#  WhatsApp Explorer:
A Data Donation Tool To Facilitate Research on WhatsApp.111Acknowledgements:
the authors warmly thank developers Shreyash Jain, Shlok Pandey, Ayushman
Panda and Ankit Kumar for their help in making WhatsAppExplorer a reality, Ved
Prakash Sharma of Across India and Mauricio Garcia of Connectar for fielding
multiple pilots (in India and Brazil, respectively) and for helping us
identify workable strategies, as well as Jose Furones, the UC3M’s data
protection officer, and Laurence Tavernier, the ERC ethics scientific officer
assigned to the POLARCHATS project, for multiple constructive dialogues that
led us to identify the protocol we describe here. The authors acknowledge
funding and/or logistical assistance from the European Research Council
(Through the ERC POLARCHATS project), Knight Foundation, National Science
Foundation, The University Carlos 3 of Madrid and Rutgers University.
222Authorship note: Both authors contributed equally to this research.

Kiran Garimella333Assistant Professor, Rutgers University. Corresponding
author<EMAIL_ADDRESS>Simon Chauchard444Associate Professor,
University Carlos III Madrid & Instituto Carlos 3 Juan March (IC3JM)

(March 27, 2024)

###### Abstract

In recent years, reports and anecdotal evidence pointing at the role of
WhatsApp in a variety of events, ranging from elections to collective
violence, have emerged (Arun, 2019). While academic research should examine
the validity of these claims, obtaining WhatsApp data for research is notably
challenging, contrasting with the relative abundance of data from platforms
like Facebook and Twitter, where user “information diets" have been
extensively studied (Barberá et al., 2015; Guess, Nagler and Tucker, 2019).
This lack of data is particularly problematic since misinformation and hate
speech are major concerns in the set of Global South countries in which
WhatsApp dominates the market for messaging (Tucker et al., 2018). To help
make research on these questions, and more generally research on WhatsApp,
possible, this paper introduces WhatsApp Explorer, a tool designed to enable
WhatsApp data collection on a large scale. We discuss protocols for data
collection, including potential sampling approaches, and explain why our tool
(and adjoining protocol) arguably allow researchers to collect WhatsApp data
in an ethical and legal manner, at scale.

Keywords: Data Donation, WhatsApp, Social Media, Misinformation, Group chats.

##  1 Introduction: The Need for WhatsApp Data

Over the past few years, alarming media reports have pointed at the role of
WhatsApp in a series of dramatic events. In countries such as Brazil and
India, studies have indicated that group interactions on WhatsApp can alter
the electorate’s perceptions (Perrigo, 2019; Bengani, 2019; Tardáguila,
Benevenuto and Ortellado, 2018). Furthermore, interactions on the platform
have also been linked to even more dramatic behaviors, including, but not
limited to, an increased likelihood of engaging in aggressive, extremist, or
violent actions (Chopra, 2019; Magenta, Gragnani and Souza, 2018; Ozawa et
al., 2023).

Although scholarship has started exploring these striking narratives, there is
still substantial work to be done in assessing their accuracy and in
unraveling the potential role of WhatsApp in these outcomes. Social scientists
focusing on issues like hate speech or misinformation, particularly in the
Global South, will increasingly require access to WhatsApp data, possibly on a
large scale. To fully comprehend the platform’s role in spreading problematic
content and its repercussions, researchers need a deeper understanding of
several aspects: (1) the nature and style of hate speech and/or misinformation
shared on WhatsApp, (2) the volume of such content, (3) its virality, (4) the
networks most conducive to its dissemination, and (5) the political, social,
and contextual factors that give rise to this content and its tangible
impacts. Moreover, it is essential for them to access this data in a manner
that is practical, legal, and respectful of users’ privacy.

Accessing WhatsApp data for research purposes however remains a challenging
task. While evidence about the “information diets" of Facebook and Twitter
users has been available for some time now (Barberá et al., 2015; Guess,
Nagler and Tucker, 2019), systematic evidence of a similar nature is notably
lacking for WhatsApp, even though the platform’s likely role in spreading
problematic content if often acknowledged, particularly in the Global South
(Tucker et al., 2018). Furthermore, in the scarce instances where researchers
do manage to obtain WhatsApp data, it often involves datasets too limited in
scope to credibly address the previously mentioned questions, and/or the data
is acquired in ways that might not be up to the strongest ethical standards.

How can researchers collect Whatsapp data from sufficiently diverse samples,
and in ways that comply with legal and ethical requirements? Taking on this
challenge, we introduce in what follows a donation tool and an associated
protocol for harvesting substantial volumes of WhatsApp data. Our proposed
data donation approach significantly reduces the practical difficulties
associated with WhatsApp data collection, while adhering to prevailing privacy
and ethical standards. We elaborate on our overarching strategy and outline a
research protocol in Section 3. Section 4 is dedicated to discussing the
ethical considerations integrated into our approach. Subsequently, in Section
5, we discuss sampling. To lay the groundwork for these discussions, the
following section begins with an examination of the challenges related to
WhatsApp data collection.

##  2 Challenges of WhatsApp Data Collection

Researchers eager to engage in WhatsApp Data collection may face technical,
legal, privacy-related, and practical challenges. Among these, certain
challenges appear more daunting than others. Technical challenges are, for
one, relatively limited: extracting data from private WhatsApp threads is
technically easy once a thread participant (whether or not they are the admin
of a group) consents to extract it: contrary to other platforms, WhatsApp
makes it very easy for its users to archive the content of the conversations
they are part of. Legal challenges originating from the platform are so far
not a real issue. Of course, the platform’s response to widespread research of
the kind discussed in this article remains to be fully observed. However,
there’s a reasonable expectation that WhatsApp might view research aimed at
identifying problematic behaviors or understanding their causes favorably.
This likelihood is enhanced by WhatsApp’s own emphasis on encryption, which
might limit the company’s ability to scrutinize content on its own platform.
We anticipate that external research, if it’s carefully scoped and doesn’t
significantly compromise users’ overall perception of privacy in WhatsApp
conversations, may not only be legally permissible but also supported and
encouraged by the platform.

That said, collecting WhatsApp data on a large scale still presents
significant ethical, legal, and practical challenges that do require a robust
discussion. These challenges are explored in greater detail in the subsequent
subsections.

###  2.1 Ethical and Legal Challenges

Given that users can effortlessly export data from their threads, and
considering researchers’ inability to access private WhatsApp threads without
the involvement of a thread participant, any WhatsApp data collection endeavor
must essentially be a donation effort. This requires one or more users to
agree to share data from the threads included in their account and to
undertake a series of steps to facilitate this export.

Donating data from one’s own account may be problematic from a privacy-
protection point of view insofar as it may contradict guidelines, norms or
laws protecting individuals’ privacy or limiting the processing of
individuals’ personal data. The European Union’s General Data Protection’s
Regulation (GDPR) currently constitutes the main example of such regulation,
though privacy laws around the world – such as India’s Personal Data
Protection Bill, Brazil’s LGDP or Canada’s Personal Information Protection and
Electronic Documents Act (“PIPEDA") - echo most of the principles at the heart
of the ruling when they exist. Besides, when it comes to the handling of
users’ personal data, principles of user consent, anonymization and
limitations on the use that can be made of such data will likely deserve a
discussion, whether a local equivalent to the GDPR exists or not.

In what ways might donating data from one’s own WhatsApp account exactly
violate the privacy of users, as defined in these norms? Though we acknowledge
that norms and regulations will differ depending on the case chosen by
researchers, we believe that five potential challenges may be associated to a
WhatsApp data donation program reliant on donations by what we hereafter refer
to as a consenting “gateway user”:

  1. 1.

Consent issues regarding third-party group participants: these are users who
aren’t the gateway into a group, but whose data – including phone numbers,
profile pictures, messages, etc. – appears in the threads. While the gateway
user consents to share their data, they cannot provide consent on behalf of
other users included in the dataset via their donation. This suggests that
researchers should either obtain consent from these third-party participants,
which might be impractical or methodologically undesirable, or ensure credible
anonymization of the third-party data to render personal information
unidentifiable.

  2. 2.

The extent of data collection allowed: under the GDPR, this issue relates to
the “data minimization" principle, which dictates that personal data
collection should be confined to what is directly relevant and necessary in
terms of time and scope. Although the GDPR’s Article 6 provides some
exceptions for research deemed “in the public interest," the minimization
principle still limits the volume of data that researchers can and should
gather, including if their research arguably qualifies as research in the
public interest. In the context of WhatsApp, this may imply that data
collection be limited to a relatively limited time frame. It may also
indirectly raise the question of the type of threads (one-on-one vs. group,
private vs. public, etc.) that may be harvested for research.

  3. 3.

The strategy for anonymization: this refers to the methods employed to
effectively anonymize stored data and minimize the risk of re-identification
of both gateway and third-party users. Anonymization is crucial for preserving
participant privacy, a priority in itself. However, it becomes particularly
vital if the data is later shared with others, as is often the case under open
science agreements. Ensuring anonymity is not only a privacy concern but also
a requirement for broader data sharing in the academic community.

  4. 4.

Data protocols pre-anonymization: this concerns the strategies that
researchers will use for transferring and storing data before it is
anonymized. Specifically, the challenge lies in ensuring that this data, in
its identifiable form, is not accessible to unauthorized individuals or lost
prior to anonymization. Effective data management practices must be in place
to secure the data during this vulnerable phase, safeguarding against both
external breaches and internal mishandling.

