unique Twitter accounts.

Table 2: Datasets before and after data cleaning Dataset | Spanish | English
---|---|---
| #Tweets | #Accounts | #Tweets | #Accounts
Total | 472,363 | 222,352 | 7,476,988 | 1,846,542
Original | 106,656 | 47,951 | 1,572,371 | 574,452
With Botometer score | 100,606 | 44,182 | 1,442,112 | 504,214
Human-owned | 74,644 | 36,056 | 975,678 | 410,180

###  4.2 Data pre-processing

As we meant to analyze people’s opinions about information privacy, we decided
to pre-process our data in three ways. We removed all retweets to avoid
analyzing exact duplicates. Afterwards, we sought to identify and filter out
tweets that were generated by bots. Our last step was to associate tweets with
different world regions. We further explain each of these steps below.

First, we excluded retweets to avoid analyzing exact duplicates of content.
This methodology step is suggested by several authors [Hajjem and Latiri
(2017), Agüero-Torales et al. (2021), Singh et al. (2020)].  We kept tweets,
quoted tweets, and replies to tweets. Exclusion of retweets reduced our
datasets’ size by 80%. We refer to the resulting datasets as original tweets
(see Table 2).

We used Botometer [Davis et al. (2016)] to detect and remove tweets created by
bots. Botometer uses machine-learning to analyse more than one thousand
features [Badawy et al. (2018)] including tweets’ content and sentiment,
accounts’ and friends’ metadata, retweet/mention network structure, and time
series of activity [Varol et al. (2017), Yang et al. (2019)] to generate a
score that ranges from 0 to 1. A higher value suggests a high likelihood that
an inspected account is a bot [Badawy et al. (2018)]. This tool has reached
high accuracy (94%) in predicting both simple and sophisticated bots [Varol et
al. (2017), Badawy et al. (2018)]. Botometer is free and has been widely
used666Since its release in May 2014, Botometer has served over one million
requests [Davis et al. (2016)] via its website (https://botometer.iuni.iu.edu)
and its Python API (https://github.com/IUNetSci/botometer-python) [Varol et
al. (2017), Yang et al. (2019)].

Botometer processed all of the Twitter accounts who wrote original tweets. It
returned a score for 44,182 (92.14%) and 504,214 (87.77%) accounts of the
Spanish and English datasets, respectively. Botometer cannot generate scores
for suspended accounts or those that have their tweets protected. We decided
to remove the tweets from these accounts from our datasets because we cannot
confidently claim that they come from humans’ accounts. We applied the Ckmeans
[Wang and Song (2011)] algorithm to define a threshold to distinguish between
humans’ and bots’ accounts. For each language, we clustered the Botometer
scores into five groups, where the first group included the accounts with the
lowest scores (more human-like) and the fifth group comprised those with the
highest scores (more bot-like). After manually inspecting the accounts around
the thresholds of each group, we concluded that the fourth and fifth groups in
each dataset were unlikely to contain human accounts. Therefore, we used the
fourth group’s lowest threshold to discriminate humans’ and bots’ accounts.
Accounts with a score lower than 0.4745 and 0.4947 in Spanish and English,
respectively, were considered as human-owned. These thresholds are similar to
those used in related work, where scores lower than 0.5 had been considered as
humans [Varol et al. (2017), Badawy et al. (2018)]. As a result, our datasets
contain 36,056 human-owned accounts that created 74,644 tweets in Spanish and
410,180 accounts that created 975,678 tweets in English.

Finally, we used the GeoNames API777http://www.geonames.org/ to identify the
country of residence of Twitter users in our datasets. On Twitter, users can
self-report their city or country of precedence. Nevertheless, textual
references to geographic locations can be ambiguous. For example, over 60
different places around the world are named “Paris” [Jackoway et al. (2011)].
To deal with this challenge, we employed the GeoNames API, which is a
collaborative gazetteer project that contains more than 11 million entries and
alternate names for locations around the world in a variety of different
languages [Bergsma et al. (2013)]. Given a text, its algorithm performs
operations to recognize potential locations, followed by a disambiguation
process. This last step checks hierarchical relations and picks a location by
their proximity to other locations mentioned in the text [Lagos (2017)]. This
tool has yielded results with an accuracy above 80% [Jackoway et al. (2011)].

We found that 80.68% of users in our Spanish dataset and 78.68% of users in
our English dataset had filled the city or country fields in their profiles.
However, the GeoNames API could not detect the users’ location in several
cases, for example when inaccurate information was provided (e.g., “Planet
Earth.. where everyone else is from”, “Mars”). Nonetheless, the tool was able
to identify the location of users who created 58.7% of the Spanish tweets and
59.9% of the English ones. In the Spanish dataset, most tweets came from Spain
(16.5%) and Latin American countries, such as Mexico (11.9%), Argentina
(6.2%), and Venezuela (4.7%). In the English dataset, the majority of tweets
came from the United States (32.4%), followed by United Kingdom (6.9%), India
(3.2%), and Canada (2.7%) (see Table 3).

Table 3: Top-10 most frequent user locations in the Spanish and English
datasets

Spanish English Tweets Users Tweets Users Location # % # % Location # % # %
Spain 12,342 16.5 5,483 15.2 U.S 315,913 32.4 132,155 32.2 Mexico 8,852 11.9
4,720 13.1 U.K 66,901 6.9 29,656 7.2 Argentina 4,648 6.2 2,505 6.9 India
30,781 3.2 12,424 3.0 Venezuela 3,518 4.7 1,094 3.0 Canada 26,487 2.7 11,564
2.8 Colombia 2,447 3.3 1,348 3.7 Australia 13,375 1.4 6,501 1.6 U.S 1,823 2.4
948 2.6 Germany 9,260 0.9 3,493 0.9 Chile 1,806 2.4 1,073 3.0 France 8,605 0.9
3,006 0.7 Peru 1,116 1.5 619 1.7 Nigeria 5,156 0.5 2,787 0.7 Ecuador 893 1.2
455 1.3 U.A.E 5,120 0.5 1,504 0.4 Brazil 587 0.8 172 0.5 South Africa 4,962
0.5 2,912 0.7 Other 5,815 7.8 3,100 8.6 Other 98,100 10.1 42,658 10.4 Unknown
30,797 41.3 14,574 40.4 Unknown 391,018 40.1 161,767 39.4

