# Conversational Agents to Facilitate Deliberation on Harmful Content in
WhatsApp Groups

Dhruv Agarwal<EMAIL_ADDRESS>0000-0002-1090-3583 Cornell UniversityUSA ,
Farhana Shahid  0000-0003-3004-7099 Cornell UniversityUSA<EMAIL_ADDRESS>and  Aditya Vashistha  0000-0001-5693-3326 Cornell UniversityUSA
<EMAIL_ADDRESS>
(20 February 2007; 12 March 2009; 5 June 2009)

###### Abstract.

WhatsApp groups have become a hotbed for the propagation of harmful content
including misinformation, hate speech, polarizing content, and rumors,
especially in Global South countries. Given the platform’s end-to-end
encryption, moderation responsibilities lie on group admins and members, who
rarely contest such content. Another approach is fact-checking, which is
unscalable, and can only contest factual content (e.g., misinformation) but
not subjective content (e.g., hate speech). Drawing on recent literature, we
explore deliberation—open and inclusive discussion—as an alternative. We
investigate the role of a conversational agent in facilitating deliberation on
harmful content in WhatsApp groups. We conducted semi-structured interviews
with 21 Indian WhatsApp users, employing a design probe to showcase an example
agent. Participants expressed the need for anonymity and recommended AI
assistance to reduce the effort required in deliberation. They appreciated the
agent’s neutrality but pointed out the futility of deliberation in echo
chamber groups. Our findings highlight design tensions for such an agent,
including privacy versus group dynamics and freedom of speech in private
spaces. We discuss the efficacy of deliberation using deliberative theory as a
lens, compare deliberation with moderation and fact-checking, and provide
design recommendations for future such systems. Ultimately, this work advances
CSCW by offering insights into designing deliberative systems for combating
harmful content in private group chats on social media.

misinformation, hate speech, polarizing content, WhatsApp, deliberation,
moderation, fact-checking

††copyright: acmlicensed††journal: PACMHCI††journalyear: 2024††price:
15.00††doi: XXXXXXX.XXXXXXX††ccs: Human-centered computing Empirical studies
in collaborative and social computing††ccs: Human-centered computing Empirical
studies in HCI††ccs: Human-centered computing Collaborative and social
computing systems and tools Figure 1. A schematic diagram showing the flow of
the agent. It scans content in a group, gets activated on what it thinks is
harmful, and then facilitates an anonymous deliberation process (shown in the
box).

##  1\. Introduction

WhatsApp is a widely used instant messaging platform with billions of users
worldwide. It has become the de facto standard for digital communication due
to features such as end-to-end encryption, group chats, and multi-modality
(text, images, audio, video) (Resende et al., 2019; Pasquetto et al., 2022).
However, the ease of communication in WhatsApp groups has enabled harmful
content such as misinformation, hate speech, rumors, and political or
religious propaganda to proliferate quickly (Resende et al., 2019; Garimella
and Eckles, 2020). This has contributed to election manipulation (Murgia et
al., 2019; Garimella and Eckles, 2020; Resende et al., 2019), deaths due to
health myths (Islam et al., 2020), and mob violence (Vasudeva and Barkdull,
2020) in many countries in the Global South.

A large body of HCI and CSCW scholarship has examined the spread of harmful
content on WhatsApp (Resende et al., 2019; Javed et al., 2021), users’
interactions with such content (Varanasi et al., 2022; Feng et al., 2022), and
approaches to mitigate its impact (Kazemi et al., 2022; Pasquetto et al.,
2022). Two main mitigation strategies have emerged from this research:
moderation and fact-checking. However, due to end-to-end encryption, WhatsApp
cannot directly moderate content. Thus, the onus of identifying, managing, and
removing harmful content falls primarily on admins and group members. Yet,
admins rarely moderate content (Shahid et al., 2024), and members refrain from
contesting content to preserve group relationships (Varanasi et al., 2022;
Feng et al., 2022). WhatsApp has also established “tiplines” for users to
request fact-checks from organizations in different regions (Kazemi et al.,
2022; Meedan, 2020). But fact-checking is limited to debunking factual content
such as misinformation and fake news, failing to address other types of
harmful content that promote propaganda and polarization (Juneja and Mitra,
2022), which is popular in WhatsApp groups (Resende et al., 2019; Javed et
al., 2021; Garimella and Eckles, 2020). Further, users tend to keep fact-
checks to themselves and do not share them in their groups to avoid
confrontation with friends and family members (Varanasi et al., 2022; Feng et
al., 2022; Pasquetto et al., 2022). As a result, both moderation and fact-
checking fail to combat harmful content in WhatsApp groups, making it an open
and important challenge.

