# Designing and Testing a Mobile Application for Collecting WhatsApp Chat Data
While Preserving Privacy

Brennan Schaffner
University of Chicago  Archie Brohn
University of Chicago  Jason Chee
University of Chicago  K.J. Feng
Princeton University  Marshini Chetty
University of Chicago

###### Abstract

It is common practice for researchers to join public WhatsApp chats and scrape
their contents for analysis. However, research shows collecting data this way
contradicts user expectations and preferences, even if the data is effectively
public. To overcome these issues, we outline design considerations for
collecting WhatsApp chat data with improved user privacy by heightening user
control and oversight of data collection and taking care to minimize the data
researchers collect and process off a user’s device. We refer to these design
principles as User-Centered Data Sharing (UCDS). To evaluate our UCDS
principles, we implemented a mobile application representing one possible
instance of these improved data collection techniques and evaluated the
viability of using the app to collect WhatsApp chat data. Second, we surveyed
WhatsApp users to gather user perceptions on common existing WhatsApp data
collection methods as well as UCDS methods. Our results show that we were able
to glean similar informative insights into WhatsApp chats using UCDS
principles in our prototype app to common, less privacy-preserving methods.
Our survey showed that methods following the UCDS principles are preferred by
users because they offered users more control over the data collection
process. Future user studies could further expand upon UCDS principles to
overcome complications of researcher-to-group communication in research on
WhatsApp chats and evaluate these principles in other data sharing contexts.

##  1 Introduction

WhatsApp, the most popular mobile messenger application worldwide, has two
billion global active users [44]. Researchers have extensively studied
WhatsApp, often focusing on aspects such as the structure of sharing networks
[58] or the spread of election misinformation [17, 35, 39, 42, 43, 55]. To
study WhatsApp data, it has been common practice for researchers to join
public WhatsApp groups and scrape their contents for analysis. However, recent
work—on platforms other than WhatsApp—has shown that collecting public data in
this way may contradict the expectations and preferences of users or violate
the integrity of the researcher–public relationship, even if the content is
effectively public [16, 82, 12, 22, 36]. The notion of ‘public’ data is
further complicated by the fact that WhatsApp is end-to-end encrypted by
design, so users on the platform may expect a degree of privacy more so than
on the open Internet. In fact, contemporary privacy frameworks like contextual
integrity (CI) [45, 46] highlight the importance of upholding contextual norms
of information transfer, meaning scraping data from WhatsApp chats may violate
user privacy since it violates norms and expectations around data sharing in a
chat. Therefore, we as a research community should consider revisiting best
practices for collecting and analyzing WhatsApp data that may be more in line
with users’ expectations and wishes or risk eroding the public’s trust.

The benefits of studying WhatsApp data are clear, especially in the field of
misinformation. Journalists, fact-checking organizations, and academic
researchers have highlighted that WhatsApp is an effective pipeline for
misinformation [71, 53, 24, 65, 15]. Misinformation spreading on WhatsApp has
resulted in severe consequences, such as when fabricated content sparked
dozens of killings [29, 3], or when viral misleading content influenced a
presidential election [2]. Additionally, the platform serves as a primary
means of communication on the Internet for many. At least 95% of the
populations of Nigeria, South Africa, and Kenya use WhatsApp monthly [67].
Thus, WhatsApp’s two billion global active users [44] are at risk of consuming
misinformation on the same platform they use as a primary means of Internet
communication. Understanding misinformation on WhatsApp has direct importance
for suppressing its spread and mitigating its impact. These works that study
the spread and consequences of WhatsApp’s viral misleading content highlight
the need for continued research using WhatsApp data.

Yet, scraping and analyzing publicly accessible data has caused ethical
concerns, e.g., a majority of Twitter users report not knowing that
researchers use tweets and believing that researchers should have to obtain
permission to do so [16]. Investigating more privacy-preserving techniques for
collecting WhatsApp chat data would therefore benefit the research community.
Thus, we posed the following research questions:

  1. 1.

RQ1: How can we study WhatsApp chats in a way that gives users more control
over what data researchers collect?

  2. 2.

RQ2: What can we learn about WhatsApp chats using such a technique?

  3. 3.

RQ3: What do WhatsApp users think about different methods of collecting their
chat data?

To answer these questions, we first outline four principles for collecting
WhatsApp chat data that give users more control and oversight of the data
collection and ensure researchers minimize the data they collect and process
off a user’s device. We refer to these principles as the User-Centered Data
Sharing (UCDS) principles. Briefly, the principles are: Constrained Data
Collection restricts data collection to strictly necessary metadata; Local
Extraction and Processing minimizes the data researchers collect and process
off a user’s device; User Involvement gives participants control and oversight
of their data; and Transparency equips users with information needed to make
informed decisions about sharing their data. We intentionally use ‘data
sharing’ instead of ‘data collection’ to indicate the mutual relationship
between researchers and users unlike the single-sided notion of data
collection that asymmetrically empowers researchers. After outlining the UCDS
principles, we then created a mobile application for iOS called URL-EXTRACTOR-
APP following UCDS principles to evaluate whether UCDS still allows
researchers to glean useful insights from WhatsApp chats. Specifically, the
app allows WhatsApp users to voluntarily share limited content from their
WhatsApp chats and have control over the data being collected. We then
conducted a user study to evaluate the feasibility of using URL-EXTRACTOR-APP
to collect private chat data with 10 participants in the United States (US).
We focused on private WhatsApp chats for the feasibility deployment—as opposed
to larger public ones111We use the same distinction of private and public
chats as Feng et al. [15], where chats joined by users via direct
communication with a user’s existing mobile contacts are private WhatsApp
chats and chats where invite links are posted online so that anyone can join
are public chats.—since they potentially represent a stronger case of privacy
requirements as they are made up of more intimate relationships (friends and
family) and fewer members than the large public chats [63, 15]. Yet messages
from these chats can still propagate widely owing to WhatsApp message
forwarding [13]. Most crucially, private WhatsApp chats are largely
understudied yet make up a majority of WhatsApp chats [13]. From the initial
deployment of URL-EXTRACTOR-APP, we found that using the mobile application
that ran locally on users’ phones to extract metadata from WhatsApp chats was
a feasible technique for gathering WhatsApp chat data and making discoveries
about WhatsApp private chats’ contents while following UCDS principles. From
the collected data using UCDS, we were able to discern that a single chat
member consistently shared a majority of links in each chat, users shared
links relatively infrequently during the time period of chats in the dataset,
and shared links covered a variety of topics with YouTube and Google links
being the most common. Although our results are not generalizable owing to a
small sample, they demonstrate that our set of UCDS best practices for
gathering private WhatsApp data remain effective and that we can glean
valuable insights about private WhatsApp chats without explicitly collecting
all the chats’ available contents, such as the text messages or images sent in
the chat.

Second, we surveyed 334 WhatsApp users to gauge their perceptions of various
approaches to WhatsApp data collection including UCDS principles. The survey
results indicate that following UCDS principles improves upon common WhatsApp
data collection methods. Specifically, common existing data collection methods
that target and scrape public WhatsApp chats violate user expectations and
miss a majority of the WhatsApp messaging ecosystem by focusing on public
chats only. Participants preferred how methods aligning with UCDS principles
limited data collection to metadata, sanitized and anonymized the data, and
showed users what data was being collected all before it reached the
researchers. Our survey results also suggested that UCDS principles can be
expanded to address the complications of researcher-to-group communication.
Based on our findings, we make recommendations for improvements to best
practices in data collection techniques more broadly.

Our main contributions are summarized below:

  * •

We provide evidence that new data collection methods for WhatsApp are needed
for two reasons. First, corroborating prior work [63], we find that the most
popular usage of WhatsApp is among friends and family, suggesting that a
majority of WhatsApp content is in private chats. Therefore, existing methods
targeting publicly accessible chats may be missing a majority of WhatsApp
content. Second, analogous to Fiesler et al. finding that research using
public Twitter data violated user wishes [16], we show that WhatsApp users are
not comfortable with common WhatsApp research methods, indicating the
necessity of revisiting best practices.

  * •

We outlined UCDS principles by building on common privacy practices from prior
works for WhatsApp data collection [18, 27, 35, 58, 57, 37, 43, 59] and adding
additional ethical best practices including: restricting collection to
metadata, local data extraction and processing, increased participant control
over data collection, and explicit transparency in the data collection
methodology. We present a research method for collecting WhatsApp chat data
using a mobile app that exemplifies one possible implementation of UCDS
principles.