To compare information privacy concerns by geographical regions, we divided
the Spanish Twitter dataset in two sets: tweets written by users from (1)
Latin America and (2) Europe. Similarly, we categorized the English dataset
into three groups: tweets written by users from (1) North America, (2) Europe
and (3) Asia. As a result, five different language-regional datasets were
generated. The Spanish-Latin America dataset includes 27,839 tweets written by
13,937 users. The Spanish-Europe dataset comprises 12,799 tweets created by
5,774 accounts. Regarding the English data, the North America dataset includes
342,400 tweets generated by 142,719 users, the English-Europe one has 111,745
tweets of 46,927 users, and the English-Asia dataset contains 42,208 tweets
produced by 17,929 accounts (Table 4). We did not consider other subsets
because of their small size. In Spanish, we only collected 1,929 tweets from
North America and 217 tweets from Asia. In English, we only collected 3,851
tweets from Latin America.

Table 4: Tweets and users in each dataset Language | Region | # of tweets | # of users
---|---|---|---
Spanish | Latin America | 27,839 | 13,937
Europe | 12,799 | 5,774
English | North America | 342,400 | 143,719
Europe | 111,745 | 46,927
Asia | 42,208 | 17,929

###  4.3 Text mining: Word embeddings to identify semantic contexts

We employed word embeddings [Mikolov et al. (2013)] to characterize the
semantic context in which privacy-related keywords are framed. Based on co-
occurrence of terms, word embeddings create a reduced multi-dimensional
representation of a corpus. Such representation can be used to analyze the
semantic proximity among the corpus’ terms. Analyzing the closest terms of a
given term can reveal the semantic context in which it is used [Rho et al.
(2018), González et al. (2019a)].

We created a set of word embeddings to enable cross-language and cross-
regional comparisons. First, we built word embeddings for the Spanish and
English datasets (containing both geolocated and non-geolocated tweets). Then,
we generated word embeddings for each of our five language-regional datasets.
Before creating the word embeddings, we transformed the text to lowercase. We
also removed stop-words and digits from the tweets. We customized our stop-
words to ensure that symbols like “#” were removed but not the words that
follow it. Links and usernames were removed. Words with total frequency lower
than three were ignored. These steps downsized the vocabulary by approximately
67% (details in Table 5).

Table 5: Initial and final vocabulary size in each dataset Language | Region | Initial vocabulary size | Final vocabulary size
---|---|---|---
Spanish | All | 65,036 | 21,736
Latin America | 35,149 | 11,359
Europe | 21,630 | 6,696
English | All | 244,371 | 76,128
North America | 115,710 | 41,109
Europe | 66,042 | 23,514
Asia | 39,120 | 13,896

We considered eight word embedding architecture combinations that involve
Word2Vec/FastText, CBOW/Skipgram and different numbers of dimensions and
epochs. As there is still no consensus about which word embedding evaluation
method is more adequate [Wang et al. (2019)], we evaluated each word embedding
architecture for the English dataset over 18 intrinsic conscious evaluation
methods [Wang et al. (2019)] using a word embedding benchmark
library.888https://github.com/kudkudak/word-embeddings-benchmarks
bakarov2018survey approach has categorized the evaluation methods in three
categories:

  * •

Word semantic similarity (WSS): RW, MEN, Mturk287, WS353R, WS353S, WS353,
SimLex999, RG65 and TR9856

  * •

Word Analogy (WA): Google Analogy Test set, MSR and SemEval 2012-2

  * •

Concept categorization (CC): AP, BLESS, BM, ESSLI 1A, ESSLI 2B, and ESSLI 2C

To choose the best architecture, we designed a point system to reflect the
embeddings’ performance. For each evaluation method, the word embedding with
the highest accuracy received a score of 8 points, the embedding with the
second highest accuracy was assigned 7 points, and so on. After running all
evaluation methods, we summed the points obtained for each architecture.
Considering a negative sampling and windows size parameters equal to 5, a
Word2Vec CBOW architecture with 300 dimensions trained during 50 epochs
achieved the total highest score (see Table 6). The same architecture had the
best performance for all English regional datasets. Given that these
evaluation methods are not available for a Spanish corpus, the same
architecture was used to create all the Spanish word embeddings.

Table 6: Word embedding architectures and their evaluation scores. Best performance is indicated with bold font style. Architecture | Evaluation word embedding score
---|---
Type | Model | Dim. | Epochs | WSS | WA | CC | Total
FastText | CBOW | 100 | 10 | 22 | 19 | 25 | 66
Word2Vec | Skipgram | 100 | 10 | 40 | 4 | 35 | 79
Word2Vec | CBOW | 100 | 10 | 35 | 11 | 33 | 79
Word2Vec | CBOW | 100 | 50 | 34 | 16 | 41 | 91
Word2Vec | CBOW | 100 | 300 | 33 | 9 | 31 | 73
Word2Vec | CBOW | 300 | 10 | 40 | 18 | 32 | 90
Word2Vec | CBOW | 300 | 50 | 53 | 21 | 36 | 110
Word2Vec | CBOW | 300 | 300 | 31 | 10 | 29 | 70

Previous work has reported that word embeddings can reflect gender bias as a
result of social constructs embedded in the data [Zhao et al. (2018), Jha and
Mamidi (2017)]. To reduce gender bias while preserving its useful properties
such as the ability to cluster related concepts, we followed bolukbasi2016man
approach. This is a post-processing method that projects gender-neutral words
to a subspace which is perpendicular to a gender dimension, defined by a set
of terms associated with gender such as girl, boy, mother and father [Zhao et
al. (2018)]. We applied the following procedure to our English embeddings: (1)
we identified a gender subspace selecting pairs of English words that can
reflect a gender direction in each word embedding such as woman-man, daughter-
son and female-male, (2) we ensured that gender neutral words are zero in the
gender subspace, and (3) we made neutral words equidistant to all pair of
terms contained in a collection of equality sets. A equality set is composed
by a pair of words that should differ only in the gender component such as
{grandmother, grandfather} and {guy, gal}. During this process, we used the
English terms suggested by bolukbasi2016man. For the Spanish word embeddings,
we used Google Translate API999https://cloud.google.com/translate/ to
translate the same terms.