Recent scholarly work has introduced deliberation as a useful approach to
support accurate belief formation on social media (Bago et al., 2020).
Deliberation refers to the open and inclusive discussion of content seen on
social media to make an informed decision about its legitimacy (Jahanbakhsh et
al., 2023). It nudges users to think critically about the content’s accuracy
and purpose (Pennycook et al., 2021). Varanasi et al. (2022) demonstrated the
effectiveness of deliberation in identifying COVID-19 misinformation in
WhatsApp groups in rural India, though the deliberation occurred in offline
social spaces rather than within WhatsApp groups. Parallelly, research has
also showed the efficacy of conversational agents in facilitating discussions
in online groups (Kim et al., 2020, 2021). These studies have shown that such
agents encourage deliberation and help surface diverse opinions. Our work
bridges these streams of work by examining the role of conversational agents
in facilitating deliberation on harmful content in WhatsApp groups. In
particular, we ask the following research questions:

  * RQ1:

How can we design a conversational agent to facilitate deliberation in
WhatsApp groups?

  * RQ2:

Is community deliberation helpful in combating harmful content in WhatsApp
groups?

To answer these questions, we used a design probe to conduct semi-structured
interviews with 21 WhatsApp users in India. The probe showed an example
conversational agent that would get activated upon detecting potentially
harmful content in a WhatsApp group and then facilitate a deliberation process
amongst the group members. We used this probe as a starting point for our
conversation with participants. After participants engaged with the probe, we
interviewed them to capture their perceptions around deliberation, including
the strengths and limitations of using an agent to facilitate deliberation,
their interaction with such an agent, and issues around privacy and group
dynamics. We ended the interview with a design worksheet which allowed the
participant to customize the agent to their liking, thereby eliciting concrete
feedback on its design features.

Overall, participants agreed that such an agent would help identify and
contest harmful content in WhatsApp groups, but preferred anonymous
deliberation to avoid confrontation with other group members. They preferred
researching the content before expressing their opinions but were wary of the
effort this required, recommending AI capabilities to reduce their workload.
They appreciated the neutrality of the agent and found it useful for hearing
diverse perspectives about a message. However, it could potentially disrupt
group dynamics and would be futile for echo chamber groups. We also found
several tensions in designing such an agent: calling out harmful content
versus freedom of speech in a private space, giving an agent full access
(privacy concern) versus having humans flag content (may disrupt group
harmony), and the difficulty of including minority viewpoints.

Based on our findings, we discuss the efficacy of deliberation from the lens
of deliberative theory and compare deliberation with existing approaches such
as moderation and fact-checking. We conclude by offering concrete
recommendations to design deliberative systems that help in identifying and
combating harmful content in private group chats on WhatsApp. Overall, we make
the following contribution to CSCW literature:

  1. (1)

We explore the design space of using a conversational agent to facilitate the
deliberation of harmful content in WhatsApp groups. We reveal design tensions
in this space and show how users would interact with such an agent.

  2. (2)

We explore the efficacy of using deliberation to combat harmful content in
WhatsApp groups, borrowing from relevant literature in deliberative theory.

  3. (3)

We provide concrete recommendations to design deliberation systems for this
purpose in the future.

##  2\. Related Work

We first situate our work in emerging HCI and CSCW scholarship on identifying
and addressing different types of harmful content on WhatsApp. We then present
scholarly work on deliberative theory and its applications in facilitating
deliberation and discussion in social media groups. Given our focus on
deliberation through conversational agents, we then discuss studies in HCI and
CSCW that use conversational agents in group communication.