  5. 5.

Managing “unexpected findings" and legal disclosure obligations: This pertains
to how researchers will handle instances where they encounter data that may be
subject to legal disclosure under international or local law. It’s crucial to
have protocols outlining the course of action for situations involving
unexpected findings, especially those that might legally necessitate
disclosure. This challenge involves balancing ethical research practices with
compliance to legal requirements, ensuring responsible handling of sensitive
or legally significant information discovered during the research process.

###  2.2 Practical challenges

In addition to these ethical and, in some instances, legal challenges, there
are also several practical challenges that researchers must address to ensure
the effectiveness – from a research perspective – of a WhatsApp data donation
program. These challenges need to be navigated in accordance with the
balancing principle described by Ohme and Araujo (2022):

  1. 1.

The success of such a data donation program hinges on the ease and efficiency
of the donation process for potential donors. If the procedure is overly
tedious, costly, or time-consuming for the donor, it’s more likely to fail.
Additionally, the likelihood of failure increases if the donation protocol
necessitates that an enumerator or research team associate has even
superficial access to the data before it undergoes credible anonymization.
Ensuring a smooth and private data transfer process is critical for both donor
participation and data integrity.

  2. 2.

The second challenge is sampling. To enable interesting research, a data
donation program will in most cases need to convince a broad and ideally
representative group of gateway users to donate their data. This could entail
significant efforts and resources from the research team to ensure that the
sample of donors is adequately diverse and representative, which is essential
for the validity and reliability of the research findings.

##  3 Procedure and Tool: Introducing WhatsApp Explorer

As noted above, to address these numerous challenges, the only available
strategy is data donation. In light of the outlined challenges, the strategy
adopted must enable easy and straightforward donation. It should also
safeguard privacy, foster trust among a varied pool of donors, and avoid legal
complications for the research team.

We devoted a considerable effort to develop an effective solution to collect
data with the aforementioned requirements from private WhatsApp groups. This
has led to the creation of a specialized web interface, WhatsApp Explorer.
WhatsApp Explorer is designed to substantially mitigate the challenges
previously discussed, while enabling the collection of large volumes of data
that are valuable for research purposes.

###  3.1 General Principles

We first detail the broad principles of this strategy, before getting into the
technical aspects of the tool, as well as the details of the data protocol, in
the next sections.

The core approach involves reaching out to individuals, encouraging them to
contribute a portion of their WhatsApp data for social science research. Our
described protocol centers on direct, face-to-face interactions between a
“gateway user" and a research associate. However, it’s crucial to recognize
the potential for adapting this to an entirely online process, enabling users
to donate data remotely, without any in-person interaction. This focus on a
face-to-face donation protocol is primarily because it seems more effective in
our target regions for WhatsApp data collection, namely India and Brazil.555We
focus on face-to-face interactions for two reasons: (i) our protocol requires
gateway users scanning a QR code within their WhatsApp, necessitating a second
screen device for autonomous data donation. This requirement might
considerably limit our sampling pool, given the unlikelihood of most users
owning two screens. (ii) Based on initial tests (elaborated on later), in-
person interactions appear to foster trust between the research team and
participants, potentially leading to greater and more varied user
participation in the study.

The primary technical innovation of our tool lies in its ability to streamline
the data donation process for consenting participants. It allows users to
effortlessly donate their data with the help of a research associate who
facilitates the donation, without accessing the data themselves. To compensate
participants for their time and contribution, we offer monetary incentives.
Additionally, we assure extensive privacy and anonymization, emphasizing that
their data, anonymized at the source, will not be shared outside the primary
research team. Notably, in our proposed design, field staff such as
enumerators and local partners will not have access to the collected data. The
data collection is facilitated by these field staff but is directly encrypted
and uploaded to a secure server accessible only to the Principal Investigators
(PIs) and the core research team.666This approach also safeguards the research
associates, as possession of certain types of WhatsApp content on their
personal or professional devices could lead to legal risks if the data were to
transit through their devices.

Crucially, to mitigate privacy concerns, our approach avoids soliciting data
from one-on-one conversations and small group threads. Instead, we focus on
collecting data from larger groups. This decision is part of our strategy to
balance the need for comprehensive data with respect for individual privacy,
with more specifics outlined in the complete protocol provided later.

To address privacy concerns, we implement a thorough strategy for data
anonymization during the upload to our servers. We ensure that no raw, de-
anonymized data is stored. For text data, we immediately anonymize any
personally identifiable information, such as names, phone numbers, and email
addresses, from the dataset. This anonymization is executed using advanced
privacy-preserving algorithms from a reputable and widely used source: the
Google Data Loss Prevention
API.777https://cloud.google.com/security/products/dlp This state-of-the-art
tool is instrumental in maintaining the confidentiality of the data while
enabling meaningful research analysis.

For visual content, our approach involves an irreversible anonymization
process for most images and videos as they are uploaded. The exception to this
rule is for content shared across at least k groups/threads (with k being a
predetermined number, for example, 5). This practice ensures that we access
only a minimal amount of un-anonymized visual content. Notably, the viral
content that we retain and analyze is highly unlikely to be personal or
private, as it, by definition, circulates widely across various online
communities. The anonymization of visual content involves a multi-step
process: (i) Automated Anonymization: we use automated tools to systematically
blur faces and other identifiable features in images and videos, such as
license plates; (ii) Human-Supervised Anonymization: following the automated
process, we implement a second layer of anonymization, which is supervised by
humans. This step occurs before any analysis of the data, reinforcing our
extensive approach to anonymization and privacy protection.

###  3.2 Technical Description

Our tool leverages the capabilities of whatsapp-
web.js,888https://archive.is/45ZNX an open-source library functioning as a
WhatsApp client library for NodeJS. NodeJS, a JavaScript runtime, enables
server-side execution of JavaScript, facilitating interaction with WhatsApp
through its web browser application. The whatsapp-web.js library allows us to
authenticate, read, and process messages on WhatsApp programmatically,
functioning by operating WhatsApp Web in the background and automating its
interaction. This automation relies on reverse-engineered API calls similar to
those used by the official WhatsApp in its web application, enhancing the
tool’s robustness. The library whatsapp-web.js has a history of active
development and contributions spanning nearly seven years. One of the
drawbacks of our approach is the inherent dependence on the WhatsApp Web
interface. Despite this dependence, our experience over the past two years
indicates stability, as we have not encountered significant changes in the web
interface that could disrupt our data collection process.

Our primary technical contribution is enhancing the existing library
(whatsapp-web.js) to create a privacy-conscious, user-friendly front end,
ensuring scalable and reliable data downloading. We start by implementing the
automatic generation of a QR code, with the capability to regenerate the code
upon expiration. Upon successful authentication using their WhatsApp account,
users’ authentication tokens are stored, offering permanent access unless
manually revoked by the user. Utilizing these tokens, users can view a list of
their group conversations, although our focus is on larger groups, this is not
a limitation of the library itself. To accommodate multiple users and
surveyors, we established a scheduled data backup system, enabling parallel
downloads for scalability. This process, running nightly, automatically
downloads new data for all users, encompassing all messages and various media
types such as images, videos, documents, and audio files. This comprehensive
approach ensures efficient and secure data collection on a large scale. As
noted above, the data is first run through our anonymization procedures before
it is stored.

The code can be accessed at https://github.com/gvrkiran/WhatsAppExplorer. The
code contains detailed instructions to setup an instance and configure the
front and backend. We also provide code which takes the data collected from
the explorer to visualize the data. We provide the details below.999(i) In the
current setup, unfortunately, the code still requires someone who is
proficient in managing a Linux server and familiar with some programming. (ii)
To prevent potential misuse of our tools, currently, we limit the access only
for academic and non profit use. The access can be requested by reaching out
to the authors.

To setup an instance of WhatsApp Explorer, researchers will need the following
storage capacity: 1. A server with at least 8 GB RAM,16 CPU cores. Three ports
need to be opened on the server for the frontend, backend and a monitoring
dashboard.101010We used ports 3000, 8000 and 8051 for the frontend, backend
and the monitoring dashboards respectively. The code also includes a live
monitoring dashboard which provides live statistics on how many users have
been added and the amounts of data that was collected from them every day. 2\.
Currently, we tested the tool on an Amazon AWS EC2 instance with 16GB RAM, 32
core CPU and 2 terabytes storage. The instance does not use much RAM though to
enable parallel data downloads, multiple CPU cores would help. For each active
user being added to the system, WhatsApp Explorer consumes 1GB of RAM and 2
cores of CPU computation. This limits the number of concurrent users who can
stay connected to the monitor at the same time, for instance, scaling up to
large field deployment with dozens of enumerators might require a powerful
server.

###  3.3 Data Visualization Platform

Once the data collection scripts are run every day (this is automated in our
code above), we start another set of scripts to process the data to prepare it
for visualization on dashboards. These dashboards visualize content which is
spreading in the groups we monitor and help understand where what content was
shared by whom, while at the same time not revealing any personal information
of the users. The pipelines perform various tasks, including clustering
similar content, detect nudity, generating thumbnails from videos, etc.

We cluster the text, images and videos using different techniques to identify
near similar versions of the same message. Text clustering is done using
Locality Sensitive Hashing (LSH) Gionis et al. (1999), image and video
clustering is done using Facebook’s PDQ/TMK
tools111111https://github.com/facebook/ThreatExchange respectively. These
clustering techniques are robust to slight modifications and can find content
which has been altered slightly (e.g. adding a water mark). Once the clusters
are processed, we run through all the messages scraped from the data
collection script stored in the MongoDB database. Here we extract links from
text messages, and captions for media files (if any). We also generate
persistent and unique identification codes for text messages, as media files
are already assigned one during the data collection process. OCR is performed
on images and video frames to extract text. Finally, we run a data indexer
script to index the above data for elastic search.