###  4.4 Manual coding & analysis

We conducted a systematic qualitative examination of the semantic contexts in
which information privacy terms appear according to the word embeddings.
First, we conducted open coding of the semantic neighborhoods of privacy-
related keywords. After several iterations, we developed a set of categories
to characterize them. To assess if information privacy concerns were present
(RQ1), we contrasted these categories to a widely accepted framework to
describe internet users’ information privacy concerns.

We focused our investigation on four keywords in English: information,
privacy, users and company. We used their corresponding translations in
Spanish: información, privacidad, usuarios and empresa. We chose to include
information and privacy because they are the main concepts under study. We
could have added data; however, its semantic context is almost identical to
that of information. Thus, adding it would have resulted in a mere duplication
of terms. To increase the size of our dataset, we decided to add users and
company because of their key roles in respect of controlling and safeguarding
personal information. We also considered these terms more specific to the
vocabulary of the data privacy domain than alternative ones (e.g., people,
organizations).

For each embedding, we retrieved the closest terms to the four keywords.
Closeness between each term and a keyword was measured using cosine
similarity. For instance, the closest terms retrieved to the keyword
information in the English word embedding were info, data, details, and
personal, in that order. We chose to study the 40 closest terms after careful
examination of the lists of close terms according to our different embeddings.
After the 40th position in these lists, we rarely found terms that were even
slightly related to information privacy. We reason that the value of this
threshold is dataset-dependent. It is likely to be related to the vocabulary
sizes (ours range from 6,696 to 41,109). In our case, we opted for using 40 as
the threshold to study the semantic context of each keyword. Hence, we
qualitatively analyzed 160 terms for each embedding. Overall, our dataset for
qualitative analysis included 1,120 terms.

Two of the authors conducted open coding of the 320 terms retrieved from the
Spanish and English word embeddings. Open coding is a process to identify,
define and develop categories based on properties and dimensions of raw data
[Williams and Moser (2019)]. We used this technique to identify distinct
concepts and themes from the extracted terms [Williams and Moser (2019)].
After inspecting the retrieved terms during several iterations, the coders
developed a coding guideline with multiple concept categories and their
corresponding explanations to classify the retrieved terms. For example, the
term info extracted from the keyword information was categorized as a
synonymous, given that we can attribute to it the same meaning. The terms data
and details were classified as data & information, and personal was labeled as
attribute or characteristic. During a series of meetings, both coders compared
their categorization process and refined a common coding guideline,
establishing rules that would increase the categorization’s reliability. The
goal during this process is to segregate, group, regroup and re-link the terms
to consolidate meaning and explanation of the categories [Williams and Moser
(2019)].

At the end of this process, 15 categories emerged from the data (see Table
10). Considering the four keywords, an inter-coder reliability measure
(Cohen’s kappa) of 0.685 and 0.754 were obtained for the Spanish and English
dataset, respectively. These scores indicate substantial agreement [Viera et
al. (2005)] during the process.

We repeated the procedure for the regional datasets. The coders categorized
the 40 closest terms to the keywords according to the coding guideline.
Through an iterative process, a total of 800 words were manually coded. No new
categories emerged from the data. On average, a Cohen’s kappa above 0.722 was
obtained in all the regional datasets.

Table 7: Inter-rater reliability (Cohen’s kappa) score by dataset

Language | Region | Information | Privacy | Company | Users | Avg. by dataset
---|---|---|---|---|---|---
Spanish | All | 0.864 | 0.630 | 0.604 | 0.642 | 0.685
Latin America | 0.749 | 0.673 | 0.687 | 0.778 | 0.722
Europe | 0.820 | 0.710 | 0.774 | 0.827 | 0.783
English | All | 0.768 | 0.747 | 0.672 | 0.829 | 0.754
North America | 0.831 | 0.721 | 0.912 | 0.971 | 0.859
Europe | 0.777 | 0.743 | 0.805 | 0.807 | 0.783
Asia | 0.832 | 0.685 | 0.736 | 0.833 | 0.771
Average of all embeddings | 0.806 | 0.701 | 0.741 | 0.812 | 0.765

To assess if information privacy concerns were present in a Twitter dataset
about a data-breach scandal (RQ1), we compared the resulting categories with
the IUIPC’s dimensions: collection, control, and awareness. IUIPC is a theory-
based model that has been widely used to study information privacy concerns on
the internet (see Section 2.1.2). Then, we tested the null hypotheses about
differences in information privacy concerns across language and world regions
(H0a and H0b). To do so, we used a Chi-squared test to assess if the
proportion of terms in the semantic contexts were significantly different
across word embeddings. In all of these tests, we accounted for multiple
comparisons by applying alpha adjustment according to Šidák  [Šidák (1967),
Haynes (2013)]. This method allowed us to control the probability of making
false discoveries when performing multiple hypotheses tests.

##  5 Results

In this section, we address our research question and test the null hypotheses
about differences in information privacy concerns by language and world
regions.

As explained above, we create word embeddings for our Spanish and English
datasets of tweets about the Cambridge Analytica scandal. Then, we take a
closer examination of how the semantic context of four keywords varies across
language and world regions. The semantic context is operationalized as the 40
closest terms of each keyword: information, privacy, company, and users. As an
example, Table 8 and Table 9 show the 20 closest terms to the keywords,
according to the Spanish and English word embeddings.101010Terms in Spanish
were translated to English by the authors Full results are available
online.111111https://github.com/gonzalezf/Regional-Differences-on-Information-
Privacy-Concerns