###  2.1. Combating Harmful Content on WhatsApp

A rich body of HCI and CSCW work has studied harmful content on WhatsApp, both
qualitatively (Varanasi et al., 2022; Feng et al., 2022) and quantitatively
(Resende et al., 2019; Machado et al., 2019; Pasquetto et al., 2022). These
studies have found the prevalence of various types of harmful content on
WhatsApp, including political and religious propaganda and polarizing content
(Resende et al., 2019; Machado et al., 2019; Javed et al., 2021), and
misinformation, fake news, and rumors (Varanasi et al., 2022; Feng et al.,
2022; Javed et al., 2021).

Scholars have also studied how WhatsApp users engage with harmful content and
found that while many users can see such content as problematic, they hesitate
to contest it openly in WhatsApp groups to avoid conflict with group members
with whom they maintain social ties offline (Feng et al., 2022; Pasquetto et
al., 2022). Instead, group members often rely on credentialed group members,
such as police officers and journalists, to discover, verify, and contest
harmful content shared in WhatsApp groups (Varanasi et al., 2022). Given the
lack of platform-mediated moderation on WhatsApp due to end-to-end encryption,
WhatsApp users often rely on fact-checking through “tiplines” to access
information credibility (Kazemi et al., 2022; Allen et al., 2021). Tiplines
are platform-run or third-party WhatsApp accounts with which users share
questionable content for fact-checking. However, despite their efficacy in
debunking factual harmful content (e.g., misinformation and fake news) (Kazemi
et al., 2022), tiplines struggle with other kinds of harmful content that are
subjective and opinionated (e.g., hate speech or polarizing content).
Furthermore, fact-checking is often centralized, allowing either human fact-
checkers (Juneja and Mitra, 2022; Haque et al., 2020; Nakov et al., 2021) or
AI models (Reis et al., 2019; Ahmed et al., 2021; Mridha et al., 2021) to
decide the truth (Jahanbakhsh et al., 2023). This is at odds with the freedom
of speech argument which demands that no centralized authority become the
arbiter of truth (Jahanbakhsh et al., 2022; Koltay, 2022). Indeed, users have
expressed distrust in platform-mediated fact-checking due to platforms’
political bias and for-profit policies (Saltz et al., 2021). Moreover,
centralized fact-checking faces scalability problems as there is far more
content online than is possible for human fact-checkers to assess, especially
when fact-checking cannot be fully automated (Juneja and Mitra, 2022; Allen et
al., 2021). As a result, decentralized approaches to combating harmful content
are gaining popularity in the research community (Jahanbakhsh et al., 2022).

Decentralized approaches aim to democratize moderation by allowing users to
actively participate in the moderation process (Jahanbakhsh et al., 2023). A
prevalent approach is to appoint a set of users as moderators who are then
responsible for identifying harmful content on the platform (Seering et al.,
2019b; Cai and Wohn, 2022), a strategy often used on Reddit (Dosono and
Semaan, 2019) and Facebook groups (Kuo et al., 2023). Another approach is
crowdsourcing, which involves aggregating content ratings from a crowd of
users (Allen et al., 2021; Pennycook and Rand, 2019a; Bhuiyan et al., 2020;
Bozarth et al., 2023; Kim et al., 2018). For example, “Community Notes” on X
(Twitter) allows users to collaboratively add context to potentially
misleading posts (Twitter, 2023). While community moderation approaches are
decentralized in the sense that users—not platforms—decide whether the content
is harmful, they still impose a single source of truth (the moderators’ or the
crowd’s decision) upon all users (Jahanbakhsh et al., 2023).

Instead of imposing a singular authoritative source upon users, researchers
have started proposing alternative strategies that empower users to
independently assess content credibility (Jahanbakhsh et al., 2022), such as
by incorporating credibility indicators (Lu et al., 2022; Zhang et al., 2018),
training personalized AI models to predict credibility (Jahanbakhsh et al.,
2023), and incorporating assessments from trusted peers (Jahanbakhsh et al.,
2022). Deliberation is another decentralized approach that involves a
discussion amongst people to gain a better understanding of an issue
(Beauchamp, 2019) and has been shown to support accurate belief formation
(Bago et al., 2020). Like other decentralized approaches, deliberation allows
users to discuss the content and empowers them to form their own beliefs about
the quality of content. Unlike crowdsourcing approaches such as Community
Notes on X (Twitter), the aim of deliberation is not to reach a consensus, but
rather critical thinking and reflection (Bago et al., 2020). Hence, no single
opinion is forced upon users. Such decentralized approaches are more suitable
for platforms like WhatsApp, where end-to-end encryption prevents centralized
moderation. Yet, decentralized approaches have not been studied in the context
of end-to-end encrypted platforms. We fill this gap by exploring the utility
of deliberation to combat harmful content on WhatsApp.