  * •

We demonstrate the effectiveness of UCDS principles through a feasibility
study of our app to collect private WhatsApp chat data while upholding
participant oversight of data collection, and present insights from an
exploratory dataset of private WhatsApp chat data from study participants in
the US.

  * •

We provide evidence that users support WhatsApp UCDS best practices for
sharing their WhatsApp chats.

##  2 Background and Related Work

We provide an overview of the WhatsApp platform and present common approaches
to WhatsApp data collection and uses of public WhatsApp chat data.

(a)

(b)

(c)

(d)

Figure 1: The process for exporting the text file logs from a WhatsApp chat
and an example of the resulting text file. WhatsApp’s homepage is shown in
(a), where users can select one of their chats. Then in (b) the user can
select to export the chat data from the chat’s options menu. Finally, the user
chooses how to export the chat in (c), linking to other apps that accept text
files. The resulting exported chat file is shown in (d).

###  2.1 WhatsApp: Private and Public Chats

WhatsApp, a mobile messaging application based in the US owned by Meta, was
released in 2009 (for iOS) and 2010 (for Android)[5, 60, 73]. WhatsApp
supports sharing images [48], audio messages [25], documents [51], and videos
[23], and WhatsApp contacts can also communicate via voice calls and video
meetings [11, 76]. Notably, all communication is encrypted by default [40].
The platform’s popularity is in part due to allowing users to send messages
over a device’s Internet connection so they can avoid expensive Short Message
Service (SMS) fees [26]. In 2019, there were 67.1 million US WhatsApp users
with projections of 85.8 million users in 2023 [68].

Conversations on WhatsApp take the form of “chats”. The platform allows users
to participate in multiple chats simultaneously. Users can be added to group
chats by a group administrator, or they can join chats via invite links
obtainable from existing chats. Additionally, they can create new chats and
add their WhatsApp contacts. Figure 1(a) shows an example private chat with a
friend (A) and public chat for fans of a popular football club (B). Websites
like WhatsApp Group Links [80] can host a list of ready-to-join public
WhatsApp groups (up to 257 members), often centered around a common interest
(e.g., political opinions, shopping deals, or dating). Another feature
provided by WhatsApp is the ability to export chat information. Due to the
forthcoming relevance of this feature, we outline the process in Figure 1.

###  2.2 Research Using Public WhatsApp Data

Prior academic work has extensively collected and analyzed WhatsApp chat data.
In 2018, Garimella and Tyson explored the feasibility of collecting and using
WhatsApp data [18]. They present an example methodology used to analyze a
random sample of 178 public group chats that they joined using online invite
links and automated web scraping tools. Like our study, this work explored
what WhatsApp group data can be collected and how it can be used. However, our
work differs in that it focuses on developing methods for collecting WhatsApp
chat data with additional privacy considerations.

Building on the feasibility of collecting and analyzing public WhatsApp group
data, some researchers have studied the network structures of the spread of
misinformation related to major social events [58] and elections [39, 42].
Other researchers have analyzed public WhatsApp groups for specific types of
harmful content such as “fear speech” [59] and incitements to violence in
India [1] or COVID-19 misinformation in Pakistan [27].

Some researchers have focused on a specific type of content (images, audio,
etc.) spreading amongst WhatsApp groups. A large portion of WhatsApp studies
focused on the spreading of image content [35, 43, 17, 55, 58]. For example,
Garimella and Eckles found that 13% of image shares contained misinformation
when analyzing politically oriented public WhatsApp groups in India.
Researchers have also examined text-based misinformation on WhatsApp. For
example, Resende et al. focused on text-only messages, finding that textual
misinformation spread quickly within social groups but took longer to bridge
into others [57]. Other studies analyzed the uniform resource locators (URLs)
[57, 35, 43], audio messages [37], or video content [35] shared in WhatsApp
chats. Some misinformation studies on WhatsApp did not directly analyze data
from WhatsApp but used interviews to probe about (mis)information sharing
dynamics [15, 28]. We extend prior work to explore how to collect and analyze
data from private WhatsApp chats without ever directly observing actual chat
messages and giving participants oversight of the data collection.

###  2.3 Collecting WhatsApp Data

Academics have broadly discussed the ethics of general online data collection
[16, 77, 72, 7, 41, 78, 49, 19] and web scraping/crawling [20, 33, 56, 34, 6,
31] on other platforms, but analogous research is lacking for WhatsApp,
perhaps due to the added complexity brought by its encryption. That is, even
though WhatsApp group invite links may be public, their message content is
still encrypted and may not be considered public to the same extent as
unencrypted public content on other platforms. This is demonstrated by the
fact that researchers must join public WhatsApp groups to access and scrape
their contents. WhatsApp researchers have weighed privacy considerations of
their data collection in a variety of ways. We identified three common
practices that researchers use to protect the privacy and wishes of their
participants. First, some researchers anonymize the personally identifiable
information (PII) of the WhatsApp chat members by creating cross references
between unique IDs and their identifiable information from their WhatsApp
accounts (e.g., names and phone numbers) [18, 27, 35, 58, 57, 37, 55, 43, 59,
62]. This protects users because researchers can delete phone numbers from
their datasets before analyzing the collected data. Second, some researchers
constrain the data collected from public WhatsApp chats limiting the
collection of inessential data, which may include PII. For example,
researchers may only collect a certain type of message such as images or news
stories [35, 43]. Third, some researchers announce their presence in the chats
from which they plan to begin scraping data by posting a message with their
intentions [35, 43]. These disclosure messages inform the chat members that
they can voice their concerns or request to opt-out of the study. In practice,
researchers have used all, some, or none [17, 39, 42, 64, 28] of these methods
in their WhatsApp studies.

While these three common practices strongly work in favor of participant
privacy, there are still some ethical concerns with the way the data is being
collected. The framework provided by these practices defaults WhatsApp users
into a position of trusting researchers. Even if researchers announce their
presence and tell users they can opt-out, how long is an acceptable amount of
time to wait for opt-out messages before moving forward with data collection?
Also, what happens if users do not see the message right away, and will
individual users that have issued opt-out requests trust researchers that
remain in the group chat to continue monitoring only the other chat messages?
Also, announcing researcher presence could have a chilling effect on the
natural discussion of the group chats [38], potentially affecting both the
quality of accurate data collection and regular WhatsApp users’ legitimate
experiences. Moreover, anonymity has notable limitations for participant
privacy. Data can be “de-anonymized” by malicious or fallible agents with
access to the cross-reference or from other attacks such as data
reconstruction (e.g., US Census data reconstruction [8]). While some privacy
frameworks such as differential privacy go beyond anonymity by adding
mathematically constructed noise to protect against such attacks, these
methods have not been used in existing WhatsApp research. Overall, WhatsApp
users’ oversight in data collection is largely excluded, justified by the data
being publicly accessible. Our work builds on prior approaches to WhatsApp
data collection to include users more actively in this process.

###  2.4 Relevant Privacy Frameworks

The most popular privacy frameworks relevant to this work include Privacy by
Design (PbD) [9] and Data Minimization (DM) [4], but these are broad guiding
principles for product designers with heterogeneous interpretations [4, 74] as
opposed to the methodological design considerations for research-driven data
collection that we put forth in this work. The most relevant contemporary
privacy framework to this work is Contextual Integrity (CI). From a CI
perspective [46, 45], common methods of WhatsApp data collection compromise
participant privacy by violating the contextual norms surrounding online group
chats. In fact, Internet researchers more broadly have found that collecting
public user data is fraught with ethical/methodological pitfalls because
contextual norms are violated [82, 12, 22, 36, 16, 77, 75, 72]. For example,
Zimmer found “considerable conceptual gaps in the understanding of the privacy
implications of research in social networking spaces” [82] with respect to
publicly released Facebook user data. Researchers have also found a lack of
consensus among Internal Review Board (IRB) staff regarding standards for
computational social science research, including “the challenge of obtaining
informed consent in large-scale data projects” [77]. To the best of our
knowledge, our work is unique in seeking user input regarding WhatsApp data
collection methodologies.

##  3 Study Part 1: Design Considerations and Feasibility

To answer RQ1, we first created four UCDS principles for collecting WhatsApp
chat data in a way that gives users more control over what data they share and
encourages researchers to minimize the data they collect and process off a
user’s device. To answer RQ2 and demonstrate the feasibility of deploying UCDS
principles for research purposes, we then implemented and evaluated one
possible instance of UCDS principles in the form of a mobile application with
a small-scale study: URL-EXTRACTOR-APP. The app design and usage by a user is
described in Figure 2.