Table 8: Top 20 closest terms to information and privacy in the Spanish and English word embeddings Information | Privacy
---|---
Spanish | English | Spanish | English
data | info | intimacy | data privacy
info | data | data | gdpr
third parties | details | confidentiality | protection
fact | personal | scams | users
third | users | personal data | user
interviewer | profiles | privacy policy | consumers
facebook | identifiers | digital security | data
users | personal data | data protection | transparency
privacy | private | identity | facebook
consent | records | minor | personal
authorization | user | facebook | consent
purposes | consent | third parties | security
private | advertisers | information | sharing
personal | permission | cibersecurity | data protection
location | metadata | sensitive | tos
ecomlancer | datas | emails | consumer
serve | companies | cookies | collection
viatec | individuals | protect yourself | opt
intimate | freely | suppose | trust
profiles | informations | take care of your data | privacyrights
Table 9: Top 20 closest terms to ‘company and users in the Spanish and English word embeddings Company | Users
---|---
Spanish | English | Spanish | English
company | companies | third parties | user
consultant | firm | sensitive | consumers
firm | companys | citizens | personal
organization | platform | illegally | peoples
obtain | firms | users | subscribers
relation | organization | authorization | customers
researcher | data | profiles | people
deliver | entity | private | facebook
plot | giant | used | data
finance | user | people | fb
ltd | facebook | illegal | apps
way | corporation | clients | individuals
facebook | fb | user | advertisers
illegally | organisation | improperly | privacy
companies | business | obtained | information
ca | users | nametests | app
own | businesses | information | citizens
brand | service | facebook | profiles
decide | ca | data | companies
scl | site | voters | collected
creole | personal | cambridgeanalytics | private
relations | organizations | infringement | consent
cambridge | employees | use | accounts
data | agency | purposes | permissions
laboratories | co | authorized | use

###  5.1 Information privacy concerns present in a Twitter dataset

As a result of the coding process, we define 15 categories to analyze the
closest terms (see Table 10). To answer our first research question , we
compare our categories with IUIPC, a framework widely used to measure
information privacy concerns in the context of the Internet [Liu and Carter
(2018)]. We find relationships among some of our categories and the three
IUIPC concepts as well as our initial keywords, as shown in Figure 2.

Table 10: Coding guideline to classify the semantic contexts of our keywords. Yellow background is for categories that match our initial keywords. Light blue background is for categories related to IUIPC. Gray background is for other categories. Categories |  Description |  Spanish Examples |  English Examples
---|---|---|---
Data & Information |  Direct references to these concepts and examples of user data and its meaning |  records, location, data, emails, profiles, contacts |  profile, messages, documents, location, accounts, metadata
Companies |  Entities that manipulate user data for their own purposes |  facebook, cambridgeanalytica, scl, grindr, advertisers |  facebook, cambridgeanalytica, google, emerdata, apple
Users |  Data owners |  users, consumers, population, citizens, people |  users, consumers, subscribers, citizens, people
Data collection, handling and/or storage |  Mechanisms and verbs associated with obtaining, collecting and handling data |  use, obtained, log in, collecting, apps, mechanisms |  collected, store, access, databases, usage, analyzed
Ownership agency |  User-control over personal information |  authorization, agree, consent, protect |  consent, opt, shield, autonomy
Privacy & security terms |  Words associated with data privacy and security |  cybersecurity, confidentiality, intimacy, safe, secure |  confidenciality, transparency, privately, dataprivacy, security
Security mechanisms |  Tools and techniques that implement security services |  credentials, password, biometry, key |  encrypted, password, biometric, privacybydesign
Privacy & security risks |  Entities or bad practices that can compromise sensitive data |  trojan, cybercriminal, illegally, scams, stealing |  grooming, databreach, misuse, illegally, violated
Regulation |  Law, rule or regulation that controls the use of user data |  rgpd, right to be forgotten, privacypolicy, iso, habeasdata |  gdpr, tos, hippa, privacyrights, regulation
Synonymous |  Same meaning than the keyword |  info, company, firm, user, private |  info, companies, firm, privacy, user,
Attribute or characteristic |  A characteristic of the keyword |  false, private, external, specialized, britain |  sensitive, giant, holistic, strategic, affected
Action |  Action or activity linked to the keyword |  define, explode, promote, attend, move |  order, solve, reveal, update, confirming
Third party |  Can not be categorized as User or Company. There is not sufficient contextual information to do so |  rrhh, medicians, interviewer, ex employee, philippine |  government, agency, indians, europeans, developers,
Reaction or attitude |  Way of feeling or acting toward a entity |  guilt, honest, overfall, suffers, handle |  willingly, freely, tighter, restricting, forced
Undetermined |  Relationship between keyword and term is unknown |  approximately, v.i.p, ground, higher, depth |  psychological, millions, new, group, image
Figure 2: Relationships between our categories and IUIPC dimensions

Three categories match our initial keywords (Table 10, yellow background): (1)
data & information is associated with the information keyword, including
direct references to this concept and examples of user data and its meaning
(e.g.,“messages” and “metadata”), (2) companies include terms about
organizations that use personal data for their own purposes such as “Facebook”
and “Apple”, and (3) users contain references to this keyword (e.g.,
“customers”, “people”).

Five categories are related to IUIPC (Table 10, light blue background). We
identify a data collection, handling and/or storage category that contains
words associated with technology or techniques useful to obtain, collect or
handle data (e.g: “databases”, “services”, “app”, “website”). This matches to
the IUIPC’s collection dimension, which refers to the “degree to which a
person is concerned about the amount of individual-specific data possessed by
others relative to the value of benefits received”[Malhotra et al. (2004)].
Examples of tweets that include terms that fit this category are:

> ‘$@$hidden_username $@$hidden_username This is bigger than facebook because
> all social media outlets collect and store this data on every user. If you
> haven’t looked, check and see what twitter has collected on you. Free apps
> are not free, neither are paid ones’

> ‘Facebook collects and sells PII data. Google and others maintain behavioral
> data anonymously and serve ads against it, but don’t connect that data to
> identities that are sold to advertisers. I was not aware Facebook was such
> an anomaly.’

The IUIPC’s control dimension denotes concerns about control over personal
information. This is often exercised through approval, modification and
opportunity to opt-in or opt-out [Malhotra et al. (2004)]. Terms related to
this dimension appeared in the coding phase (e.g., “consent”, “opt”,
“permission”) and were categorized as ownership agency. This category also
includes advice directed to users and good privacy practices terms (e.g.,
“prevent”, “protect”). Examples of tweets related to this category are:

> ‘If anything we should learn from the #Facebook data breach. Don’t volunteer
> information and prevent that secondary data collection by using #adblocker
> and #VPN’

> ‘Cambridge Analytica whistleblower Christopher Wylie urges U.S. senators to
> focus less on data consent and more on the idea that it’s almost impossible
> to opt out of, for example, Google.’