###  2.2. Deliberation and Deliberative Theory

Deliberative theory, also known as deliberative democracy, is a political
theory that emphasizes the importance of reasoned discussion, open dialogue,
and inclusive deliberation in the decision-making processes of a society.
Deliberative theory was developed by Habermas (Habermas, 1985) and Cohen
(Cohen, 1989) in the 1980s in response to the limitations of traditional
democratic theory (Beauchamp, 2019). In particular, scholars argued that
existing electoral systems, such as voting, may be fair and democratic, but do
not produce decisions that best reflect the interests of participants (Bohman,
2000; Ackerman and Fishkin, 2002; Mansbridge et al., 2010). For example,
voters may not know all the facts, may not have considered conflicting
opinions, or may not have considered even their own opinions deeply (Fishkin,
2013). On the other hand, deliberative theory argues that “thoughtful,
careful, or lengthy consideration by individuals” (Davies and Chandler, 2012)
promotes critical thinking and evidence-backed reasoning (Cohen, 1989). Thus,
deliberation allows people to “better learn facts, ideas, and the underlying
conceptual structures relating between them” (Beauchamp, 2019), even under
conditions of conflict and uncertainty (Friess and Eilders, 2015).

In recent years, deliberative theory has been applied to study and organize
civic engagement online. For example, recent scholarly work on social media
shows that the lack of critical thinking promotes belief in fake news, and
deliberation results in the formation of more accurate beliefs (Bago et al.,
2020; Pennycook and Rand, 2019b). Varanasi et al. (2022) reported collective
deliberation practices within WhatsApp groups in India; urban users
deliberated on potential misinformation in parallel “satellite” groups and
rural users deliberated during in-person social gatherings. However,
deliberation done through such informal structures often reinforces existing
power structures and leads to poor deliberation outcomes. For example, the
same study shows that marginalized members in rural communities could not
express opinions during deliberation, and close ties and deference to elders
constrained people from expressing their viewpoints in WhatsApp groups (Feng
et al., 2022; Pasquetto et al., 2022; Scott, 2023).

This is antithetical to the ideals of effective deliberation, which requires
open discussion where individuals present and critically evaluate diverse
viewpoints. It encourages inclusive processes that include diverse
participants from various perspectives and backgrounds. Satisfying these
ideals requires carefully considering three main components of deliberation
defined by Friess and Eilders (2015): the design, the process, and the
outcome. Our work examines these three components of a conversational agent
that creates a formal deliberative structure within WhatsApp groups to foster
more informed, participatory, and reflective discussions on harmful content.
We now discuss prior HCI and CSCW on using conversational agents to facilitate
discussions in online groups.

###  2.3. Conversational Agents in Groups

Conversational agents or chatbots are computer programs designed to converse
with humans using natural language (Hussain et al., 2019). They have been
increasingly used to help maintain, moderate, or grow online communities
(Seering et al., 2018). A review by Seering et al. (2019a) found that most
chatbots are dyadic: they support one-on-one user interactions and are either
task-oriented or chat-oriented. Task-oriented chatbots perform a task or
provide a service to the user upon their request, such as providing customer
service (Xu et al., 2017) or tracking nutrition (Graf et al., 2015). Chat-
oriented chatbots are more performative and are intended to showcase advanced
natural language processing and generation capabilities. However, such dyadic
chatbots rarely support group interactions.