Once the pipeline finishes its processing, we get the data hosted using an
Express JS server and ElasticSearch server. This data is displayed using our
dashboard (hosted at http://analysis.whats-viral.me/, available via login)
which is developed over React JS, using the Material-UI library. This
dashboard consists of various components such as user authentication, lazy
data loading, dynamic search using filters, etc. The data which is displayed
on the frontend is limited to the group the logged user is assigned to. Data
is displayed in five individual tabs: Forwarded, Image, Video, Text, Link.
Forwarded tab shows all messages (image, video and text) which were marked
‘Forwarded many times’ by WhatsApp. The image, video, text and link tabs show
content which was shared in at least 3 distinct groups in our dataset. To see
the contextual information about any particular message, we can see all the
groups where the message was sent and its respective timestamp. Data items are
loaded in chunks as an infinity scroll to reduce internet consumption, and
offer smooth user experience, instead of being overwhelmed by millions of data
items. All the communication with the backend happens using APIs secured with
JWT Tokens. These tokens are also helpful for having persistent login sessions
using cookies. Major backend operations such as user authentication, streaming
video, loading metadata, etc. are performed by Express JS server, whereas
fetch for lazy loading and search operations are handled by the ElasticSearch
server.

Figures 1, 2 shows screenshots of the data visualization platform.

Figure 1: A screenshot of the “Fowarded" tab in our visualization dashboard.
This tab shows text, video, and images which were ‘forwarded many times’ on
WhatsApp. We can see functionality to filter messages by date and search for
text in images/videos. Figure 2: Upon clicking a piece of content, this view
shows the groups in which the content was shared along with their timestamps,
giving a quick view of the spread of content.

Currently the authentication for the data collection and data visualization
platforms is separate, to enable distinct stakeholders to have access to
different views of the data, e.g., we could provide access to other
researchers or fact checkers, while restricting access to other stakeholders
like the surveyors.

###  3.4 Illustration: a Protocol For Ethical WhatsApp Data Donation

This tool may be used by researchers to assist with WhatsApp data donation. We
stress however that using the tool does not guarantee that the data donation
will be ethical and/or successful. Researchers will indeed need to design a
specific protocol around it to meet these objectives. We detail here our own.

As noted above, in the protocol we’ve developed for data collection in India
and Brazil, we employ a strategy that involves the physical presence of a
research associate. This approach is particularly suited for contexts in the
Global South, where many users may not have the necessary hardware –
specifically, two devices equipped with screens, such as phones, tablets,
laptops, or desktop computers – to independently complete the process online.
Moreover, users might lack the technical skills required for an online
process. Beyond the issue of hardware and skills, an in-person approach can be
crucial in both the Global South and other regions for effectively conveying
privacy assurances and building trust with participants.

While we cover potential sampling strategies at greater length below, this
section concentrates on the procedure followed after identifying a potential
donor. Once a potential donor is pinpointed, a research associate,
specifically a trained enumerator from a collaborating survey firm, personally
visits the individual. During this visit, the associate invites the individual
to partake in a research study focusing on their social media activity, and
particularly with regards to discussion groups they are part of and about the
content that circulates on these groups.

This is how we envision the data collection will then look like, step-by-step:

  1. 1.

The research associate, upon contacting an individual, explains the study’s
objectives and seeks consent through a detailed consent process. This involves
providing the potential donor with a flyer that contains essential information
about the project. The flyer introduces the researchers and their objectives,
including contact details. It features logos of all partnering organizations,
both local and international. There is also a link to a registry detailing the
research plans and legal basis, ensuring transparency in data processing
activities. For further inquiries, a hotline phone number is provided. The
flyer explains the anonymization strategy in clear, straightforward language,
ensuring that it is understandable for non-technical readers. Additionally, it
includes explicit instructions on how participants can opt out of the project
at any stage.

  2. 2.

If and only if the individual agrees to participate, the enumerator then asks
them to scan a QR code generated by our tool through their WhatsApp app on
their own smartphone. The research associate, using the web interface we’ve
developed (found at https://whatsapp.whats-viral.me/), generates this QR code
on their device. The participant can easily scan it using the “linked device"
function in WhatsApp, commonly used for connecting WhatsApp to a computer.
It’s important to note that at no point during this process does the research
associate need to handle the respondent’s device, ensuring privacy and
security.

Figure 3: The scanning process

  3. 3.

Once the QR code is scanned, the research associate connects the donor’s
WhatsApp account to WhatsApp Explorer. During this process, the associate
might briefly see the list of group included in the donor’s account, but does
not have access to any group content. Once the connection is established, the
enumerator displays on their own device (likely a tablet) a list of these
groups, then turns the device towards the donor for them to view.121212At this
stage, we automatically exclude smaller threads, defined as those with fewer
than 4 participants, to minimize data handling and enhance privacy protection.
Throughout this entire process, enumerators do not have access to the content
of the threads; the data are not uploaded onto their devices. In the use we
make of WhatsApp Explorer, screen shows as pre-selected all threads with 4 or
more participants and that have at least 15 messages in the past two weeks,
while all other groups remain deselected and unselectable.131313These criteria
are adjustable, depending on researchers’ interpretation of the data
minimization principle and any legal/ethical limitations they may face or
choose to impose. The specified numbers are not definitive; they are perceived
as a reasonable balance, but other configurations could also be appropriate,
depending on their IRB’s interpretation of legal and privacy guidelines.

Figure 4: Including and excluding groups to donate

  4. 4.

At this stage, participants can actively de-select any number of the pre-
selected groups they may not wish to donate, by clicking the respective boxes;
alternatively, they can choose to donate all pre-selected groups. By doing so,
they consent to share data from their chosen groups for a period extending two
months prior and two months after their interview date. Participants thus have
the flexibility to limit their donation to either historical or future data,
or a combination of both, from selected groups. During this selection process,
the interviewer inquires about the type of data the participants are
comfortable sharing: a) Historical data (from the past two months), b) Future
data (for the coming two months), c) Both historical and future data. If
participants opt to share future data, they are informed about the necessary
steps to ensure data collection continues for the next two months and how to
stop it, should they decide to do so. These actions can be swiftly executed on
their own phones. Detailed instructions about this are provided in the draft
flyer distributed during the consent process. The research associate is also
prepared to demonstrate these steps in person if needed.

  5. 5.

Once the participant completes the inclusion/exclusion process, the research
associate presses the “log messages” button, triggering two critical actions.
Firstly, the content from the last two months of the chosen threads is
uploaded to our secure server, having been anonymized according to our
established strategy. We strictly adhere to the policy of uploading and
storing only anonymized content, which is encrypted during its transfer from
the field staff’s devices to our cloud server. This step assures that neither
co-PIs nor research staff have access to any non-anonymized content. Secondly,
the action sets up an anonymized mirror copy of the selected threads, enabling
the collection of data from these threads going forward. Ensuring a finite
period for future data collection is paramount, hence the system is programmed
to automatically deactivate after two months. This automatic shutdown leads to
the user’s disconnection and the deletion of their contact information from
our database, effectively and securely concluding their data contribution.

  6. 6.

At this stage, once the messages and threads have been logged, the respondents
are asked to answer a brief series of questions. These questions pertain to up
to 5 of the threads for which data were either harvested or excluded.
Additionally, the respondents provide information about their own demographic
characteristics.

Figure 5: Screenshot from post-donation survey

  7. 7.

Within a few weeks of their participation, the respondents are compensated for
their contribution, typically receiving rewards such as phone credits directly
on their phones. Along with this compensation, they are once again provided
with the contact information for the “hotline” and a link to further details
about the data donation program.

##  4 Legal and Ethical Safeguards

Our protocol includes clear legal and ethical safeguards, which we detail
here.

###  4.1 Strong Anonymization and Privacy Protection

Understanding the effectiveness of our anonymization strategy in protecting
user privacy requires a comprehensive explanation of the data collected, along
with the specifics of how and when anonymization occurs. This detailed
overview will provide clarity on the types of data harvested through this
approach and the precise methods and timing employed to ensure privacy is
maintained throughout the data collection and analysis process.

With this data collection process, we gather a range of information. This
includes details on the users’ connections (i.e., their address book), the
identities of individuals they have communicated with on WhatsApp, the number
of groups they are a member of, the size of these groups, and, most
critically, the content of chats to which users have consented to share. The
content from these chats encompasses messages, images, and videos, along with
the timestamps of these communications and the identities of the
senders.141414Additionally, as will be detailed later, users’ names and
contact numbers are immediately anonymized in a manner that ensures neither we
nor anyone else can access this personal information. This approach is
designed to balance the need for comprehensive data collection with strict
adherence to privacy and anonymization standards.

Our data collection process involves multiple stages of anonymization to
ensure privacy. The first stage, an automated anonymization process, occurs
before the data is stored on our servers. During this stage, we employ
automated procedures to strip any personally identifiable information, such as
names, phone numbers, and emails, from the dataset. This is accomplished using
advanced, privacy-preserving algorithms from a well-established and widely
utilized library provided by Google, known as the Google Data Loss Prevention
API. Each piece of sensitive information is converted and replaced with a
unique identifier. Although this identifier could technically be used for re-
identification, we eliminate the original key as soon as we have confirmed
that the data is securely stored, making re-identification impossible.
Additionally, we do not store group icon pictures (i.e., profile pictures) in
our dataset. These measures are integral to our commitment to maintaining the
highest standards of data privacy and security throughout our research
process.