The third IUIPC’s dimension is awareness, which refers to individual concerns
about her/his awareness of organizational information privacy practices
[Malhotra et al. (2004)]. Three of our categories are associated with this
dimension: (1) privacy and security terms that include words associated with
data privacy and security such as “confidentiality”, “transparency” and
“safety”; (2) security mechanisms that refers to tools and techniques that
implement security services (e.g., “password”, “encryption”); and, (3) privacy
& security risks that denote entities or bad practices that can compromise
sensitive data, for example: “troyano” (trojan), “databreach”, “grooming” and
“ciberdelincuente” (cybercriminal). Tweets that use these terms are:

> ‘Hmm- what do you think? I forsee a wave of new social network startups-
> will any be able to rise? Besides privacy and transparency what else would
> you want from a social network? #swtech’

> ‘WhatsApp Co-Founder To Leave Company Amid Disagreements With Facebook.
> Facebook’s desire to weaken WhatsApp’s encryption and collect more personal
> data reportedly fueled the decision’

> ‘Canadian federal privacy officials warned that third-party developers’
> access to Facebook users’ personal information raises serious privacy risks
> back in 2009. $@$hidden_link’

Another privacy-related category emerges from our coding but can not be easily
associated with an IUIPC dimension. This is the regulation category, which
includes terms associated with laws and rules that control the use of personal
data such as “gdpr” in reference to the European General Data Protection
Regulation or “tos” in reference to Terms of Services. Examples of tweets with
these terms are:

> ‘New regulation in Europe called gdpr makes companies liable for data
> breaches with penalties which include fines of a percentage of global
> turnover. It feels like all Zuckerberg is liable for is a slap on the wrist
> and having to apologise in public’

> ‘#Today we are confirming that multiple snippets of data from CI that was
> lifted from facebook are in Russia. If you are an EU citizen this means you
> have a right to sue both companies for gdpr based infringements. We will be
> leading this cause should no one else step up….’

> ‘Senator to #Zuckerberg: Your terms of services are only a few pages long.
> People complain when online contracts are too long and filled with legalese.
> Now lawmakers are complaining they’re too short. What’s the threshold for
> length and detail, and how do we decide?’

Other categories are identified as well (Table 10, gray background). The
attribute or characteristic category contains modifiers of a specific keyword.
For example, the term “sensitive” emerges from the closest terms to
information, and the term “britanica” (British) appears among the nearest
terms to company. The action category includes words related to an act. For
instance, the verbs “obtener” (obtain) and “entregar” (deliver) come out among
the closest terms to company. The third party category contains terms related
to entities that can not be categorized as user or company because there is
not sufficient contextual information to do so, such as “indians”, “third”,
“individuals” and “americans”. Additionally, the reaction or attitude category
comprises terms that represent a way of feeling or acting toward a person,
thing or situation. For example, the terms “deny” and “admitted” are present
in the closest terms to company. A synonymous category emerge during the
process as well. This contains equivalent terms to each keyword. For example,
the terms “info” and “informations” are close to information and the terms
“companys”, “corporation”, “companies” and “firm” appear among the closest
terms to company. Terms with no clear relation to the keywords were classified
as undetermined.

Figure 3: This force-directed graph represents the open coding categories
related to the keyword privacy and provides examples of the terms that were
coded as each category. Categories with the higher frequency are larger and
closer to the keyword. Yellow nodes represent keywords and light-blue nodes
denote privacy-related categories.

We used force-directed graphs [Kobourov (2013)] to represent all the
categories that emerged from the analysis of semantic context of each keyword.
Figure 3 shows the categories related to the keyword privacy. In this graph,
distance represents closeness in the semantic context. For example, terms that
were categorized as regulation are closer to privacy than terms that were
categorized as security mechanisms. Additionally, the visualization shows
examples of terms in each category in Spanish or English.121212Terms in
Spanish were translated to English by the authors. These terms are shown in
cursive Force-directed graphs of the categories and terms associated with the
other keywords (information, company, and users) are available
online.131313https://andreafigue.github.io/word_embeddings/visualization.html

Overall, we observe that the semantic contexts of four privacy-related
keywords include terms corresponding to information privacy concerns. We
illustrate such presence in Figure 2. We positioned each IUIPC dimension at
the intersection between two of our keywords. Companies carry out collection,
handling and or storage activities regarding data & information. Users
exercise (some) agency over the control of their data & information. The
awareness dimension arises from the users’ perception of the companies’
practices. Our results suggest that the awareness dimension might be further
categorized into sub-topics, such as awareness of privacy and security terms,
security mechanisms, and privacy and security risks.

Beyond what the IUIPC model proposes, we find that regulations are relevant to
Twitter users who talk about Cambridge Analytica. We position this concept
close to awareness, as it is considered an environmental factor that relates
to information privacy concerns [Lee et al. (2019), Zou et al. (2018),
Mohammed and Tejay (2017)] but is not integrated into the IUIPC.

###  5.2 Emphasis on information privacy concerns across languages and world
regions

As we are able to observe the presence of information privacy concerns on the
Twitter datasets, we can now turn to test the null hypotheses regarding
differences across language and world regions. We compare the emphasis on
information privacy concerns (IPC) in the semantic contexts that emerge from
the different word embeddings. Figure 4 reports the distribution of terms that
relate to the initial keywords (Table 10, yellow background) and IPC (Table
10, light blue background) in each language and world region under study.
Others include all remaining categories.

To test our hypotheses, we performed Chi-square goodness-of-fit tests. Because
we ran multiple tests, we applied Šidák correction to counteract the problem
of multiple comparisons, thus controlling the family-wise error rate.
According to our Šidák ’s adjustment, to maintain an overall alpha of 0.05 for
the collection of 10 tests, null hypotheses can be rejected when $p<0.0102$.

(a) By Language

(b) By world-region

Figure 4: Proportion of categories by language (a) and world region (b)

We find no significant differences on the emphasis on information privacy
concerns across languages or regions (see Table 11). Thus, we cannot reject
the null hypotheses. We conclude that IPC are present at similar rates in
Spanish and English. They cover a considerable proportion of the semantic
contexts, with more than 30% of terms in both languages. Considering the
regional datasets, IPC describes between 20% and 40% of the terms. While we
observe some variation in emphasis on IPC across regions, with the largest
proportion in the Latin American dataset and the smallest fraction in the
Asian data, the differences across regions are not enough to be statistically
significant.