(a)

(b)

(c)

(d)

(e)

Figure 2: Screenshots of URL-EXTRACTOR-APP’s navigation screens. A user could
either add an exported WhatsApp chat text file or share the file directly to
the app (a). Once the exported chats were shared to or imported into URL-
EXTRACTOR-APP, the app displayed a list of all shared chats. It is shown as
‘Chat 1’ in this example (b). The example metadata for a chat could be
verified (c). The list of URLs for the selected chat was displayed as a list
with an ‘X’ next to it (d). Clicking this ‘X’ deleted the URL from the chat’s
metadata. When a user clicked ‘Add to send’, the user could then enter the
email addresses for which to send the extracted chat along with the ability to
automatically send it to the research team (e).

###  3.1 User-Centered Data Sharing Principles

Here we introduce the UCDS principles in more detail and use the development
of URL-EXTRACTOR-APP as a recurring example implementation of each principle.
The principles build upon common practices mentioned in Section 2.3 to also
include: Constrained Data Collection, Local Extraction and Processing, User
Involvement, and Transparency. The full UCDS principles are detailed below. In
practical implementation, the principles often work hand in hand.

Constrained Data Collection To maximize understanding of the content contained
within private chats, it is tempting to collect all shared content: images,
texts, videos, documents, audio, and metadata. However, this data is likely to
contain PII typically exceeds the amount of data required for research
studies. Following Constrained Data Collection means only targeting data that
can be collected using privacy preserving methods and narrowing the data
collection scope to strictly necessary data for each study. For example, URL-
EXTRACTOR-APP did not extract the content of any message type unless it was a
URL. The app replaced usernames with coded identifiers (e.g., ‘User1’) so that
we could track how many people were in a chat and which users were sending
messages without recording identities. URL-EXTRACTOR-APP extracted only the
following data:

  * •

The start and end dates of the chat

  * •

The number of users in the chat

  * •

The number of messages from each user in a chat overall

  * •

The number of messages from each user per day

  * •

The number of URLS and text messages from each user

  * •

The message type (URL or text) of each message

  * •

The URLs in each chat

  * •

The anonymized sender of each message (e.g., John Doe and Jane Doe become
User0 and User1, respectively)

  * •

The date each message was sent

The data we collect is similar to the metadata that Meta collects from all
WhatsApp chats [14].

We designed the app to focus on URLs for three reasons. First, prior work has
shown that misinformation (a primary area of focus for WhatsApp researchers)
often spreads in the form of URLs [43, 35]. Second, shared URLs may operate as
a proxy for chat content, providing a glimpse into a chat’s discussion without
reading the private messages explicitly. Third, since images, texts, audio,
and video being sent can contain PII that is hard to scrub, we felt that
participants would be more comfortable with letting us collect URLs from their
chats. Upon collection, we stripped the URLs down to their domain name and
top-level domain (TLD) due to sensitive information that could be gathered
from the full-length URL, such as their online usernames or events they had
attended. For example, shared links to an online Zoom meeting or YouTube video
became simply “zoom.us” and “youtube.com”. We also kept country code top-level
domains (ccTLD) and followed redirections from URL shorteners. By reducing
links to their domain-level, we minimized the risk of violating the
participants’ privacy while still being able to characterize the types of
links shared. Even the constrained data collected by URL-EXTRACTOR-APP is more
than necessary for some research questions; it exemplifies types of minimally-
invasive yet useful chat data.

Local Extraction and Processing In UCDS, filtering the available data down to
the constrained data and performing any computations should occur locally on
the users’ devices insofar as it is possible. Following Local Extraction and
Processing gives participants full control over the extraction process and the
extracted data before any information is transferred off the device. Thus, the
risk of transferring potentially private information is minimized. In our URL-
EXTRACTOR-APP, to implement this UCDS principle, we deployed our app to study
participants so they could launch the data extraction from their WhatsApp
chats locally. In the case of URL-EXTRACTOR-APP, data collection via Local
Extraction removed the need for our research team to join any WhatsApp chats,
which is the primary means of public WhatsApp data collection and introduces
additional privacy concerns. Instead, the users could export their WhatsApp
chats and share them directly to URL-EXTRACTOR-APP. URL-EXTRACTOR-APP analyzed
the file, packaged the above data, and anonymized it fully locally.

User Involvement The principle of User Involvement seeks to balance the data
collection relationship between researchers and participants. Involving users
is enabled by conducting the extraction and data processing locally giving
users oversight and final say when it comes to what data is shared. In our
implementation example, URL-EXTRACTOR-APP allowed users to manage the data
extraction and choose whether to continue with sharing their chat data, on a
chat-by-chat basis. Specifically, users were shown the list of URLs and
metadata that the URL-EXTRACTOR-APP extracted from each respective WhatsApp
chat and were able to remove any URLs from the list that they did not want to
share (Figure 2(d)). As such, these URLs were not added to our dataset.
However, we recorded whether the list of a chat’s URLs had been edited or not
because we still wanted to know whether the extracted list of URLs was all or
a subset of the shared URLs. We also wanted to know whether participants would
edit the extracted data or share it with us as is in our feasibility study.

Transparency Following the principle of Transparency equips users with the
information they need in order to make informed decisions about sharing their
data. Transparency covers both the study details and ‘observation windows’
into the data collection procedures. For our implementation of URL-EXTRACTOR-
APP, we provided detailed documentation at ANONYMIZED-URL to users on how the
application was to be used, how it operated, and the motivations of our study
through a privacy policy sheet with frequently asked questions. We also
ensured that users could view the extracted data in its complete form by
offering the option for them to email the extracted chat file to anyone
(including themselves) as well as the research team. Participants were also
able to uncheck a box that sends the email to the research team if they wanted
to preview the file prior to sending it to the team.

###  3.2 App Implementation and Testing

We coded URL-EXTRACTOR-APP in JavaScript using the React Native framework and
published it to the Apple App store. To evaluate whether our specific
implementation of the UCDS principles (URL-EXTRACTOR-APP) was a feasible means
of collecting WhatsApp chat data, we conducted a small-scale deployment study.
Our user study was approved by the IRBs of our two institutions who also
checked that URL-EXTRACTOR-APP did not violate WhatsApp’s terms of service,
chat member’s rights, or wiretapping laws in our state. We recruited
participants using social media at both of our institutions in the US and a
recruitment survey center at one of our institutions between May and June
2021. Each participant completed a demographic survey (see Appendix B.1) and
reported their WhatsApp usage.

For each participant, we conducted a Zoom session that lasted 15–45 minutes
where we guided participants through using the application to share chat data
with the research team. We left the number of chats to share up to the
participant. We asked that they shared chats they participated in the most or
ones with high amounts of shared links. After the participants shared data
with us, we thanked them and concluded the session. Each participant was
compensated with a $15 gift card.

Data Analysis We analyzed the dataset of private WhatsApp chat data for chat
member dynamics such as how many members were in each chat and who sent each
link. Next, we looked at the emergence of URLs in the dataset by calculating
the frequency and occurrences of links being shared relative to text only
messages. Third, we analyzed what type of information was being shared by
analyzing the links’ domains. We also analyzed the TLDs and ccTLDs of each
link. In the dataset, the first user sending a message in a chat is User0,
incrementing the counter for each subsequent unique user—User1, User2, etc.
For each chat per participant, we label the first chat as A, the second as B,
and so on.

The participants had an uneven representation in the dataset because they
shared different numbers of chats which contain different amounts of messages.
To avoid skewed data in our analysis, we report medians. To calculate overall
medians, we first calculated medians across all chats for each participant.
Then, we calculated medians of the participants’ medians, to not skew data
analysis towards over-represented participants. For example, to calculate the
percentage of messages that contained URLs, we first calculated the median
percentage of messages containing URLs for all a participant’s chats. Then, we
take the median percentage across all participants.

Table 1: A summary of the participants and dataset gathered from the UCDS
feasibility study, including the number of chats collected by our app, how
long they have used WhatsApp, their number of WhatsApp contacts, the total
number of URLs in their chats, and the median durations of their chats.