In addition, for pictures/videos included within the threads that we collect,
we create the following pipeline:

  1. 1.

At the outset, we securely store the pictures and videos collected on our
servers. Simultaneously, we generate hashes for each of these visual elements.
Hashing is a process that transforms these images and videos into a unique,
fixed-size string of characters.

  2. 2.

Following the creation of hashes for the images and videos, we utilize these
hashes to determine whether the same image or video appears in the data shared
by multiple users. Since each hash is a unique identifier corresponding to a
specific image or video, comparing these hashes enables us to identify
instances of the same content being shared across different data sets.

  3. 3.

We take significant measures to ensure the privacy and anonymity of the visual
content we store. Most images and videos are irreversibly anonymized, with the
exception of those shared by at least 5 groups or threads in our dataset and
that do not contain personal data. Given that this will apply to a relatively
small subset of the content, our core research team will review each of these
items individually to determine their eligibility for exclusion from
anonymization. This thorough procedure is designed to ensure that we avoid
accessing the vast majority of un-anonymized visual content, thereby
maintaining strict adherence to privacy standards.

For the anonymization of visual content, we employ state of the art tools
designed to automatically blur faces. This technology offers a convenient and
automated process for anonymizing not only faces but also other identifiable
features in images and videos, such as car license plates. This entire
procedure occurs within our servers, ensuring that the data never leaves our
system during the anonymization process. Once the images and videos are
anonymized, we replace the original, un-anonymized files stored on our servers
with these newly anonymized versions. Following this, the original un-
anonymized images and videos are permanently deleted. This step is crucial to
ensure that no personally identifiable visual content is retained in our
database, aligning with our commitment to uphold strict privacy standards in
our research.

Despite the rigor of our protocol, it’s important to recognize that perfect,
infallible anonymization is unattainable and cannot be solely reliant on
automated procedures. Therefore, we introduce a second layer of anonymization,
this time driven by human oversight. This Systematic Anonymization Audit (SAA)
is conducted before any analysis of the data. The objective of this audit is
to enhance and, ideally, perfect the anonymization process already in place.
The SAA involves a meticulous review of the data by trained personnel, who
manually check for any residual personally identifiable information that might
have escaped the automated procedures.

In practice, the SAA involves research associates, well-versed in the relevant
context, examining all text and visual content previously anonymized through
automated means, to assess any remaining re-identification risks of personal
data. The focus initially is on the text content, where they systematically
eliminate any references to specific locations, identification numbers, and
mentions of individual attributes, whether physical, physiological, genetic,
mental, economic, cultural, or social. Additionally, details about unique
possessions or aspects of an individual’s company or social network that could
lead to identification are also carefully redacted. This process is crucial
for enhancing the privacy safeguards already established by the automated
anonymization methods.

After completing the text anonymization, the focus shifts to further
anonymizing visual content as necessary. Specifically, this involves blurring
any elements in the images that could hint at or directly indicate a location,
such as street signs or storefronts. Additionally, we ensure that potentially
distinctive or atypical landmarks in the background are blurred. If the attire
of individuals in the images is distinctive or identifying, that too is
blurred for added anonymity. Finally, any distinctive body marks, like scars
or tattoos, are also blurred, although faces will have already been obscured
in the earlier stage of anonymization. We will update this list as need
occurs.

###  4.2 Policy with Regards to Legally Problematic Content

Our protocol in addition incorporates explicit provisions to address scenarios
where data collection or research activities might lead to the discovery of
content that requires mandatory legal disclosure, in accordance with the laws
of the countries where our research is conducted.

Although we anticipate a low likelihood of such occurrences ex-ante, we
acknowledge that this data collection project or the subsequent analysis could
result in “unexpected findings." These are findings that, while outside the
primary scope of our research objectives, might require researchers to take
specific actions, such as disclosing information to appropriate or designated
authorities. This aspect of the protocol ensures we’re prepared to responsibly
manage any unforeseen or incidental discoveries that arise during the course
of our research.

In a project centered on social media and violence, ethical dilemmas
classified as ’serious and/or complex’ might arise if the research uncovers
unexpected or incidental findings that necessitate interventions for the
safety and well-being of participants. This could include indications of
physical abuse, self-harm, drug dependency, or neglect, particularly in
minors. Additionally, the research might reveal information subject to
mandatory disclosure under the national laws of the countries where the
research is conducted. Such situations would require researchers to break the
confidentiality normally afforded to research participants. Instances that
might necessitate such disclosures include criminal activities like crimes,
child sexual exploitation, human trafficking, or acts of terrorism.

Should such content be detected, our research team commits to consulting with
the ERC POLARCHATS’ ethics advisory board, which includes specialists from
both Brazil and India, within 3 days from the time of discovery. This
consultation will focus on determining the most appropriate course of action
for each individual case. We consciously avoid setting a predetermined,
blanket policy for such situations, given the dynamic political environments
in both countries and the possibility of political bias within their judicial
systems. This approach allows for a more nuanced and context-sensitive
response to each unique scenario, ensuring ethical integrity and adaptability
in our research practices.

###  4.3 Restraint in Amount of Data Collected

As previously mentioned, we request participants to share data spanning up to
four months: two months prior and two months following their involvement. This
data is specifically from threads meeting certain criteria–those with four or
more participants and at least 15 messages in the past two weeks. However,
participants are also provided the flexibility to share only a portion of this
selected data. They have the option to contribute either historical data or
data going forward. Additionally, they can choose to exclude any thread from
the initial list presented to them and can opt out of as many threads as they
wish. It’s also important to note that participants have the freedom to
withdraw from the program at any point after giving their consent, ensuring
their ongoing control over their data and participation.

Consequently, our approach does not entail an indefinite connection to
participants’ WhatsApp accounts, and we deliberately refrain from collecting
data from certain types of threads. We consider these parameters to strike an
appropriate balance between three key elements: (i). adherence to the data
minimization principle, (ii). the practicality of our extensive anonymization
strategy, and (iii). our capacity to conduct meaningful scientific research,
particularly statistical analyses, that serve the public interest. This
balance is critical to ensure that our research methodology is both ethically
sound and scientifically robust.

All other threads, particularly one-on-one conversations, are completely
omitted from our data collection process. These threads do not appear on the
research associate’s screen when interacting with potential donors and cannot
be selected for inclusion in the donation, even if the gateway user expresses
a desire to share them.

These measures substantially restrict the volume of data that our research
team can collect. Although this approach is designed to furnish a considerable
amount of politically and socially pertinent data regarding users’ WhatsApp
activities in the target countries, enabling meaningful and representative
analyses, it also serves as a safeguard against the excessive or unrestrained
accumulation of private data. We view this restraint as essential,
particularly as data donation programs become more prevalent.

The ongoing discussion within the research community about the optimal balance
between data access for research purposes and privacy considerations is
crucial. We believe that the provisions we have implemented significantly
address the ethical and legal challenges associated with WhatsApp data
collection, as outlined earlier. However, it’s important to recognize that
while our tool and protocol effectively navigate these challenges, they don’t
completely eliminate all obstacles to deploying a successful data donation
program. Adhering to ethical data collection practices doesn’t inherently
assure, and might even conflict with, the ability to gather data from samples
that are scientifically compelling and representative.

##  5 Solving the Sampling Challenge

As noted above, our protocol imposes strict limitations on the types of data,
the types of groups, and the amount of data we collect at the individual
level. We believe these limitations to strike the right compromise between our
ability to carry research and the need for privacy protection, insofar as
these limitations theoretically do not prevent us from collecting the data we
are most interested in, in the context of our research; that is, the content
of larger groups (here defined as groups including 5 or more members).

With this said, even if we deem the data donated by each individual donor to
be interesting enough, our ability to understand what circulates on large
WhatsApp groups in India and Brazil is contingent on our ability to solve the
aforementioned sampling challenge. That is, it is contingent on our ability to
gather data from samples of users that are ideally representative of the
societies we are interested in (or at least of some subgroups of users in
these societies), or as a second best, samples which at least include donors
from diverse enough subgroups from these populations.

In the rest of this section, we outline several possible strategies that we
deem more or less desirable and/or realistic, based on the pre-tests and
exploratory surveys we have so far conducted in India and Brazil.151515Because
our main research objectives are openly descriptive, and require that we speak
about a broader population, we exclude from the get-go snowball sampling as
well as simpler forms of convenience sampling.

###  5.1 Probability Sampling

A first – and maybe ideal – possibility may be to rely on probability or
random sampling, possibly based on some stratification. In this case, after
the research team identify targeted individuals based on some form of random
selection from an established sampling frame (a population list), associates
would approach them and ask them to donate part of their WhatsApp data,
following the protocol detailed above. This attempt at extracting WhatsApp
data may be embedded in another survey or constitute the central objective of
the study.