The rest of the terms are better described by our initial categories, such as
company, information and users. Compared to the IPC category, all of them
cover smaller fractions of the semantic contexts under study. It should also
be noted that irrelevant categories (grouped as others) add up to large
proportions in all datasets, ranging from 30% to more than 60%. Together,
these results reveal that while a social media dataset about a data breach
scandal does bring relevant content about information privacy concerns, this
comes with a fair amount of noisy content.

Table 11: Results of Chi-squared tests to compare proportions of terms by language and world regions. The null hypothesis was rejected if $p<.0102$ Null hypothesis | $\chi^{2}$ | N | DF |  $p$ value
---|---|---|---|---
There is no difference in % of IPC terms between languages | 0.15 | 110 | 1 | .70
There is no difference in % of IPC terms among world regions | 8.04 | 237 | 4 | .09
There is no difference in % of collection terms between languages | 11.65 | 31 | 1 | <.001
There is no difference in % of collection terms among world regions | 10.97 | 68 | 4 | .03
There is no difference in % of control terms between languages | 0.00 | 24 | 1 | 1.00
There is no difference in % of control terms among world regions | 7.15 | 33 | 4 | .13
There is no difference in % of awareness terms between languages | 4.12 | 41 | 1 | .04
There is no difference in % of awareness terms among world regions | 13.58 | 95 | 4 | .009
There is no difference in % of regulation terms between languages | 0.69 | 13 | 1 | .41
There is no difference in % of regulation terms among world regions | 11.69 | 26 | 4 | .02

####  5.2.1 IUIPC dimensions

Digging deeper in the terms related to information privacy concerns, we
analyze the proportions of terms that match each IUIPC dimension across
languages and world regions (see Figure 5 and Table 11).

We observe a broader emphasis on collection in English
($\chi^{2}(1,31)=11.65,p=<.001$) than in Spanish. Cohen’s effect size value (w
= $.61$) suggests that this is a high practical significance [Cohen (1988)].
Even though this pattern seems to be influenced by a higher proportion on
collection in the English content from North America than in any other region
(Figure 5), regional differences are not statistically significant after
multiple comparisons correction ($\chi^{2}(4,68)=10.97,p=.03$).

In turn, while we cannot reject a null hypothesis regarding differences on
awareness by language after corrections ($\chi^{2}(1,41)=4.12,p=.04$), we find
a significant difference across world regions ($\chi^{2}(4,95)=13.58,p=.009$).
Cohen’s effect size value (w = .38) suggests a moderate to high practical
significance. Here, we calculated the standard residuals to determine which
world regions make the greater contribution to this chi-square test result. We
find that compared with other world regions, data in English from North
America have a smaller ratio of awareness terms (chi-square standard residual
= $-2.56$). The opposite is found in data in Spanish from Latin America (chi-
square standard residual = $2.05$).

Finally, we find no evidence to reject the null hypothesis regarding control.
Control is equally present in both languages and the regions under study.

(a) By Language

(b) By world-region

Figure 5: Proportion of terms related to the IUIPC dimensions by language (a)
and region (b)

####  5.2.2 Regulation

Even though the concept of regulation is not part of the IUIPC dimensions,
prior literature [Cockcroft and Rekker (2016), Lee et al. (2019), Da Veiga
(2018)] has suggested that it is related to people’s concerns about
information privacy. We find terms associated with this category in all our
word embeddings (see Figure 6). However, the difference in proportions between
Spanish and English data is not statistically significant
($\chi^{2}(1,13)=0.69,p=.41$). Likewise, we do not find enough evidence to
reject the null hypothesis regarding differences across world regions after
multiple comparison correction ($\chi^{2}(4,26)=11.69,p=.02$) (see Table 11).

(a) By Language

(b) By world-region

Figure 6: Proportion of terms associated to regulations by language (a) and
region (b)

##  6 Discussion

This work explores the potential of social media as a data source to study
cross-language and cross-regional differences in information privacy concerns.
We conduct an analysis of Twitter data related to a particular data breach
news to deepen our understanding of how people from different world regions
and who speak different languages frame privacy concerns. We chose to focus on
the Cambridge Analytica scandal because it triggered a wide-ranging exchange
on social media about user information and companies’ data practices. We build
upon the potential of word embeddings to derive a semantic context of each
term in a corpus. The contexts are built according to terms that are commonly
used in the same phrases. By characterizing a keyword’s nearby terms, we seek
to reveal the context in which a keyword was discussed [Rho et al. (2018)].
Based on more than a million non-duplicated, human-generated tweets, we
generate word embeddings for data in Spanish and English and for data from
Latin America, North America, Asia and Europe. For each embedding, we conduct
a qualitative analysis of the semantic contexts of four privacy-related
keywords: information, privacy, company, and users.

Collecting and analyzing the semantic contexts of these privacy-related
keywords allows us to observe the presence of terms related to information
privacy concerns in the collected tweets. Through iterative manual coding, we
characterize the semantic contexts using 15 categories. Several of these
categories are easily mapped to the three dimensions of the Internet User
Information Privacy Concerns (IUIPC): collection, awareness, and control (See
Figure 2). In this way, we find evidence that social media content can reveal
information about privacy concerns.

Our approach takes into consideration a vast amount of online content posted
freely and spontaneously on Twitter to create the semantic context of each
keyword. Thus, it gives a sense of a collective perspective on information
privacy concerns by language and world region, which can become a
complementary approach to current survey-based methods. Our method aims to
discover knowledge from a large-scale social media dataset in a topic for
which a ground truth does not exist. Unfortunately, such ground truth is
unlikely to exist because large-scale, multi-country, and multi-language
surveys are too expensive to conduct [Li et al. (2020)]. As an alternative
approach, we used word embeddings to find the semantic contexts of relevant
keywords and followed a qualitative approach to validate the results. We
carefully analyzed more than a thousand terms of the semantic contexts,
conducted open coding to formulate a data-grounded categorization, and then
contrasted our categorization with IUIPC [Malhotra et al. (2004)], one of the
well-accepted theoretical conceptualizations of information privacy concerns.
While this is not the common ground truth of other natural language processing
tasks such as classification, our process draws from qualitative approaches to
validate the results of an automated text analysis. We discuss below how our
findings extend our current understanding of privacy concerns and open new
lines of inquiry.