Participant | # Chats |  | # Years Used
---
WhatsApp
| # WhatsApp
---
Contacts
| # URLs in
---
Chats
| Median Chat
---
Len. (months)
P1 | 3 | 7 | 105 | 194 | 20.6
P2 | 5 | 10 | 150 | 152 | 67.9
P3 | 5 | 5 | 400 | 140 | 26.4
P4 | 5 | 7 | 25 | 20 | 4.9
P5 | 3 | 5 | 82 | 71 | 13.7
P6 | 4 | 7 | 70 | 18 | 19.6
P7 | 1 | 7 | 70 | 30 | 3.7
P8 | 3 | 10 | 500 | 110 | 4.6
P9 | 4 | 3 | 24 | 23 | 1.1
P10 | 3 | 3 | 118 | 336 | 15.9
Median | 3.5 | 7 | 111.5 | 90.5 | 14.8

Participants 10 participants in total used URL-EXTRACTOR-APP to share chat
data with us. Our participants were younger, and many had higher education
degrees reporting varied incomes. Five participants were between ages 18-24
and five were 25-35 years old. Four had a bachelor’s degree, five had a
master’s degree and only one reported having only some college. Six were
female identifying and the rest were male identifying. Five reported making
$50K or less, three reported making ¿$50K and two declined to answer. The
number of chats for which participants used our app to automatically extract
data and shared with us ranged from one to five per participant, with a median
of 3.5. In total, participants shared data from 36 chats. We denote
participants in Study Part 1 using the notation P1–P10. The exploratory
dataset arising from the feasibility deployment is summarized in Table 1. It
consists of a wide range of chat lifetimes, from a six-day chat (P8) to an
almost six-year chat (P2; created in June of 2015). The median chat length in
our dataset was 1.23 years, and the median number of URLs shared in a chat was
15.

###  3.3 Study Part 1 Findings: UCDS Is Feasible

Study Part 1 demonstrates the ability to garner insights following the UCDS
principles. We use Garimella and Tyson as a recurring point of reference [18],
who conducted an early feasibility study of using WhatsApp data for research
collected from joining and scraping public chats. Note, our user study
resulted in a small-sized exploratory dataset and serves as a proof-of-concept
evaluation for methodologies following the User-Centered Data Sharing
principles; these findings may be sample dependent. We had three main insights
from the URL-EXTRACTOR-APP suggesting that research using UCDS principles is
not prohibitive for deriving research insights.

Participant Chat Dynamics. Over 80% of the chats in our dataset had only two
users, but there were several larger private chats with as many as 25 users.
The number of users for every chat in our dataset is presented in Appendix
A.1. Related work has found public chats to be much larger, with a median of
127 members [18]. Regardless of the chat size, a sole user was responsible for
a majority of the links shared in the private chats we examined over the
course of the chat’s lifetime. Across the 36 chats, the single user that sent
the most links was responsible for 64% of the links of a chat (including chats
with more than two members). Prior work where researchers joined and scraped
public chats found similar but less extreme skews towards a small portion of
disproportionately active users [18].

Table 2: The total number of messages in our dataset, including the number of
shared links and text-only messages.

# URLs | 1094
---|---
# Texts | 112,523
Total Messages | 113,617

Trends of Relatively Low URL Exposure. The median number of times a domain was
shared throughout a chat’s lifetime in our dataset was one, resulting in a
distribution containing a short peak of higher-shared domains followed by a
long tail of domains shared infrequently. Overall, URLs were uncommon in our
dataset of private chats and dwarfed by text messages (Table 2). By median of
medians, only 1.09% of all messages contained links, a much smaller portion
than what previous literature has shown in public group chats (39% [18]).

Varied Types of Shared Links. As listed in Table 3, the domains that were the
most shared across the 36 chats were YouTube (4.4%), Google (4.2%), and
Twitter (0.8%). These three were the only domains that appeared in at least
half of the chats for at least half of the participants. Notably, Garimella
and Tyson found YouTube to be the most popular domain in their dataset of
public WhatsApp chats as well [18]. 44% percent of chats contained links with
international country codes (e.g., ‘.mx’ (Mexico) and ‘.fr’ (France)). Note,
we excluded ccTLDs being used for aesthetic or brand appeal (e.g.,
‘twitch.tv’). Table 3 shows all the ccTLDs present in the dataset. Prior work
also found that 85% of public chats had members representing over 10 countries
[18].

###  3.4 Feasibility and Limitations

Our findings provide examples of how using UCDS methods for WhatsApp chat data
collection can still assist in answering a variety of research questions. The
many WhatsApp studies that operate by automatically joining and scraping
chats, which violate UCDS principles, use similar insights to answer their
research questions (albeit at larger scales). Study Part 1 suggests that
maintaining heightened participant privacy with UCDS methods is feasible for
research purposes. There are several limitations for using the development and
deployment of URL-EXTRACTOR-APP as a feasibility study for the UCDS
principles. For example, guiding participants through the app usage in Zoom
meetings was resource intensive, limiting the dataset to a smaller scale.
Also, the full reach of UCDS principles is difficult to measure from a
feasibility study in a single context. Future studies in other contexts may
highlight unforeseen parameters and require adjustments to the design of the
data sharing mechanisms. Additionally, the UCDS principles can be expanded
upon to fit study needs and learned user preferences as further elaborated in
Section 5.

Finally, recall that User Involvement allowed for users to edit their chat
data before we could analyze it. While we consider this a privacy enhancing
feature—that may increase user participation and thus data collection volume
in the long run—as opposed to a true limitation, it is worth noting that
providing this ability has the potential to skew takeaways. In Study Part 1,
only 3/10 participants edited their chats totaling 8/36 edited chats.

Table 3: The domain properties in our dataset across all 36 chats, including
the most popular domain names, most popular TLDs, and all ccTLDs present.

Top Domains | ccTLDs | Top TLDs
---|---|---
youtube (4.4%) |  [.mx, .uk, .us, .fr, .es, .eu, .ru, .nl, .py, .pk, .hu, .ca, .in] | .com (75.7%)
google (4.2%) | .org (4.5%)
twitter (0.8%) | .edu (1.2%)

##  4 Study Part 2: User Perceptions of WhatsApp Data Collection Methods

The findings of Study Part 1 demonstrated the feasibility of following the
heightened privacy-persevering UCDS principles for collecting WhatsApp chat
data and showed that insights about the private chats’ contents can be gleaned
such as what type of information was being shared, who shared it, and how
often it was shared. However, this study did not examine user perceptions
about the different methods for collecting WhatsApp chat data or UCDS
principles (RQ3). We deployed a survey in Study Part 2 to answer RQ3 and
better understand user expectations of privacy and contextual norms when
researchers are involved in WhatsApp chats.

###  4.1 Survey Design

To gather user perceptions of WhatsApp data collection methods including UCDS,
we designed a survey grouping questions into the following topics:222The full
survey instrument can be found in Appendix B.2.

  * •

WhatsApp Usage: After providing study information and obtaining consent, we
asked each participant about their general WhatsApp usage and contacts.

  * •

Data Sharing Comfort by Data Type: We then asked participants to indicate the
comfort level they felt towards sharing different categories of chat data with
researchers on a 5-point Likert scale (Extremely Comfortable to Extremely
Uncomfortable). We included a range of data categories from minimally invasive
data categories (e.g., the number of users in the chat) to PII (e.g., the
names of all chat members). The data categories were presented in a random
order while keeping similar categories next to each other to reduce cognitive
burden on the participants (e.g., whether a message contains a URL and the URL
itself would always be adjacent). When presenting results, we categorize the
different data types into Metadata, Message Contents, and PII.

  * •

Data Collection Scenarios: We then asked participants to answer questions
based on two hypothetical data collection scenarios: one representing current
techniques and one representing the UCDS principles from Study Part 1. In the
first scenario, a researcher joins a WhatsApp chat via a publicly accessible
invite link to collect the chat’s data (Figure 4). Since the invite link is
publicly available online, we consider the hypothetical chat in Scenario 1 a
public chat [15]. In the second scenario, a researcher is in contact with a
chat member but does not join the WhatsApp chat (Figure 4). Chats in Scenario
2 can be either private or public chats. URL-EXTRACTOR-APP (from Study Part 1)
is one possible instance of Scenario 2. Separating the researchers from the
chat allows for additional questions about how data filtering and
anonymization can occur in ways that are not possible in contemporary methods
of joining and scraping chats. Participants answered 5-point Likert-scale
questions related to both scenarios. The two scenarios were presented in a
random order, and each concluded with an open-ended question to provide any
additional thoughts about the scenario for scraping WhatsApp chat data.