Although others may be more lucky in different locations, our experience
suggest that such a strategy, while not altogether impossible, may be either
expensive or inefficient or both. We attempted a version of this in August
2023 in a single district of Uttar Pradesh, India, initially over a large
sample.161616While the initial plan was to contact 800 individuals, we cut
short the experience after 6 days, in light of low participation rates.
Despite the fact that we were offering relatively large incentives, the
proportion of randomly selected respondents that were contacted and who later
accepted to donate some of their WhatsApp data never crossed the 15% mark.
Importantly, while this was not encouraging, the denominator here included a
large a amount of individuals (more than half of those contacted, in fact) who
either (i) did not own or have access to a smartphone, (ii) had access to a
smartphone but did not have internet data at the moment of contact, or, (iii)
had a smartphone and data but either did not use WhatsApp or used it too
little to qualify as eligible donors. We also note that the rate of donation
was almost three times higher among some demographics that may be of specific
interest to researchers (young men, for instance, in this context),
potentially making random sampling a viable option if targeted on some
specific demographic subgroups. Nonetheless, such numbers would require
researchers to deploy very large resources in order to obtain donations from a
sample of individuals that would eventually remain a biased sample. While we
did not carry a similar experiment in Brazil, we embedded in two existing
studies based on stratified random sampling (first, in the relatively affluent
city of Blumenau (N=426), in the South of the country; and separately across
the Northern state of Pernambuco (N=999)) a series of attitudinal questions
gauging individuals’ willingness to donate their data. Importantly, these
questions were asked after the protocol was explained in great detail, and
respondents were able to consult all formal documents that our potential
donors would receive in the final study. They were also told that they would
receive relatively large monetary incentives immediately before they were
asked whether they would accept to participate to the data donation program.
Finally, to make the question even more meaningful, respondents were asked if
the research associate could return the following week at an agreed upon time
to complete the data donation protocol. While the actual rate of donation
would likely be smaller than what we can infer from these behavioral
intentions, results were overall more encouraging than in India. In our first
sample, 25% of the individuals we overall approached pledge to donate their
data, and 29% of those who declared being active on some WhatsApp groups.
Further, the number rose, sometimes as high as 40% among some demographics,
with the young, male and less affluent respondents overall being more prone to
donating their data.171717We by contrast detected no correlation with
education. While it took place among a less affluent population, with much
lower rates of WhatsApp usage, results were comparable in our second sample:
18.3% of the individuals we overall approached pledge to donate their data,
but this number grew to 35% among those who declared being active on some
WhatsApp groups and were eligible donors as per our criteria. Importantly,
differences in willingness to donate overall did not significantly differ
across age, gender, education level or income levels in this second sample.

Altogether, while more research may be needed to increase our confidence in
this intuition, these data suggest that random sampling might be a feasible
option if researchers can offer incentives as substantial as ours, if they are
able to previously identify the criteria of likely donors, and if these
correspond to a population of interest. This may be possible in some contexts
and with regards to specific research questions. Nonetheless, we believe this
should overall remain a difficult and expensive option, including for
researchers with substantive research budgets. For this reason, we believe
researchers may be better off using a less probabilistic and more creative
approach to sampling.

###  5.2 A More Feasible Alternative: Decentralized quota sampling

Given the challenges we faced as we attempted to rely on random sampling, we
devised an alternative approach for our pilot studies in India and Brazil.

While it does not have the obvious advantages of random sampling, the strategy
we relied on India avoids some of the most obvious pitfalls associated to
purposeful sampling, convenience sampling or snowball sampling. We name this
strategy “decentralized quota sampling". Practically, we recruit a large
number of research associates (a much larger number of associates than may be
necessary in a traditional survey) and later request each of them to recruit,
following some strict demographic quotas implemented at the level of each
research associate, a relatively small number of donors located around their
own place of residence. Concretely, we have many research associates
disseminated around a territory of interest, each of which recruits a
relatively small number of donors following some quotas. This strategy
guarantees that we recruit individuals with sufficiently diverse demographic
characteristics – quotas prevent homogeneity in the pool of donors recruited
by each associate –, and that we recruit individuals drawn from a variety of
different networks, as each associate may only recruit a small numbers of
donors. Provided the number of potential donors recruited by each associate
remains small (for instance $<$ 20), and that the number of associates
initially recruited is high and spread over a series of locations which may
jointly be representative of the population of interest, we contend that such
a strategy would enable researchers to collect data based on larger (and
possibly more diverse) samples than random sampling arguably would. More
importantly, such a strategy would allow researchers to better focus their
resources.

We piloted this strategy in one district of Uttar Pradesh (India) in September
2023. This allowed us to receive donations from 379 users giving us 1,094
groups at a fraction of the cost we would have spent had we relied on random
sampling. We later piloted this strategy in Sao Paulo (Brazil) where we
harvested data from 201 users, who gave us a total of 792 groups.

While we have have so far refrained from engaging in analyses about the
content of the threads we exported, these pilots already provide us with
important aggregate statistics about WhatsApp usage and about willingness to
give away content in both of our samples. As shown in Figure 6, the average
number of groups with more than 2 participants that donors had on their phones
was 23.2 in Brazil and 15.85 in Brazil.181818For the record, the average
number of one-on-one conversations that donors had on WhatsApp was 171.42 in
Brazil and 140.17 in India. Of these, users were by design - following the
limitations we self-impose as per our protocol - only able to donate an
average of 5 and 3.32, respectively. One good news from a sampling standpoint
is that they did give away the vast majority of these groups, as evidenced by
the comparison between the number of eligible groups and the number of
effectively donated groups. While our sample is likely biased, there is little
evidence that the sample of groups that participants donated was. Figure 7 in
turn shows the distribution of group sizes in our two datasets. The median
group sizes in India and Brazil were 104 and 71 respectively. As we can see,
these are potentially large groups as indicated by previous research on the
membership of users in large public groups (Lokniti, 2018). Because we collect
messages over a period ranging from 2 to 4 months per participant, this
strategy allows us to harvest a large number of messages: we harvest an
average of 2,760 messages per donor in Brazil, and 1,103 in India.

Figure 6: Group metrics Figure 7: Size of Donated Groups in India and Brazil

Breaking this down by age category (Figure 8), we confirm that all age
categories maintain relatively few large and active groups (as implied by
Figure 6), but also show that the number of such groups broadly seems to
correlate with age in both countries, with the youngest donors having and
hence donating the lowest number of groups.191919Small N in some of the
highest age categories in both countries make these aggregate statistics
potentially unreliable.

Figure 8: Number of donated groups by age categories

The comparison Brazil/India in terms of activity on large WhatsApp groups so
far may imply that this activity is somewhat correlated to living standards,
with comparatively better-off Brazilians maintaining a higher number of such
groups. A break down of the number of donated groups by demographics other
than age (in Figures 9 and 10) broadly seems to confirm this point. In India
(Figure 9), while we did not have a reliable way to measure donors’ income as
part of the survey tagged onto our pilot, we see that the number of groups is
extremely correlated with caste categories, with upper-caste donors
maintaining many more large active groups than lower-caste donors. Similarly,
Hindu respondents appear to maintain (and hence donate) many more groups than
Muslim respondents. In Brazil (Figure 10), we observe similar evidence
suggesting that income and education correlate to the amount of groups.
Meanwhile, we detect no clear correlation with gender, race, or religion.

Figure 9: Demographics and consented groups (India) Figure 10: Brazil - mean
consented groups by demographic

Finally, the metadata allow us to describe the type of content we extract. In
Figure 11, we first show statistics about the ratio of messages that are
forwards, and the number of times they have been forwarded in a row, until
they have been forwarded five times or more and are labeled as “frequently
forwarded" by WhatsApp. As shown in the Figure, the vast majority of messages
are not forwards, and only around 1% of all content is frequently forwarded
content. We however note some major and interesting differences in rates of
forwards across India and Brazil, with forwards accounting for a fairly large
proportion of all messages received by our Indian donors.

Figure 11: Forwarding score comparison. 127 indicates "forwarded many times
messages". roughly 1% in each dataset

Second, we can use our data to describe whether messages are chats (text
messages), image, videos, or other types of content. As shown in Figure 12,
only around 50% of all messages in both countries are chats, illustrating the
multimedia nature of WhatsApp. We however note some interesting differences in
the way in which Indians and Brazilians use the service. For instance, images
are significantly widely used in India, with close to 40% prevalence,
Brazilians use significantly larger share of audio and video messages.

Figure 12: Comparison of the top 6 modalities

##  6 Conclusion

In sum, the strategy we detail here should provide researchers with an
efficient, privacy-protecting and secure methodology to collect WhatsApp data
to answer a variety of research questions. In that sense, we hope our work
will help develop research on the platform and its uses around the world.

Of course, we acknowledge that this strategy has limitations. First, we
recognize that the multiple, extensive stages of anonymization we implement
eventually fall short of eliminating 100% of the possible risks of
identification of the individuals involved. We however believe it comes
extremely close to doing that, in practice, and note that researchers willing
to undertake WhatsApp research must, in one way or another (ideally, only in a
residual way), be willing to invoke that their research is in the public
interest - as per GDPR’s article 6 -, to deviate from otherwise very limiting
guidelines and make said research practically possible. Our strategy, while it
goes a long way in striking the right balance between privacy protection and
our ability to do research, is not perfectly foolproof on the privacy
protection front unless we invoke our right to deviate from usual privacy
protection guidelines to carry research in the public interest.

Second, we still lack sufficient data to speak to the representativeness of
the data we will eventually manage to extract. Until a larger study is run, we
will remain unclear as to whether the strategy will for instance function
among some demographics, and the extent to which respondents will be selective
in terms of the groups they choose to donate. There is in addition little
doubt that researchers focusing on populations by nature difficult to
investigate (say for instance, members of a rebel army or of a vigilante
group) will continue to struggle to obtain data to study the influence that
WhatsApp networks may have in these processes. Our technology may not entirely
change the reticence that many users may have when approached and asked to
donate their smartphones’ content.