Beyond matching content to current conceptualizations of information privacy
concerns, our results suggest a more granular categorization of one of them.
Our results hint that awareness might include more specific sub-topics that
users can be aware of, such as privacy and security terms (e.g.,
cybersecurity, confidentiality), security mechanisms (e.g., credentials,
encrypted), and privacy and security risks (e.g., scams, grooming). The
presence of terms that fit these categories reveals that they are already part
of public online conversations around privacy. A distinction among broad
privacy and security terms, mechanisms to protect data and potential data
risks might be useful to further describe the kinds of knowledge people have.
Additionally, awareness about some of these subtopics might be more
influential than others. For example, knowing about risks and mechanisms might
be a sign of higher privacy concerns while knowing broad privacy and security
terms might not. The distinction between sub-topics could also guide users’,
educators’ and practitioners’ efforts to enhance information privacy literacy.
Future work can explore the relevance of this distinction and its implications
for information privacy practices.

Besides, the presence of the regulation category highlights its importance in
relation to information privacy concerns. Regulation refers to laws or rules
that aim to regulate the use of personal data. They have often been considered
a factor influencing information privacy concerns[Ebert et al. (2020),
Benamati et al. (2021)]. The emergence of this category from our open coding
confirms its relevance through its frequent appearance in public posts about a
data breach scandal. Such relevance might be related to the elaboration of
laws and public policies about data usage worldwide. These regulations are not
only a topic of data and law experts, but it seems to be part of the public
discourse around data privacy online. It is noticeable the common presence of
a specific regulation, the GDPR, in our datasets.  GDPR is a privacy
regulation that has been in effect since late May 2018 in the European Union
[Gellman (2019)]. Our data collection period covered the early months of its
implementation. This regulation prohibits processing and exploiting personal
data such as health status, political orientation, sexual preferences,
religious beliefs, and ethnic origin. Thus, it aims to decrease the privacy
risks that may derive from malicious use of such information, including cases
like the Cambridge Analytica scandal [Cabañas et al. (2018)]. GDPR seeks to
convert individuals into empowered citizens involved in the decision-making
process related to their personal information [Karampela et al. (2019)]. As an
example, with this regulation in effect, companies are required to inform
individuals about their rights (e.g., restriction of processing, erasure of
data), the storage period of data, and additional sources that have been used
to acquire personal data [Ebert et al. (2020)]. The explicit presence of GDPR
in our data might be evidence of its influence on shaping people’s arguments
about privacy concerns and its importance not only in Europe but worldwide.
Further work can focus on exploring how to integrate better the role of
regulations into the current conceptualizations of information privacy
concerns, which were proposed long before data privacy regulations were as
common as they are now around the globe. Moreover, future work could also
explore the interaction between regulations and specific information privacy
concerns dimensions.

While we find similar rates of terms related to information privacy concerns
across languages and regions, we observe significant differences in emphasis
on collection and awareness. These results indicate that different groups view
the Cambridge Analytica scandal from a particular standpoint. It is important
to notice though that while information privacy terms appear through our
method, they also come along with a considerable amount of other terms that we
consider noisy data. Nevertheless, our findings show the potential of using
social media data for cross-language and cross-regional comparisons to
identify similarities and nuanced differences on privacy-related perspectives
worldwide.

Our analysis reveals that the semantic contexts generated by tweets written in
English have significantly more terms related to collection than those written
in Spanish. This is a novel finding. When freely expressing online about
privacy keywords, English speakers give significantly more emphasis to data
collection than Spanish speakers. This difference can lead researchers and
practitioners to explore the effectiveness of more tailored data privacy
campaigns to specific populations. For example, populations that are more
concerned about collection might need more information about the benefits of
sharing their information to be able to make a decision about it. A high
emphasis on collection in English is also congruent with prior literature
observing that college students from the USA are more worried about collection
of personal information than control over it [Yang (2013)]. Exploring if this
trend is shared by people from other English speaking countries can help
clarifying which of these patterns are better explained by location or
language.

Future work can explore why we observe a significant language difference in
emphasis on collection. A feasible explanation might be related to the users’
country of residence. Note that our tweets in English come mainly from the USA
and UK. Both were the countries most closely connected to the Cambridge
Analytica scandal due to the misuse of data for political campaigns in the
USA’s 2016 presidential election and Brexit [Cadwalladr and Graham-Harrison
(2018)]. It is possible that this shared experience resulted in a larger
emphasis on collection in the English than the Spanish data. An alternative
hypothesis is associated with differences in regulations. Information privacy
concerns might be a reflection of customer privacy regulations in their
respective countries [Markos et al. (2017), Kumar and Reinartz (2012)]. In
contrast to European countries, that have adopted a data protection directive
from a government-imposed perspective, the USA has followed an industry self-
regulation [Kumar and Reinartz (2012)]. Considering that companies have more
freedom to collect and process personal data in North America, it would be
reasonable that data collection practices are of deeper concern to individuals
from North America than those in Europe. This could also be supported by our
data when observing that North America has the highest proportion of terms
related to company (see Figure 4), which we also found in our prior work using
a different text mining method and a smaller dataset [González et al.
(2019b)]. However, our data analysis does not support the hypothesis of
regional differences. It is possible that our data does not have enough power
given the multiple comparisons we conducted. Future research is needed to
explore alternative hypothesis that can explain the broader emphasis on
collection among English speakers, compared to Spanish speakers.

We also observe significant regional differences on awareness. Particularly,
data from North America shows the smallest emphasis on awareness while Latin
America has the highest. Given that most studies on information privacy
concerns are centered on the USA, this finding is particularly important. It
warns us against the (sometimes implicit) assumption that North American data
about privacy concerns can be generalizable to other regions. At least
regarding emphasis on awareness, we find evidence that data from the USA is
not similar to other regions. Thus, this result provides observational
evidence to argue that it is necessary to include more diverse populations to
have more a accurate understanding of the phenomena around data privacy. This
finding also invites practitioners to address other regions, such as Latin
America, using more different approaches in their terms of services and
privacy policies. Populations that are more concerned about awareness might be
more receptive to companies that use more transparent communications of their
use of personal data, for example.