  * •

Miscellaneous: Next, we asked participants a few miscellaneous questions
related to WhatsApp data collection such as their willingness to share data
for research purposes and trust levels towards their contacts and researchers.

  * •

Demographics: Lastly, we gathered demographic information including age,
education, and gender identity and asked participants to share any final open-
ended thoughts about WhatsApp data collection in general.

Table 4: Demographics of Study Part 2: age, education, gender, and annual
income.

Age | # | % | Education | # | % | Gender | # | % | Income | # | %
---|---|---|---|---|---|---|---|---|---|---|---
18-24 | 39 | 12 | High School | 22 | 7 | Female Identifying | 168 | 50 | <$25,000 | 67 | 20
25-34 | 144 | 43 | Some College | 56 | 17 | Male Identifying | 161 | 48 | $25,000-$50,000 | 86 | 26
35-44 | 90 | 27 | Associate Degree | 30 | 9 | Non-Binary | 2 | 1 | $50,000-$100,000 | 112 | 34
45-54 | 34 | 10 | Bachelor’s Degree | 154 | 46 | Pref. not to answer | 3 | 1 | $100,000-$200,000 | 53 | 16
55+ | 27 | 8 | Grad. or Prof. Degree | 70 | 21 |  |  |  | >$200,000 | 9 | 3
|  |  | Other | 2 | 1 |  |  |  | Pref. not to answer | 7 | 2

Scenario 1: Standard Method Imagine the following scenario: The invite link to
one of your WhatsApp chats is publicly available online, and a researcher uses
it to join the chat. Figure 3: Illustration provided to participants
explaining the standard methods for collecting WhatsApp chat data where
researchers join WhatsApp chats via invite links found online.

Scenario 2: UCDS Principles Imagine the following scenario: One of your
WhatsApp contacts is involved in a research study that is collecting chat
data, including data from a chat that you are also in. In this scenario, the
researchers do not join your chat but are in contact with one of the chat
members. Figure 4: Illustration provided to participants explaining the
scenario representing UCDS. Specifically, researchers do not join the chats
but externally interface with a chat member (e.g., via an application like
URL-EXTRACTOR-APP).

###  4.2 Survey Deployment and Analysis

We built the survey using Qualtrics [81] and piloted the survey with
volunteers familiar with HCI research to refine it for clarity and flow. We
received IRB approval and deployed the survey to 350 participants recruited
via Prolific [52]. We filtered for participants currently located in the US
that regularly use WhatsApp. We paid participants based on ANONYMIZED-STATE
minimum wage for 20 minutes of work (based on piloting response times). We
excluded a total of 16 responses for either failing one of the survey’s two
attention check questions or not qualifying for the study (e.g., a non-
WhatsApp user), resulting in 334 total responses. We denote participants in
Study Part 2 using the notation S1–S334. When reporting Likert results, we bin
together strongly and somewhat responses. That is, if a question had a
response breakdown of 44% Strongly Disagree, 21% Somewhat Disagree, 15%
Neutral, 15% Somewhat Agree, and 5% Strongly Agree, we would report this as
65% of participants disagreed and 20% agreed with the prompt, the rest being
neutral.

Survey Participants The survey participants’ age distribution approximately
matched the US WhatsApp population [10], and most participants (67%) had
completed at least a bachelor’s degree reporting a variety of incomes. The
full participant demographics for Study Part 2 are shown in Table 4. The
median number of years participants used WhatsApp was 5 years with a range of
less than a year to 13 years maximum.333WhatsApp was first available in 2009,
13 years prior to the time of survey deployment. The participants had a median
of 30 WhatsApp contacts. The minimum number of contacts reported was one, and
five participants checked a box indicating they had more than 500 WhatsApp
contacts.

###  4.3 Study Part 2 Findings: UCDS Is Preferred But Needs Improvement

From the survey of 334 WhatsApp users, we found that the UCDS principles
benefit users by putting them in control of the data collection process and
identified aspects that future iterations of best practices could address.
Specifically, the survey findings are summarized into three main themes: we
uncovered concerns with current approaches where researchers join and scrape
public WhatsApp chats; we found that following the UCDS principles to collect
WhatsApp chat data is well-suited for implementing users’ privacy preferences;
and complexities related to group consent remain unsolved by the UCDS
principles in their current form. We elaborate on these themes in the
following sections.

Figure 5: Participant comfort level sharing different types of WhatsApp chat
data, grouped by Metadata, Message Contents, and PII.

####  4.3.1 Concerns With Scraping Public WhatsApp Chats

The survey respondents indicated that most of their WhatsApp chats were
private chats with close contacts where no invite link was publicly available,
and that they were concerned about researchers collecting their chat data
without explicit consent even if the chat data were publicly accessible.

Most Whatsapp Chats Are Not Accessible by Online Public Invite Links. Survey
respondents indicated that the majority of their WhatsApp conversations were
private chats among family and friends; suggesting that research targeting
only publicly accessible WhatsApp chats [18, 27, 35, 37, 43, 57, 58, 59, 17,
39, 42, 55, 64] misses a lot of the WhatsApp ecosystem. When asked to
characterize their WhatsApp contacts, most participants (92.8%) said they had
contacts that were friends, followed by 78.7% that selected family
(participants could select multiple responses). One third (32.6%) had contacts
that were work colleagues, and only 15.3% said they were contacts with people
they only know from WhatsApp group chats. Similarly, the most common ways that
participants joined WhatsApp chats were from their friends and family directly
adding them to chats (93.7%) or by creating their own chats and adding their
contacts (63.7%). Only 11.7% selected that they joined WhatsApp chats by
public online invite links.

Users Do Not Want Data to Be Collected From ‘Public’ Whatsapp Chats Without
Their Consent. When asked about researchers joining a public chat to collect
WhatsApp chat data (Scenario 1), almost all participants (92.5%) agreed that
even if an invite link was publicly accessible online, that researchers should
inform the members of their presence and obtain consent from all chat members
before collecting data. Only 3% disagreed and 4.5% were neutral. Some
participants provided more detail about their reasoning, for instance: “I do
understand that ‘publicly available’ generally means accessible to anyone, and
that makes sense, but I still feel it would be best practice to inform
everyone involved of the intention to collect data, even if that could
compromise or alter behavior” (S27), and “Groups of friends talk in public and
its still considered rude if a stranger interjected into a conversation. Then
took a picture of you” (S248).

These results suggest that users would like researchers to announce presence
and intent even if the chat is technically publicly accessible. Respondent S49
gave context to the dissenting opinions which aim to preserve the original
nature of researched chats: “If researchers inform their intention, the
dynamic of the group chat might change and people might act differently and
unnaturally because they feel uncomfortable being studied or observed.”
Moreover, 83.5% of participants agreed that their willingness to share their
WhatsApp data with researchers depended on the goals of the research (9% were
neutral and 7.5% disagreed). For users to exercise their right to not
participate in research studies, they may want to know more about the
research. S83 explained: “I wouldn’t want them creeping in on my work
conversations. […] Like work documents I don’t think I would be to comfortable
with sharing, unless I knew what the research was for.” The high degree of
circumstance influencing comfort has also been shown for Twitter [16] and
Facebook [19] users.

####  4.3.2 UCDS Principles are Well-Suited for Implementing Users’ Privacy
Preferences

Our survey respondents reported that WhatsApp data should be filtered down to
just necessary metadata, anonymized, and given participant oversight before
researchers could even access this data. These preferences are made possible
by Scenario 2 and lend themselves to a method following the UCDS principles of
Study Part 1 where data extraction happens locally in the hands of WhatsApp
users.

Data Collected Should Be Limited to Metadata If Possible. When we asked
participants about their comfort level sharing the various types of data
associated with any WhatsApp chat, they were much more comfortable sharing
metadata over the actual chat contents. As shown in Figure 5, there is a steep
decrease in comfort when participants were asked about sharing a message’s
content itself versus the type of content contained in a message (i.e., text,
URL, media, or document) or other high-level chat metadata like the date the
chat started. Further, of actual message contents we asked about collecting,
participants were least uncomfortable sharing messaged URLs. Participants were
also most uncomfortable with sharing names or contact information with
researchers. In sum, participants preferred that only (and anonymous) metadata
be extracted as opposed to the full chat data. In contrast, common methods of
WhatsApp data collection scrape full chat data, sometimes including member
identities [17, 39, 42, 64]. In an open-response survey question, S15
concisely summarized this approach: “All images and audio should also be
extracted and left with just data that a image or audio existed there at one
point.” The next finding highlights user opinions regarding how (when) this
data extraction should occur.