Third and relatedly, our strategy is costly in labor, infrastructures, and
resources, especially if researchers are going to provide rewards or
incentives to potential donors. This implies that many researchers relying on
it will not be able to collect large and/or representative datasets in the
case of their choosing.

In spite of these important limitations, we believe the technology we present
in this article, and which we will keep improving over the next few years,
will dramatically improve current research opportunities and practice. Our
early experiments in the field in India and Brazil on our own project (ERC
POLARCHATS) suggest that we will be able to obtain large datasets from a
diverse, if not representative, group of users. This is, in and of itself, an
improvement over the status quo, and one that should allow us to answer some
important research questions and monitor the virality of problematic contents
on the app.

Further, while we acknowledge that most researchers will not be able to
collect as much data as we plan to due to the rather costly nature of our
strategy, we also hope it will help set the standards for how to collect
WhatsApp data, regardless of the amount of data collected by specific
researchers. Important discussions about consent, privacy and anonymization
are at stakes and need to be balanced with the impervious need to access this
data to document and analyze some pressing dangers. Even if researchers
assemble datasets more limited in scope than the ones we are planning to
assemble, we believe their strategy should equally go through this balancing
exercise and provide clear safeguards to users. In that sense, we hope this
article will push researchers to reflect on what “fair” WhatsApp data
collection should look like – a thorny issue we have tried to solve –, in
addition to assisting their practical needs.

## References

  * (1)
  * Arun (2019) Arun, Chinmayi. 2019.  “On WhatsApp, rumours, lynchings, and the Indian Government.” Economic & Political Weekly 54(6).
  * Barberá et al. (2015) Barberá, Pablo, John T Jost, Jonathan Nagler, Joshua A Tucker and Richard Bonneau. 2015.  “Tweeting from left to right: Is online political communication more than an echo chamber?” Psychological science 26(10):1531–1542.
  * Bengani (2019) Bengani, Priyanjana. 2019.  “India had its first ‘WhatsApp election.’We have a million messages from it.” Columbia Journalism Review 16\.
  * Chopra (2019) Chopra, Rohit. 2019.  “In India, Whatsapp is a weapon of antisocial hatred.” The Conversation 23\.
  * Gionis et al. (1999) Gionis, Aristides, Piotr Indyk, Rajeev Motwani et al. 1999.  Similarity search in high dimensions via hashing. In Vldb.  Vol. 99 pp. 518–529.
  * Guess, Nagler and Tucker (2019) Guess, Andrew, Jonathan Nagler and Joshua Tucker. 2019.  “Less than you think: Prevalence and predictors of fake news dissemination on Facebook.” Science advances 5(1):eaau4586.
  * Lokniti (2018) Lokniti, CSDS. 2018.  “How widespread is WhatsApp’s usage in India?”.
https://www.livemint.com/Technology/O6DLmIibCCV5luEG9XuJWL/How-widespread-is-
WhatsApps-usage-in-India.html

  * Magenta, Gragnani and Souza (2018) Magenta, Matheus, Juliana Gragnani and Felipe Souza. 2018.  “How WhatsApp is being abused in Brazil’s elections.” BBC News [Internet] .
  * Ohme and Araujo (2022) Ohme, Jakob and Theo Araujo. 2022.  “Digital data donations: A quest for best practices.” Patterns 3(4).
  * Ozawa et al. (2023) Ozawa, Joao VS, Samuel C Woolley, Joseph Straubhaar, Martin J Riedl, Katie Joseff and Jacob Gursky. 2023.  “How Disinformation on WhatsApp Went From Campaign Weapon to Governmental Propaganda in Brazil.” Social Media+ Society 9(1):20563051231160632.
  * Perrigo (2019) Perrigo, Billy. 2019.  “How volunteers for India’s ruling party are using WhatsApp to fuel fake news ahead of elections.” Time Magazine .
  * Tardáguila, Benevenuto and Ortellado (2018) Tardáguila, Cristina, Fabricio Benevenuto and Pablo Ortellado. 2018\.  “Fake News Is Poisoning Brazilian Politics. WhatsApp Can Stop It.” International New York Times pp. NA–NA.
  * Tucker et al. (2018) Tucker, Joshua A, Andrew Guess, Pablo Barberá, Cristian Vaccari, Alexandra Siegel, Sergey Sanovich, Denis Stukal and Brendan Nyhan. 2018.  “Social media, political polarization, and political disinformation: A review of the scientific literature.” Political polarization, and political disinformation: a review of the scientific literature (March 19, 2018) .


[a]Pia Leonie Jones Petrak

# Towards the determination of sigma terms for the baryon octet on
$N_{\mathrm{f}}=2+1$ CLS ensembles

Gunnar Bali  Sara Collins  Jochen Heitger  Daniel Jenkins  Simon Weishäupl
Thomas Wurm

###### Abstract

A lot of progress has been made in the determination of nucleon sigma terms.
In this work we consider the sigma terms of the other octet baryons as well.
These are determined on CLS gauge field ensembles employing the Lüscher-Weisz
gluon action and the Sheikholeslami-Wohlert fermion action with
$N_{\mathrm{f}}=2+1$ . The ensembles have pion masses ranging from
${410}\,\mathrm{MeV}$ down to the physical value and lattice spacings covering
a range between ${0.09}\,\mathrm{fm}$ and ${0.04}\,\mathrm{fm}$. We present
some preliminary results for $a\approx 0.06$ fm along a trajectory where the
sum of the sea quark masses is kept constant, focusing on the quark mass
dependence. We discuss multi-state fits to tackle the well-known problem of
excited state contamination and detail how we analyse connected and
disconnected contributions.

##  1 Introduction

Sigma terms are referred to as the quark contributions to the mass of a given
baryon. They consist of matrix elements of a scalar current $J$ times a quark
mass such that

$\displaystyle\sigma_{qB}=m_{q}\langle B|J|B\rangle$ (1)

where $m_{q}$ denotes the quark mass of flavour $q$. The pion-baryon sigma
terms are defined by $\sigma_{\pi B}=\sigma_{uB}+\sigma_{dB}$. We focus on
scalar flavour-singlet quark currents $J=\bar{q}\,\mathds{1}\,q$,
$q\in\\{u,d,s\\}$. In the matrix element, $B$ refers to the ground state of a
baryon $B$. The most prominent examples are the nucleon sigma terms ($B=N$)
which appear in the expressions for WIMP-nucleon scattering cross-sections and
are relevant for comparing model predictions to the exclusion bounds obtained
from direct detection dark matter experiments (such as the XENON1T
experiment).

We make use of and adjust methods established for the nucleon (reviewed in
[1]) when analysing the entire baryon octet. Studying the sigma terms of the
lambda $\Lambda$, sigma $\Sigma$ and cascade $\Xi$ baryons allows us to
investigate flavour symmetry breaking in the octet. In addition, discrepancies
between results for the pion-nucleon sigma term from Lattice QCD and
phenomenology are still to be resolved (see [2], and e.g., [3, 4]). In a
recent paper, results more consistent with phenomenology were obtained by
explicitly including $N\pi$ and $N\pi\pi$ excited states in the analysis [5].
By considering baryons other than the nucleon, we hope to understand the sigma
terms in more detail so as to help solve this puzzle.

##  2 Excited state analysis - Ratio method

The ratio method [6, 1] is a way of extracting the ground-state matrix element
needed to construct sigma terms (eq. (1)). We consider the two- and three-
point functions of a baryon (from the octet) at rest in the initial and final
state. The spectral decomposition of the two-point function reads

$\displaystyle
C_{\mathrm{2pt}}({t_{\mathrm{f}}})=\sum_{\vec{x}}\left\langle\mathcal{O}_{\mathrm{snk}}(\vec{x},{t_{\mathrm{f}}})\bar{\mathcal{O}}_{\mathrm{src}}(\vec{0},0)\right\rangle=\sum_{n}|Z_{n}|^{2}e^{-E_{n}{t_{\mathrm{f}}}}$
(2)

where $Z_{n}\propto\langle\Omega|\mathcal{O}_{\mathrm{snk}}|n\rangle$ is the
overlap of the interpolator $\mathcal{O}_{\mathrm{snk}}$ onto the state $n$
(and $\Omega$ the vacuum state) and ${t_{\mathrm{f}}}$ the source-sink
separation. Summation over spin and colour indices and projection onto
positive parity are implied. These indices become apparent when writing down
the operators explicitly. The interpolators for the four octet baryons are

$\displaystyle\mathcal{O}_{\mathrm{snk}}^{\alpha,\mathrm{N}}$
$\displaystyle=\epsilon^{abc}u_{a}^{\alpha}\left(u_{b}^{\beta}(C\gamma_{5})^{\beta\gamma}d_{c}^{\gamma}\right)\quad\text{and}\quad\mathcal{O}_{\mathrm{snk}}^{\alpha,\Lambda}=\epsilon^{abc}s_{a}^{\alpha}\left(u_{b}^{\beta}(C\gamma_{5})^{\beta\gamma}d_{c}^{\gamma}\right),$
(3) $\displaystyle\mathcal{O}_{\mathrm{snk}}^{\alpha,\Sigma}$
$\displaystyle=\epsilon^{abc}u_{a}^{\alpha}\left(u_{b}^{\beta}(C\gamma_{5})^{\beta\gamma}s_{c}^{\gamma}\right)\quad\,\text{and}\quad\mathcal{O}_{\mathrm{snk}}^{\alpha,\Xi}=\epsilon^{abc}s_{a}^{\alpha}\left(s_{b}^{\beta}(C\gamma_{5})^{\beta\gamma}u_{c}^{\gamma}\right).$
(4)

$a,b,c$ are colour indices, $\alpha,\beta,\gamma$ are spin indices and
$\mathcal{O}_{\mathrm{src}}^{\alpha}=\mathcal{O}_{\mathrm{snk}}^{\alpha}$ and
$\bar{\mathcal{O}}_{\mathrm{src}}=\mathcal{O}_{\mathrm{src}}^{\dagger}\gamma_{4}$.
$C$ stands for the charge conjugation operator.