It is worth noting that Latin American shows the largest emphasis on
awareness. Our results provide evidence of a disconnection between Latin
America and North America regarding this aspect. It is possible that this
broad interest on awareness can be a reflection of a connection of Latin
America to the European perspective on data privacy. Latin America presents a
particular scenario. It lies between two different approaches to personal data
regulation: the principles contained by the European GDPR and the fragmented
framework of the USA, where data protection is divided by sector [Aguerre
(2019)]. Privacy regulations are considered an essential concern for many
Latin American countries, and after data privacy breaches such as the
Cambridge Analytica one, this issue has received increased attention in the
public opinion and policy spheres in the region [Aguerre (2019)]. Previously,
researchers have argued that GDPR could be one of the most influential pieces
of data protection legislation ever enacted with influence beyond Europe
[Kuner et al. (2017)]. Indeed, in Brazil, a new GDPR-like law (Lei Geral de
Proteção de Dados Pessoais, LGPD, in Portuguese) has become effective since
August 2020 [Dias Canedo et al. (2020)]. Future studies can explore
connections among data privacy regulations worldwide, how they relate to
public opinion on the issues of privacy, and how they are influenced by
national and international data breaches.

As we found regional but not language differences in emphasis on privacy
concerns, we conducted a follow-up analysis to assess whether there is a
language difference within a single region. Europe was the only region where
we had enough data in both languages to conduct such a comparison. We did not
find significant differences in emphasis of any IUIPC dimension between data
in English and Spanish from Europe ($\chi^{2}(3,92)=0.15,p=.98$). There was no
evidence of significant differences in emphasis on regulations either. Thus,
this additional analysis provides additional evidence to support that
information privacy concerns are more related to the region of residence than
the spoken language. Nevertheless, further research is required to understand
better the role of regulatory regimes, consumer practices, and economic
development factors on these differences [Okazaki et al. (2020)]. As the
Spanish-English balance in tweets in our dataset is such that it does not lend
itself to intra-region comparison for Asia and North and Latin America, future
work could seek to explore if this pattern repeats in those regions as well

As with any study, our research has limitations. We collected data through the
free standard streaming Twitter API using specific hashtags and keywords.
Thus, we only had access to a limited sample of all the tweets about the
scandal. We used Botometer to detect and remove tweets likely to be created by
bots. This tool can only analyze Twitter public accounts; therefore, it could
not be used on suspended accounts or those with their tweets protected when
running our analysis. We decided to remove these accounts’ tweets from our
datasets because we can not confidently claim that humans generated them.
Indeed, previous research suggests that it is likely that social bots were
present in this cohort [Heredia et al. (2018)]. Moreover, we focused our
investigation on four keywords in English: information, privacy, users, and
company and their corresponding translations to Spanish. While using synonyms
would have brought similar semantic contexts, adding more concepts can
strengthen the results. Future work can explore other keywords such as:
intimacy, and consumers.  Similarly, we did not use the terms user, and
companies as keywords. While word embeddings capture syntactic regularities
such as singular/plural forms [Mikolov et al. (2013), Yeşiltaş and Güngör
(2020)], we reason that this methodological decision should not have affected
considerably our results. Nevertheless, future work could include plural and
singular versions of the same term to confirm this hypothesis. The sample size
of our manual coding process (40 words per keyword in each embedding) could
have impacted the results. We chose the number of retrieved terms after
manually inspecting the list of nearest words by each keyword in all our
embeddings. We picked a threshold that allowed us to obtain a high number of
meaningful words in most embeddings. Higher thresholds make it more likely to
include terms with no apparent relation to the keywords (e.g., v.i.p; ground;
approximately). In word embeddings with reduced vocabularies like ours, the
number of relevant terms available for a specific keyword is limited. This
characteristic explains why the number of irrelevant terms (Other in Figure 3)
is high in datasets with small vocabularies, such as the Spanish and English-
Asia datasets. Future work could evaluate how sensitive our approach is to
changes in vocabulary size and threshold for the nearest terms. This decision
may introduce a bias in the results, and it is one of the limitations of our
approach of social media textual data.

##  7 Conclusion

We conducted a cross-language and cross-regional study on social media content
about a major data privacy leakage: the Cambridge Analytica scandal. We
categorized our Twitter data into two different languages and four
geographical regions. Our results shed light on language and regional
differences on information privacy concerns by 1) creating word embeddings by
language and world regions to leverage social media data about a data breach
scandal, 2) conducting open coding and content analysis of the semantic
contexts (generated by the embeddings) of privacy-related keywords, 3) mapping
the results to a well-known information privacy framework, and (4) conducting
a comparative analysis across two languages and four world regions.

We found that data in English shows a broader emphasis on data collection,
while data from North America shows the smallest emphasis on awareness. In
turn, data from Latin America has the broadest emphasis on awareness. We
discuss how our findings extend current conceptualizations of information
privacy concerns, and how they might relate to regulations about personal data
usage in the regions we analyzed.

Future work can dig deeper on the differences we observed and explore further
the potential causes we discussed. Future studies might build upon our work to
examine privacy concerns considering more languages, more geographical
locations or different information privacy frameworks. Using our methodology
to compare datasets across longer periods of time could be useful to determine
if the semantic contexts of the privacy keywords changes over time.

##  8 Acknowledgments

The authors want to thank Francisco Tobar, MSc. Computer Science student at
Universidad Técnica Federico Santa María, for helping us to strengthen our
findings through statistical analysis. Moreover, we acknowledge anonymous
reviewers for insightful comments that helped us revise and refine the paper.

##  9 Funding and conflicts of interests

This collaboration was possible thanks to the support of the Fulbright
Program, under a 2017-18 Fulbright Fellowship award. This work was also
partially funded by CONICYT Chile, under grant Conicyt-Fondecyt Iniciación
11161026. The first author acknowledges the support of the PIIC program from
Universidad Técnica Federico Santa María and CONICYT-
PFCHA/MagísterNacional/2019-22190332. The authors declare that there is no
conflict of interest regarding the publication of this paper.

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