Data Should Be Extracted and Anonymized Before It Reaches the Researchers.
Users preferred that the data be filtered down to the desired metadata even
before the researchers could access it. We informed the survey participants to
understand data extraction as the process of filtering the WhatsApp chat data
down to only what is needed serving both as saving resources and protecting
privacy by not collecting extraneous data. 52.7% of participants reported that
an app should be used to perform the WhatsApp data extraction before it is
sent to the researchers (30.6% disagreed and 16.8% neutral). In contrast,
21.0% reported the researchers should conduct the WhatsApp data extraction,
either on their own or with servers (59.9% disagreed and 19.2% neutral). For
instance, S328 shared: “I do believe their should be a system in place that
extracts any unneeded data before being gathered.”

Participants preferred that the data also be anonymized before researcher
access. That is, 92.6% reported (4.5% neutral and 3% disagreed) that personal
identifiers in WhatsApp chats should be anonymized prior to researcher access
versus the 70.1% that supported (12.3% neutral and 17.7% disagreed)
researchers anonymizing the WhatsApp data themselves. S71 explained: “I
believe any identifying data should be completely removed. Having it removed
automatically before researchers get it should be the way to have it done so
there is no potential to breach a person’s information.”

Participants Should See (And Possibly Edit) The Data Before It Is Sent to
Researchers. Almost every participant reported (92.9% agreed, 3% disagreed,
and 4.2% were neutral) that they should be able to view the data being
collected by the researchers. However, participants had less agreement when it
comes to the ability to edit extracted data. 52.1% agreed and 24.6% disagreed
with allowing users to edit data before it is sent to the researchers (23.4%
were neutral), indicating a slight majority supporting editable data. S70
explains their support: “the data owner should have full control over how much
is collected and how it us used.” Unfortunately, no participant directly spoke
to their disagreement with allowing data to be editable. A possible reason for
their position may be that they think it could make the data less accurate or
could be abused as a sort of ‘tampering.’

####  4.3.3 Collecting Chat Data With a Locally Running Application Raises
Concerns About Group Consent

When asked about consent, survey respondents indicated that every user in the
chat should be involved with the study when it comes to informed consent, not
just a sole study participant in contact with the research team and operating
on behalf of all chat members. This indicates that the UCDS principles should
be expanded further to meet user preferences related to researcher-to-group
communication.

Informed Consent Required From Every Chat Member. Almost every participant
(96.1%) agreed that even if only one chat member was directly involved in a
research study, they should receive consent from the other group members
before any data is collected. Only 1.5% disagreed, and the remaining 2.4% were
neutral. Even if the data were to be sufficiently anonymized and could not be
linked to any chat members, most participants still wanted all members to be
informed and provide consent, as S216 said: “The chat member should not send
anyone else’s data without their permission.” S264 elaborated that if other
members’ data is being used, then they should be treated like a normal study
participant: “It seems odd that the rest of the contacts in the group aren’t
being given agreement and disclosure forms that are usually given to each
participant in a study.”

Users May Not Trust Their Fellow Chat Members. Participants were split on
whether they trusted fellow chat members and their WhatsApp contacts to
appropriately share data with researchers. 41.0% of participants agreed that
they trusted their WhatsApp contacts versus 37.2% that disagreed (21.9% were
neutral). Being in a chat together did not mean users would allow one another
to share data on their behalf. S319 said that “This feels like a huge
violation of privacy and trust. If I found out that somebody I was writing to
was doing this, I wouldn’t write to them again. They can’t be trusted.”

###  4.4 Study Part 2 Limitations

Study Part 2 had several limitations. While, crowd-sourced data collected from
Prolific may include biases such as the rapid-responder bias [70], researchers
found Prolific data to be of higher quality than from popular competitors like
Mechanical Turk [50] and even more representative of the population than a
census-representative web-panel [54]. We iterated our survey wording refining
clarity based on pilot feedback, included a mix of open-ended and closed-ended
responses, and randomized the order of survey statements where appropriate.
Yet, the true preferences of survey participants may differ from their
responses due to survey-related imperfections such as response errors, mode
effects, or lack of understanding of the research scenarios. Furthermore, the
participants were limited to WhatsApp users in the US only, and WhatsApp usage
and perceptions could differ by country.

##  5 Discussion

Study Part 1 outlined proposed UCDS principles, provided an example
implementation of them, and proved the feasibility of deploying the principles
for research purposes. Moreover, Study Part 2’s survey suggested that the
standard methods of joining and scraping public WhatsApp chats miss a majority
of WhatsApp chats (private chats) and violate privacy expectations since users
do not expect researchers to join their chats and read their contents. Our
survey participants also preferred WhatsApp data be restricted to just
necessary metadata, anonymized, and given participant oversight before
researchers could access this data, in line with UCDS principles. The survey
also raised concerns about the complications of researcher-to-group
communication in WhatsApp data collection. Below, we discuss the implications
of our findings, how UCDS fits into the broader picture of data collection,
and how future work can improve and expand on the UCDS principles to shift
data collection towards a two-sided data sharing relationship between users
and researchers.

Researcher-to-Group Interface In developing URL-EXTRACTOR-APP as an example of
UCDS, we surmised that following consenting procedures for a group member
alongside increased data sanitization and privacy considerations would address
user concerns about a fellow chat member sharing data from their chats.
However, participants made clear in Study Part 2 that interfacing with a sole
chat member was not enough, despite that common current methods of collecting
WhatsApp data do not typically ask for consent from any chat member. That is,
all chat members should be involved in the research process in addition to the
increased privacy considerations of UCDS, indicating that additional
principles (e.g., Group Engagement) may be necessary. Involving each chat
member may also allow for addressing problematic cases where members of the
same chat may not trust one another to appropriately share data with
researchers. Obtaining group consent is nontrivial and the frameworks for
doing so are open questions in other fields [66, 61, 21]. The extent to which
each chat member should be involved and the mechanism for researcher-to-group
communication could be explored in future work. Future work that adapts and
tests the UCDS principles in other scenarios—not WhatsApp chat data—would also
be invaluable for expanding and iterating the UCDS principles.

Expanding USDS: A Family of Related Methods The proposed UCDS principles from
this work represent a family of related methods for collecting data. An app
like URL-EXTRACTOR-APP is one potential instance of the principles focusing on
URL sharing in WhatsApp chats. In general, the UCDS principles can be
implemented in a number of study-specific ways or used alongside other privacy
preserving methods. For example, implementations could be configured to target
different types of metadata as opposed to URLs or could be paired with
interviews to gain both a deeper understanding of a target population and
privacy-preserving insights into their chats. Researchers could also set up a
website to continually receive data that an app like ours locally collected
from participants’ devices, similar to a crowd-sourced tip line model [30, 69,
79, 62] but with localized processing and metadata across a full chat instead
of researcher controlled processing and stand-alone messages. If metadata is
not sufficient for a research study’s goals, researchers could tweak the
collected metadata towards their research goals and still follow the design
considerations. For example, in misinformation research, a study could include
deploying an app that locally analyzes chats by perceptual hashing [32]. If
actual chat content is required for analysis, researchers could implement the
analysis locally where users can oversee and verify its results, expanding on
what we did with our example app.

Towards Best Practices In Data Collection Study Part 2’s findings suggest that
the discussion regarding the collection of public data should extend beyond
studying WhatsApp for research purposes. Our participants agreed that just
because their chat data may be technically accessible on the web, does not
mean that they feel comfortable with researchers collecting it. The decades-
old Belmont Report serves as a wide-reaching foundation of ethical research
standards for many disciplines [47]. However, researchers have noted how the
reports principles are not sufficient for modern online research and argue for
additional ethical considerations for general online data collection. Further,
while regulations are intended for platforms, their stipulations resemble the
preferences of our study’s participants. For instance, modern privacy
regulations like the California Consumer Privacy Act and its extension, the
California Privacy Rights Act, grant expansive rights to users over their
data. These rights include knowing when your data is being collected, knowing
how it is being used, and more control over collected data, all of which are
supported by UCDS. The research community could also benefit from adapting
these publicly supported rights to maintain the public’s trust.