Turning to the three-point function, its spectral decomposition reads

$\displaystyle C_{\mathrm{3pt}}({t_{\mathrm{f}}},t)$
$\displaystyle=\sum_{\vec{x},\vec{y}}\left\langle\mathcal{O}_{\mathrm{snk}}(\vec{x},{t_{\mathrm{f}}})J(\vec{y},t)\bar{\mathcal{O}}_{\mathrm{src}}(\vec{0},0)\right\rangle-\sum_{\vec{x},\vec{y}}\left\langle
J(\vec{y},t)\right\rangle\left\langle\mathcal{O}_{\mathrm{snk}}(\vec{x},{t_{\mathrm{f}}})\bar{\mathcal{O}}_{\mathrm{src}}(\vec{0},0)\right\rangle$
$\displaystyle=\sum_{n,n^{\prime}}Z_{n^{\prime}}Z_{n}^{*}\langle
n^{\prime}|J|n\rangle e^{-E_{n}t}e^{-E_{n^{\prime}}({t_{\mathrm{f}}}-t)},$ (5)

where $t$ is the insertion time of the scalar current,
$J=\bar{q}\,\mathds{1}\,q$, $q\in\\{u,d,s\\}$. As $J$ has the same quantum
numbers as the vacuum, the vacuum expectation value needs to be subtracted.
Note that depending on the type of baryon, different Wick contractions (so
different currents) contribute that result in connected and disconnected
quark-line diagrams.

Taking the ratio of the two spectral decompositions leads to

$\displaystyle
R_{\Gamma}({t_{\mathrm{f}}},t)=\frac{C_{\mathrm{3pt}}({t_{\mathrm{f}}},t)}{C_{\mathrm{2pt}}({t_{\mathrm{f}}})}=g_{S}^{q}+c_{01}\mathrm{e}^{-\Delta\,\cdot\,t}+c_{10}\mathrm{e}^{-\Delta\,\cdot\,({t_{\mathrm{f}}}-t)}+c_{11}\mathrm{e}^{-\Delta\,\cdot\,{t_{\mathrm{f}}}}+...$
(6)

where $g_{S}^{q}=\langle B|J|B\rangle=\langle
B|\bar{q}\,\mathds{1}\,q|B\rangle$ is the ground-state matrix element of
interest. $\Delta=E_{1}-E_{0}$ is the energy gap between the ground state and
the first excited state. The coefficients $c_{01},c_{10},c_{11}$ are made up
of matrix elements of different transitions such as $N_{1}\rightarrow N$ ,
$N\rightarrow N_{1}$ and $N_{1}\rightarrow N_{1}$ for the nucleon and
similarly for the other three baryons. $N_{1}$ stands for the first excited
state of the nucleon and may be a single- or multi-particle state. As we
consider the baryon at rest, $c_{01}=c_{10}\equiv c_{1}$ holds in this case.

##  3 Renormalisation

Quark masses $m_{q}$ are renormalised via

$\displaystyle m_{q}^{\mathrm{ren}}=Z_{\rm m}\left[m_{q}\,+\,(r_{\rm
m}-1)\frac{{\rm Tr}M}{N_{\rm f}}\right],$ (7)

which holds up to cut-off effects. $Z_{\rm m}$ is the renormalisation
parameter of the non-singlet scalar density and ${\rm Tr}M=\Sigma_{q}m_{q}$.
The matrix elements must renormalise in the inverse manner w.r.t. the masses
so that

$\displaystyle\sigma_{qB}^{\mathrm{ren}}=\left(m_{q}+(r_{\rm m}-1)\frac{{\rm
Tr}M}{N_{\rm f}}\right)\left(g_{q,S}^{B}+(r_{\rm m}^{-1}-1)\frac{{\rm
Tr}g_{S}^{B}}{N_{\rm f}}\right)$ (8)

and $\sigma_{\pi
B}^{\mathrm{ren}}=\sigma_{uB}^{\mathrm{ren}}+\sigma_{dB}^{\mathrm{ren}}$. The
normalisation factor $r_{\rm m}$ is the ratio of flavour non-singlet and
singlet scalar density renormalisation parameters, determined in Refs. [7, 8]
for our lattice discretisation. It accounts for the mixing of quark flavours
under renormalisation for Wilson fermions.

##  4 Numerical setup

Figure 1: Overview of the ${\rm Tr}\mathrm{M}=\mathrm{const.}$ ensembles part
of the CLS effort that we plan to include in our analysis. So far the
ensembles highlighted in black (diamonds) have been analysed. The pion masses
and lattice spacings are given on the $y$ and $x$ axes, respectively. The red
circle indicates the physical point.

We perform our calculations on CLS gauge field ensembles [9] employing the
Lüscher-Weisz gluon action and the Sheikholeslami-Wohlert fermion action with
$N_{\mathrm{f}}=2+1$ ($m_{l}=m_{u}=m_{d}\leq m_{s}$). Pion-baryon and strange
sigma terms are determined on the three ensembles highlighted in (black)
diamonds in fig. 1 along a trajectory where the sum of the sea quark masses is
kept constant. Only one lattice spacing of ${0.06426(74)(17)}\,\mathrm{fm}$
($\beta=3.55$) [10] and a lattice size of $128\times 48^{3}$ have been
considered so far, focusing on the quark mass dependence. We take three pion
masses into account: ${411}\,\mathrm{MeV}$, ${345}\,\mathrm{MeV}$ and
${284}\,\mathrm{MeV}$. We use $r_{\mathrm{m}}(\beta=3.55)=1.523(14)$ non-
perturbatively determined in [8]. To compute the connected three-point
correlation functions on the $m_{l}=m_{s}$ ensemble (N202), we used the
standard sequential source method [11]. On the other ensembles we employed the
stochastic method described in [12, 13] (see also [14, 15, 16, 17]),
estimating a timeslice-to-all propagator. This approach enables us to obtain
measurements for all baryons of interest, as multiple source and insertion
positions can be estimated simultaneously. Four different source-sink
separations, ${t_{\mathrm{f}}}/a=[11,14,16,19]$, corresponding to
${t_{\mathrm{f}}}\approx[0.71\,\mathrm{fm},0.9\,\mathrm{fm},1.03\,\mathrm{fm},1.22\,\mathrm{fm}]$,
are employed. Four measurements ($2$ replica $\times$ (forward and backward
direction)) are performed for each ${t_{\mathrm{f}}}$ on every configuration
except for the $m_{l}=m_{s}$ ensemble (N202) where we used the sequential
source method; here, only one measurement is undertaken at
${t_{\mathrm{f}}}=11$ and two at ${t_{\mathrm{f}}}=[14,16]$ (whereas the
number of measurements is also four at ${t_{\mathrm{f}}}=19$).

The disconnected three-point functions are constructed by correlating a quark
loop with a baryon two-point function. The loop is estimated stochastically
leading to additional noise on top of the Monte-Carlo gauge sampling. In order
to reduce the noise, the truncated solver method [18], the hopping parameter
expansion technique [19] and time partitioning [20] are utilised. Forty
measurements ($2$ replica $\times$ $20$ different spatial source positions) of
the two-point function are performed on each configuration with the exception
of N202 where the number is 52. The source-sink separations range from
${t_{\mathrm{f}}}/a=4\leftrightarrow{t_{\mathrm{f}}}\approx 0.26\,\mathrm{fm}$
to ${t_{\mathrm{f}}}/a=19\leftrightarrow{t_{\mathrm{f}}}\approx
1.22\,\mathrm{fm}$. For the analysis of the statistical errors we employ the
$\Gamma$-method [21] that is based on autocorrelation functions.

##  5 Analysis and preliminary results

Figure 2: The connected and disconnected ratios that contribute to the sigma
terms of the $\Xi$ baryon for ensemble N203: Simultaneous fit to the connected
and disconnected ratios is indicated by the coloured shaded regions, with the
resulting ground state scalar matrix element displayed as a grey band. We
obtain $\chi^{2}/\mathrm{d.o.f.}=0.72$ and an energy gap of $\Delta\approx
651\,\mathrm{MeV}$. At the top, the connected ratios are plotted against the
insertion time $t$ at different source-sink separations ${t_{\mathrm{f}}}$ for
the $\bar{u}u$ current (left) and the $\bar{s}{s}$ current (right). At the
bottom, the disconnected ratios are plotted against the source-sink separation
${t_{\mathrm{f}}}$ at different insertion times $t$ for $J=\bar{l}l$ (left)
and $J=\bar{s}s$ (right) .