In response, recent work has started to propose multi-party chatbots that
communicate with multiple users in a group. For example, researchers built
bots to mediate task management in teams (Toxtli et al., 2018), engage in a
coffee chat with humans (Candello et al., 2018), and facilitate online
discussions (Lee et al., 2020; Kim et al., 2020). Facilitating discussions has
emerged as an especially important application of multi-party chatbots to
encourage rational debate, which is often lacking in group chats on platforms
such as WhatsApp and Telegram (Kim et al., 2021). This is due to a variety of
reasons. First, group chats, unlike structured forums, are fast-moving,
unstructured, and unthreaded, which makes it difficult for users to keep track
of diverse opinions expressed in the group (Kim et al., 2020). Second, group
chats are associated with procrastination, loss of concentration, and uneven
participation (Duin and Archee, 1996; Kim et al., 2020; Schultz, 2000).
Thirdly, people may not be accepting of contrasting opinions in online groups
(Mutz, 2006). To overcome these problems, recent work has proposed using
chatbots to facilitate effective deliberation in group chats (Kim et al.,
2020, 2021). Integrating chatbots into users’ familiar environment rather than
building external interfaces has proven useful in encouraging deliberation,
facilitating decision-making and open-debating, and helping surface diverse
opinions (Kim et al., 2021, 2020).

However, these works have only considered discussions about non-sensitive
topics such as travel decisions, ethical dilemmas, or estimation (e.g., height
of the Eiffel Tower). In fact, Kim et al. (2021) recognized the need for
“specialized interventions” for deliberation about sensitive or divisive
topics that could be affected by social dynamics. We extend this line of work
by exploring the design of an agent to facilitate the deliberation of harmful
content in WhatsApp groups, a potentially divisive topic affected by social,
cultural, and power dynamics (Varanasi et al., 2022; Feng et al., 2022;
Pasquetto et al., 2022).

##  3\. Methodology

To answer our research questions, we conducted a qualitative study with 21
WhatsApp users in urban and rural India. Our participants were part of
multiple WhatsApp groups and encountered harmful content such as hate speech
and misinformation in their groups.Moved to 3.4 Participants first engaged
with a design probe showcasing a conversational agent for facilitating
deliberation in WhatsApp groups, followed by their participation in a semi-
structured interview.

###  3.1. Design Probe

|
---|---
(a) Setting the context | (b) Misinformation shared
Figure 2. The probe showed a fictional informal WhatsApp group among friends
where sometimes harmful content is shared. The agent is shown in Figure 3 in
the appendix.

HCI researchers often use design provocations to understand the needs and
desires of users in real-world settings and get feedback on new technologies
(Hutchinson et al., 2003). Since our participants might not be familiar with
the idea of using conversational agents to facilitate online deliberation, we
created a design probe to show participants an example of how such an agent
could be implemented. Seeing a tangible example allowed participants to better
comprehend and engage with the concept. This allowed them to think critically
about the capabilities, strengths, and limitations of using a conversational
agent to deliberate on harmful content in WhatsApp groups. The probe served as
a starting point for our discussion with the participants.

We designed the probe using a free WhatsApp mock chat creator called WhatsMock
(Playfake, 2023). The probe first showed a fictional WhatsApp group titled
“Best Friends Forever” (see Figure 2) in which group members shared greetings,
pleasantries, and information with each other. The probe then showed one of
the group members forwarding a message that was tagged as ‘forwarded many
times’111‘Forwarded many times’ is a label introduced by WhatsApp in 2019 to
alert users and slow down the spread of harmful content (WhatsApp, 2023) and
said, “All vaccinated people will die within 2 years.” We curated this message
from a popular fact-checking website in India that debunked it as
misinformation. The probe then showed an example design of a conversational
agent integrated into the WhatsApp group to facilitate the deliberation of
potentially harmful content, so that participants think about the design
critically and concretely instead of abstractly.

|  |
---|---|---
(a) Activation | (b) Polling opinions | (c) Summarization
Figure 3. Different phases where the agent interacts with WhatsApp group
members.

####  3.1.1. Design of the Conversational Agent

Our example probe showed the conversational agent as a member of the group
with the ability to see messages in the group, send direct messages (DMs) to
other users, and have a contact name and a profile picture. We did not give
the agent a name or profile picture to avoid biasing participants about its
functionality. Borrowing from the literature on facilitation bots (Kim et al.,
2020), the agent divided the deliberation process into three phases:

  1. (1)

Activation: The agent gets activated upon seeing harmful content and initiates
the deliberation process.