##  6 Conclusion

To investigate ethical data collection practices on WhatsApp, we conducted a
two-part study. In Study Part 1, we outlined four UCDS principles for
collecting WhatsApp chat data granting users control and transparency in data
sharing processes while also minimizing data collected. We provided a proof-
of-concept evaluation of following UCDS principles by creating and deploying
an app using UCDS to build an exploratory dataset that led to valuable
insights into participants’ WhatsApp chats. In Study Part 2, we surveyed
WhatsApp users for their perceptions about data collection research methods
including UCDS. Our findings suggest that users preferred how the UCDS
principles handle data collection but highlighted that these principles could
be expanded by exploring mechanisms for group consent. Future work can expand
on the UCDS principles and evaluate them in other contexts to help the usable
privacy community to continue to build best practices in data sharing between
users and researchers.

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##  Appendix A Supplementary Tables

Here we include additional tables for this work.

###  A.1 Dataset of Shared Chats

Participant ID | Chat ID | # Members
---|---|---
P1 | A | 2
P1 | B | 2
P1 | C | 2
P2 | A | 2
P2 | B | 2
P2 | C | 2
P2 | D | 2
P2 | E | 2
P3 | A | 2
P3 | B | 2
P3 | C | 2
P3 | D | 2
P3 | E | 2
P4 | A | 6
P4 | B | 2
P4 | C | 2
P4 | D | 2
P4 | E | 2
P5 | A | 8
P5 | B | 2
P5 | C | 2
P6 | A | 2
P6 | B | 2
P6 | C | 2
P6 | D | 2
P7 | A | 25
P8 | A | 2
P8 | B | 2
P8 | C | 2
P9 | A | 4
P9 | B | 3
P9 | C | 5
P9 | D | 2
P10 | A | 2
P10 | B | 2
P10 | C | 2
median: | 2

Table 5: The number of members for all chats in the dataset.

##  Appendix B Survey Instruments

Here we include the survey instruments for both the data-sharing participants
of Study Part 1 and the survey participants of Study Part 2.

###  B.1 Study Part 1: Background Survey Instrument

In this survey we asked participants for their demographic information and
characteristics of their WhatsApp usage.

####  B.1.1 Demographics

First, we have a few background questions.

##### 1\.

What is your age?

  * •

18-24 years old

  * •

25-34 years old

  * •

35-44 years old

  * •

45-54 years old

  * •

55 years or older

  * •

Prefer not to answer

##### 2\.

What is your highest level of education?

  * •

Less than High School Degree

  * •

High School Degree

  * •

Associate’s/Some College Degree

  * •

Bachelor’s Degree

  * •

Master’s Degree

  * •

Doctoral Degree

  * •

Prefer not to answer

##### 3\.

How long have you used WhatsApp?

  * •

Slider from 0 to 12 years

##### 4\.

What is estimated annual income? (If prefer not to answer, leave blank)

  * •

Drop-down menu

##### 5\.

What is your occupation? (If prefer not to answer, leave blank)

  * •

Text entry

##### 6\.

What state do you live in? (If prefer not to answer, leave blank)

  * •

Drop-down menu

##### 7

What is your gender?

  * •

Male

  * •

Female

  * •

Non-binary

  * •

Other

####  B.1.2 WhatsApp

Now, we’d like to know more about your WhatsApp usage. Please respond to the
best of your ability.

##### 8

Do you consider yourself a frequent WhatsApp user?

  * •

Yes

  * •

No

  * •

Prefer not to say

##### 9

Why do you use WhatsApp? (vs imessage, messenger, etc.)

  * •

More convenient for US communication

  * •

More convenient for international communication

  * •

Forced to join chat (work or family related)

  * •

Other (Please Specify):

##### 10

How many contacts do you have on WhatsApp?

  * •

Slider from 0 to 500

##### 11

What relationship do you have with your WhatsApp contacts?

  * •

Friends

  * •

Family

  * •

Work colleagues

  * •

Acquaintances

  * •

Prefer not to say

##### 12a

What percentages of your WhatsApp messages are Groupchats (chats with more
than 1 person)?

  * •

slider from 0 to 100

##### 12b

What percentages of your WhatsApp messages are Private Messages (chats with
only 1 other person)?

  * •

slider from 0 to 100

##### 13

Are you aware of how WhatsApp encrypts your chats?

  * •

Yes

  * •

No

  * •

Prefer not to say

####  B.1.3 Misinformation Encounters and Recent Events

Next, we have some questions on your experience with sharing information in
WhatsApp.

##### 14

Do you frequently rely on WhatsApp for news?

  * •

Yes

  * •

No

  * •

Prefer not to say

##### 15

How often do you SEND WhatsApp messages?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 16

How often do you RECEIVE WhatsApp messages?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 17

How often do you CHECK your WhatsApp messages?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 18

How often do you FORWARD links/URLS you receive in your WhatsApp chats?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 19

How often do you FORWARD audio/video messages you receive in your WhatsApp
chats?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 20

How often do you FORWARD images messages you receive in your WhatsApp chats?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 21

How often do you RECEIVE links on your WhatsApp chats?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 22

How often have you RECEIVED messages on your WhatsApp chats that were false or
misleading?

  * •

Every day

  * •

A few times a week

  * •

Once a week

  * •

A few times a month

  * •

Rarely

##### 23

How did you know that a message you received on WhatsApp was false or
misleading?

  * •

Person who sent it

  * •

Search on the web (Google or other search engines)

  * •

Looked suspicious

  * •

Known source of misinformation

  * •

Other (Please specify)

##### 24

What did you do to fact check WhatsApp messages you found false or misleading?

  * •

Online fact checkers

  * •

Through friends

  * •

Just knew

##### 25

Do you consider yourself a frequent Instagram user?

  * •

Yes

  * •

No

  * •

Prefer not to say

##### 26

Please answer the following on information you’ve seen on Instagram (Figure
6). Note: This question was only shown to participants that answered Yes to
the previous question.

Figure 6:

##### 27

Do you consider yourself a frequent Twitter user?

  * •

Yes

  * •

No

  * •

Prefer not to say

##### 28

Please answer the following on information you’ve seen on Twitter (Figure 6).
Note: This question was only shown to participants that answered Yes to the
previous question.

##### 29

Do you consider yourself a frequent Facebook/Messenger user?

  * •

Yes

  * •

No

  * •

Prefer not to say

##### 30

Please answer the following on information you’ve seen on Facebook/Messenger
(Figure 6). Note: This question was only shown to participants that answered
Yes to the previous question.

##### 31

Please answer the following on information you’ve seen on your Text messages
(Figure 6).

####  B.1.4 Technology

Next, we have some questions on your thoughts with any current technology.

##### 32

Have you used any of these features on WhatsApp?

  * •

WHO Health Alert (screenshot provided)

  * •

Magnifying glass icon pop up (screenshot provided)

  * •

No

  * •

Prefer not to say

##### 33

Would you want a better way to fact-check information on WhatsApp?

  * •

Yes

  * •

No

  * •

Prefer not to say

##### 34

Would you want WhatsApp to cover or censor known misinformation sources?

  * •

Yes

  * •

No

  * •

Prefer not to say

##### 35

In what ways, do you think WhatsApp can be improved to help address these
issues with false, inaccurate, or misleading information?

  * •

Open text entry.

####  B.1.5 Conclusion

Please include any final thoughts you would like to share below.

##### 36

How has anything you said been vastly different from how you send or receive
messages on other social media platforms (Instagram, Twitter, Facebook, etc.)
you use?

  * •

Open text entry.

##### 37

Is there anything else regarding WhatsApp, tech, or information quality that
you want to talk about?

  * •

Open text entry.

###  B.2 Study Part 2: User Perceptions Survey Instrument

In this survey we asked participants for characteristics of their WhatsApp
usage, their perceptions of two data collection scenarios, and their comfort
sharing different types of chat data with researchers. We concluded the survey
with miscellaneous data collection questions and optional demographic
questions.

####  B.2.1 WhatsApp Usage

Now, we’d like to know more about your WhatsApp usage. Please respond to the
best of your ability.

##### 1

Do you consider yourself a frequent WhatsApp user?

  * •

Yes

  * •

No

##### 2

How long have you used WhatsApp?

  * •

Slider from 0 to 13 years

##### 3

Why do you use WhatsApp (instead of other messaging apps like iMessage,
Facebook Messenger, Signal, etc.)? Select all that apply.

  * •

More convenient for US communication

  * •

More convenient for International communication

  * •

My family and friends use it

  * •

My work uses it

  * •

Works better than the other services

  * •

Other (Please Specify):

##### 4

Please select only WhatsApp from the list below. Attention Check Question.