Connected and disconnected ratios are constructed separately for all scalar
currents that contribute. In order to tackle excited state contamination we
perform multi-state fits, according to eq. (6). For each baryon we fit all
connected and disconnected ratios simultaneously, with the energy gap $\Delta$
being the common fit parameter. As an example, the ratios (and fits) relevant
for determining the sigma terms of the $\Xi$ baryon on the N203 ensemble are
displayed in fig. 2 showing all ratios involved. While we were able to resolve
the first two excited state terms from eq. (6), it was not possible to resolve
the third and we set $c_{11}=0$ throughout our analysis. The
$\chi^{2}/\text{d.o.f}$ values were below one for all baryons.

Figure 3: Pion mass dependence of sigma terms: The dotted vertical lines point
to the physical pion mass. The pion-baryon (left) and strange-baryon (right)
terms are depicted by squares (nucleon), diamonds ($\Lambda$), circles
($\Sigma$) and pentagons ($\Xi$). Simultaneous fit to pion-baryon and strange
sigma terms is displayed by the dashed lines (including the error bands as
shaded regions) resulting in $\chi^{2}/\mathrm{d.o.f}=1.29$. Both LO LECs and
the octet baryon mass in the chiral limit are kept fixed to $F=0.446(7)$,
$D=0.731(12)$ and $m_{0}=729(42)\,\mathrm{MeV}$ from a preliminary analysis of
the nucleon mass and the axial charges in the chiral limit; while the NLO LECs
$b_{D},b_{F},\bar{b}$ and the pion decay constant $F_{0}$ are fitted. We get
$\bar{b}=0.00317(29)$, $b_{F}=-0.000335(27)$, $b_{D}=0.0000493(21)$ and
$F_{0}=119.9(9.8)\,\mathrm{MeV}$. It differs greatly from
$F_{0}=71(2)\,\mathrm{MeV}$, the preliminary value from a combined fit to the
pion decay constant and the pion mass part of the analysis mentioned above.

The ground-state matrix elements of interest can now be extracted from the
fit. Matrix elements of different currents are combined and multiplied by the
corresponding quark masses as to make up pion-baryon and strange sigma terms
for all octet baryons considered, see eq. (1). Renormalisation is applied via
eq. (8). Our preliminary results for pion-baryon and strange sigma terms are
plotted against the pion mass in fig. 3. From Baryon Chiral Perturbation
Theory (BChPT) we can derive the pion mass dependence expected from SU(3)
flavour symmetry [22] (see also [23, 24, 25]); we apply the Feynman-Hellmann
theorem that relates sigma terms to derivatives of the baryon mass with
respect to quark masses, resulting in

$\sigma_{\pi{}B}=M_{\pi}^{2}\left\\{\frac{2}{3}\bar{b}-\delta
b_{B}+\frac{m_{0}^{2}}{(4\pi
F_{0})^{2}}\left[\frac{g_{B,\pi}}{2M_{\pi}}f^{\prime}\left(\frac{M_{\pi}}{m_{0}}\right)+\frac{g_{B,K}}{4M_{K}}f^{\prime}\left(\frac{M_{K}}{m_{0}}\right)+\frac{g_{B,\eta}}{6M_{\eta}}f^{\prime}\left(\frac{M_{\eta}}{m_{0}}\right)\right]\right\\},$
(9a)
$\sigma_{s}=\left(2M_{K}^{2}-M^{2}_{\pi}\right)\left\\{\frac{1}{3}\bar{b}+\delta
b_{B}+\frac{m_{0}^{2}}{(4\pi
F_{0})^{2}}\left[\frac{g_{B,K}}{4M_{K}}f^{\prime}\left(\frac{M_{K}}{m_{0}}\right)+\frac{g_{B,\eta}}{3M_{\eta}}f^{\prime}\left(\frac{M_{\eta}}{m_{0}}\right)\right]\right\\},\,\quad\quad$
(9b)

where $m_{0}$ and $F_{0}$ are the octet baryon mass and pion decay constant in
the chiral limit. $\delta b_{B}$ is a combination of two of the three BChPT
next-to-leading order (NLO) low energy constants (LECs)
$b_{D},b_{F},\bar{b}=-6b_{0}-4b_{D}$ and depends on the baryon,

$\displaystyle\delta b_{N}=\tfrac{2}{3}(3b_{F}-b_{D}),\quad\delta
b_{\Lambda}=-\tfrac{4}{3}b_{D},\quad\delta
b_{\Sigma}=\tfrac{4}{3}b_{D},\quad\delta b_{\Xi}=-\tfrac{2}{3}(3b_{F}+b_{D}).$
(10)

The couplings $g_{B,\pi},g_{B,K}$ and $g_{B,\eta_{8}}$ are made up of
different combinations of the leading order (LO) LECs $F$ and $D$,

$\displaystyle g_{N,\pi}$ $\displaystyle=\tfrac{3}{2}{(D+F)}^{2},$
$\displaystyle g_{N,K}$ $\displaystyle=\tfrac{5}{3}D^{2}-2DF+3F^{2},$
$\displaystyle g_{N,\eta_{8}}$ $\displaystyle=\tfrac{1}{6}{(D-3F)}^{2},$
$\displaystyle g_{\Lambda,\pi}$ $\displaystyle=2D^{2},$ $\displaystyle
g_{\Lambda,K}$ $\displaystyle=\tfrac{2}{3}D^{2}+6F^{2},$ $\displaystyle
g_{\Lambda,\eta_{8}}$ $\displaystyle=\tfrac{2}{3}D^{2},$ $\displaystyle
g_{\Sigma,\pi}$ $\displaystyle=\tfrac{2}{3}D^{2}+4F^{2},$ $\displaystyle
g_{\Sigma,K}$ $\displaystyle=2D^{2}+2F^{2},$ $\displaystyle
g_{\Sigma,\eta_{8}}$ $\displaystyle=\tfrac{2}{3}D^{2},$ $\displaystyle
g_{\Xi,\pi}$ $\displaystyle=\tfrac{3}{2}{(D-F)}^{2},$ $\displaystyle
g_{\Xi,K}$ $\displaystyle=\tfrac{5}{3}D^{2}+2DF+3F^{2},$ $\displaystyle
g_{\Xi,\eta_{8}}$ $\displaystyle=\tfrac{1}{6}{(D+3F)}^{2},$ (11)

that also appear in the ChPT expressions for the axial charges. $f^{\prime}$
is the derivative of the loop function $f$ that is set to $f(x)=-\pi x^{3}$ in
Heavy Baryon ChPT [26, 27] or

$\displaystyle
f(x)=-2x^{3}\left[\sqrt{1-\frac{x^{2}}{4}}\arccos\left(\frac{x}{2}\right)+\frac{x}{2}\ln(x)\right].$
(12)

in covariant BChPT in the extended on-mass-shell (EOMS) scheme [28, 29, 22].
This BChPT prediction (9) tells us that pion-baryon and strange sigma terms
should be describable by the same set of LECs. We find that fitting our
preliminary pion-baryon and strange sigma terms simultaneously is successful
so we can describe both sigma terms consistently, see fig. 3.

In addition, we investigate whether we obtain consistent results for the LECs
with a preliminary study where $F_{0}$ was estimated from a combined fit to
the pion decay constant and the pion mass. As part of the same study $m_{0}$,
$F$ and $D$ were determined in an analysis of the nucleon mass and the axial
charges in the chiral limit. We see that it is not possible to arrive at a
satisfactory fit keeping these four parameters fixed to the preliminary
values. Instead we find that at least one parameter has to account for the
difference in curvature. We show the best fit for our sigma terms in fig. 3;
we perform a simultaneous fit to pion-baryon and strange sigma terms according
to eq. (9) using eq. (12) for the loop function. Here the three NLO LECs and
$F_{0}$ are the common fit parameters whilst keeping $F$, $D$ and $m_{0}$
fixed to the values from the aforementioned (preliminary) analysis. Our fit
result for $F_{0}$ is unreasonably large. This may be due to the fact that we
do not yet incorporate cut-off and finite-volume effects on this small subset
of ensembles at a single lattice spacing. Note that higher order ChPT effects
may also contribute.

##  6 Conclusion and outlook

We have demonstrated that it is possible to obtain pion-baryon and strange
sigma terms for all octet baryons using similar methods to those for the
nucleon. Taking a closer look at the renormalisation pattern, it might be more
convenient to consider other combinations of sigma terms. We also aim to take
into account all main sources of systematics. We will for example try out
further fitting techniques; in order to determine whether we control excited
state contributions sufficiently, the summation method [6, 1] may serve as a
cross-check. In the future we plan to extend the analysis to include
additional ensembles. This will allow for a chiral extrapolation to the
physical pion mass and an investigation of cut-off and finite-volume effects.

## Acknowledgments

This work is supported by the Deutsche Forschungsgemeinschaft (DFG) through
the Research Training Group “GRK 2149: Strong and Weak Interactions – from
Hadrons to Dark Matter” (P. L. J. P. and J. H.). G. B., S. C., D. J., S. W.
and T. W. were supported by the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie grant agreement no.
813942 (ITN EuroPLEx) and grant agreement no. 824093 (STRONG-2020).
We gratefully acknowledge computing time granted by the John von Neumann
Institute for Computing (NIC), provided on the Booster partition of the
supercomputer JURECA [30] at Jülich Supercomputing Centre (JSC). Additional
simulations were carried out at the QPACE 3 Xeon Phi cluster of SFB/TRR 55.
The authors also gratefully acknowledge the Helmholtz Data Federation (HDF)
for funding this work by providing services and computing time on the HDF
Cloud cluster at the Jülich Supercomputing Centre (JSC) [31].

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