  * •

WhatsApp

  * •

Telegram

  * •

Signal

  * •

Facebook Messenger

  * •

Threema

  * •

Wire

  * •

Other

##### 5

How many contacts do you have on WhatsApp?

  * •

Slider from 0 to 500 with an option to check a box indicating over 500
contacts.

##### 6

What relationship do you have with the members of your WhatsApp chats? Select
all that apply.

  * •

Friends

  * •

Family

  * •

Work colleagues

  * •

Acquaintances

  * •

People I know only from WhatsApp group chats

  * •

Other (Please Specify):

##### 7

How have you joined chats in the past? Select all that apply.

  * •

Created my own chats with my WhatsApp contacts

  * •

My friends/family created chats and added me

  * •

By receiving invite links from friends/family

  * •

By finding invite links online from public WhatsApp groups

  * •

Other (Please Specify):

####  B.2.2 Scenario Intro

In these questions, we want to know what methods you feel are okay for
researchers to use for the collection of WhatsApp chat data. You will be given
2 scenarios, each with a page worth of questions. Please answer each full page
of questions based on the scenario given at the top. Note that Scenario 1 and
2 were presented in random order.

####  B.2.3 Data Collection Method Scenario 1

Imagine the following scenario: The invite link to one of your WhatsApp chats
is publicly available online, and a researcher uses it to join the chat.
Please answer all the questions on this page based on this scenario.

##### 8

What should the researcher do when they first join the chat? 5-point Likert
(Strongly Disagree to Strongly Agree)

  * •

The researcher should inform the chat members of their presence.

  * •

The researcher should inform the chat members of their intention to collect
data.

  * •

The researcher should carry on as a normal chat member.

  * •

The researcher does not need to say anything.

##### 9

Should the researcher ask for permission to collect data? 5-point Likert
(Strongly Disagree to Strongly Agree)

  * •

The researcher should obtain consent from all chat members.

  * •

The researcher should obtain consent from a majority of chat members but not
necessarily all of them.

  * •

The researcher should obtain consent from at least one chat member but not
necessarily a majority of them.

  * •

The researcher need not worry about consent because the chat is publicly
accessible.

  * •

The researchers can collect chat data without consent as long as they provide
the ability for chat members to opt-out.

  * •

The researcher should only collect data from the members that have provided
consent.

  * •

If consent is needed, the researchers should obtain it by directly messaging
the individual chat members.

  * •

If consent is needed, the researchers should obtain it by messaging to the
whole group chat.

##### 10

Should the researcher collect personally identifiable information (e.g.,
Names, Profile Pictures, Phone Numbers) of the chat members? 5-point Likert
(Strongly Disagree to Strongly Agree)

  * •

It is okay if the researcher collects any personally identifiable information
of the chat members.

  * •

The researchers should not collect any personally identifiable information of
the chat members.

##### 11

What else would you like to say about this data collection scenario?

  * •

Open text entry.

####  B.2.4 Data Collection Method Scenario 2

Imagine the following scenario: One of your WhatsApp contacts is involved in a
research study that is collecting chat data, including data from a chat that
you are also in. In this scenario, the researchers do not join your chat but
are in contact with one of the chat members. Please answer all the questions
on this page based on this scenario.

##### 12

What should your WhatsApp contact do? 5-point Likert (Strongly Disagree to
Strongly Agree)

  * •

They should inform you of their involvement in the study.

  * •

They should ask for your permission to share chat data for the chats you are a
member of.

  * •

They can continue with the study without any additional considerations.

  * •

They can continue with the study without talking with you as long as no data
can be linked to you.

Here are some examples of WhatsApp chat data.

##### 13

As shown above, a single WhatsApp chat contains a lot of different data.
Researchers often don’t need all of it, so they extract only the pieces that
they may need for their study. The statements below are about how this data
extraction occurs. 5-point Likert (Strongly Disagree to Strongly Agree)

  * •

Your WhatsApp contact should upload the full chat data to the researcher’s
online servers that will automatically perform the data extraction.

  * •

Your WhatsApp contact should send the full chat data directly to the
researchers who will manually perform the data extraction.

  * •

Your WhatsApp contact should use an app that automatically performs the data
extraction before sending it to the researchers.

##### 14

WhatsApp chat data includes personal identifiers, such as Names or Phone
Numbers. Since researchers typically do not need this information, they
anonymize the data by replacing personal identifiers with placeholders like
User0 and User 1. The statements below are about how the anonymization occurs.
5-point Likert (Strongly Disagree to Strongly Agree)

  * •

The personal identifiers do not need to be anonymized.

  * •

The personal identifiers should be anonymized by the researchers.

  * •

The personal identifiers should be automatically anonymized before the
researchers get it.

##### 15

What else would you like to say about this data collection scenario?

  * •

Open text entry.

####  B.2.5 Data Sharing Comfort by Type

In these questions, we want to know what parts of your chat data you feel
comfortable sharing with researchers.

##### 16

Please indicate the level of comfort you feel towards sharing each category of
your chat data with researchers. 5-point Likert (Extremely Uncomfortable to
Extremely Comfortable). The statements below were presented in a random order
while keeping similar categories next to each other to reduce cognitive burden
on the participants (e.g., Whether a Message Contains a URL and The URL Itself
would always be adjacent).

  * •

The number of users in the chat

  * •

The names of all chat members

  * •

The contact information (e.g., phone numbers) of the chat members

  * •

The anonymized sender of each message (e.g., Jane Doe and John Doe become
User-0 and User-1, respectively)

  * •

Whether each message contains a URL

  * •

The URL itself

  * •

Whether each message contains text

  * •

The full textual content

  * •

Whether a message contains media (audio, video, or image)

  * •

The media itself (the audio, video, or media)

  * •

To make sure you are reading the questions carefully, please select Somewhat
comfortable for this statement. Attention Check Question

  * •

Whether a message contains a document attachment

  * •

The document itself

  * •

The start and end dates of a chat

  * •

The calendar date each message was sent

  * •

The time of day each message was sent

  * •

The number of messages from each user per day

  * •

The total number of messages from each user

  * •

The total number of messages from each user by message type (e.g., text,
media, URL, document)

  * •

Any and all data related to the chat

####  B.2.6 Miscellaneous Data Collection Questions

##### 17

What controls should you have over the chat data being collected? 5-point
Likert (Strongly Disagree to Strongly Agree)

  * •

I should be able to see the data that the researchers collect.

  * •

I should be able to edit the data before it is sent to the researchers.

##### 18

How do you feel about the roles of others in the collection of your WhatsApp
chat data? 5-point Likert (Strongly Disagree to Strongly Agree)

  * •

I trust researchers to be responsible with the data they collect from my
WhatsApp chats.

  * •

I trust my WhatsApp contacts to appropriately share chat data with
researchers.

  * •

I trust myself to appropriately share chat data with researchers.

##### 19

How do you feel about your chat data being used for research? 5-point Likert
(Strongly Disagree to Strongly Agree)

  * •

I should be compensated for my WhatsApp chat data.

  * •

I should be informed if my WhatsApp chat data is being collected.

  * •

I should be informed how my WhatsApp chat data is being used.

  * •

I would like it if my chat data is being used for research purposes.

  * •

My willingness to share my WhatsApp data with researchers depends on the goals
of the research.

##### 20

Is there anything else you would like to add about WhatsApp data collection?

  * •

Open text entry.

####  B.2.7 Demographics

##### 21

What is your age?

  * •

18-24 years old

  * •

25-34 years old

  * •

35-44 years old

  * •

45-54 years old

  * •

55 years or older

  * •

Prefer not to answer

##### 22

What is the highest level of school you have completed or the highest degree
you have received?

  * •

Less than high school degree

  * •

High school graduate (high school diploma or equivalent including GED)

  * •

Some college but no degree

  * •

Associate degree in college (2-year)

  * •

Bachelor’s degree in college (4-year)

  * •

Master’s degree

  * •

Doctoral degree

  * •

Professional degree (JD, MD)

  * •

Prefer not to answer

##### 23

What is estimated annual income?

  * •

Less than $25,000

  * •

$25,000 to $50,000

  * •

$50,000 to $100,000

  * •

$100,000 to $200,000

  * •

More than $200,000

  * •

Prefer not to say

##### 24

What state do you live in?

  * •

Drop-down menu including option for Prefer not to say.

##### 25

What is your gender?

  * •

Female identifying

  * •

Male identifying

  * •

Non-binary

  * •

Prefer not to answer

  * •

Prefer to self-describe:

    * –

text entry