  2. (2)

Polling Opinions: The agent encourages group members to reflect on the harmful
message and collects their opinions via direct message (DM).

  3. (3)

Summarization: The agent collates the opinions into an anonymous summary and
shares it back in the group for members to reflect upon and deliberate on
collectively.

Figure 1 shows the schematic representation of the three phases and Figure 3
shows an example scenario of the agent functioning in a group.

Overall, our agent design encouraged deliberation in two ways. First, it
solicited anonymous opinions from group members. This facilitated idea-sharing
even in the presence of close ties within the group (Feng et al., 2022;
Pasquetto et al., 2022), thereby prioritizing inclusivity—an essential aspect
of deliberation theory (Fishkin, 2013; Habermas, 1985). Second, it posted a
summary message containing everyone’s opinion. This prompted reflection
amongst group members, setting the stage for further deliberation and
fostering open discussion within the group. We now explain the three phases
listed above in detail.

Activation. The agent gets activated upon seeing harmful content in the group
and sends the following message in the group: “Hi everyone! We should discuss
the validity of this message. I will DM you to ask what you think about this
message.” As prior research shows that nudging people to think critically
about information significantly reduces its propagation (Pennycook et al.,
2021), we showed that the agent sends this message as an accuracy nudge
instead of silently polling users in the background. However, since this was
only a design provocation, we purposely left out many details so that
participants could provide suggestions about different aspects of the
activation process.

Polling Opinions. The agent then reaches out to the group members via direct
message (DM). It greets the user and sends them the harmful message upon which
the agent wants reflection and deliberation. After confirming if the user has
seen the message, it prompts them to (a) rate the accuracy of the message on a
scale of 1–10, and (b) provide reasoning behind their rating. The rating was
intended to encourage participants to assess the message critically. Even if
the content was factually correct (e.g., some hate speech may not be
fabricated), assigning a rating could aid in recognizing its harmful nature.
The agent also asks for the reasoning behind their rating to further encourage
reflection as prior research shows that asking users to reason about their
rating encourages them to think more deeply about the content (Jahanbakhsh et
al., 2023). After the user sends their response, the agent thanks them for
sharing their opinion.

Summarization. The agent collates the responses received from multiple group
members and reports them back to the group in the form of a summary message.
In the summary message, the agent reports the average accuracy rating and
lists the collected opinions anonymously. We listed opinions anonymously
because prior work highlights the importance of anonymity in sharing opinions
on sensitive content (Feng et al., 2022; Varanasi et al., 2022), and this also
enabled us to examine participants’ preferences around anonymity in the
deliberation process. The agent then encourages the group members to
collectively deliberate on the shared summary. To emphasize the time-consuming
nature of the deliberation process, the probe showed that the summary was sent
four hours after the original harmful message (see Figure 3c). While this may
risk resurfacing a harmful message in the group, it has been found that
harmful content left unaddressed can falsely imply to users that it’s not
harmful (Pennycook et al., 2020). Thus, it is important to call out harmful
content it even after a delay.

###  3.2. Semi-structured Interviews

|  |
---|---|---
(a) COVID-19 misinformation | (b) Financial misinformation | (c) Polarizing content
Figure 4. After demoing the agent, participants were shown one of these three
harmful messages. They were asked the same questions the agent would ask them:
to rate the accuracy of the message on a scale of 1–10 and provide reasoning
behind their rating. The three messages were sourced from popular fact-
checking websites in India.

Once participants engaged with the design probe, we conducted semi-structured
interviews with them to understand their needs, preferences, and concerns
about using an agent to deliberate on harmful content. We conducted 21
interviews, stopping when the responses reached theoretical saturation. Each
interview lasted for about 60–90 minutes and was conducted either in-person or
virtually over Zoom in Hindi or English based on participants’ preferences.
Each interview started with a brief introduction to the study and the informed
consent process. After receiving consent, we first asked questions to
understand the demographics of the participants, their use of WhatsApp, and
their experiences with harmful content in WhatsApp groups.

Subsequently, we asked them questions to understand their preferences for
different design features, the perceived benefits and limitations of the
deliberation process, and concerns around embedding an agent in WhatsApp
groups. We then showed them a harmful WhatsApp message and asked how they
would respond if the agent asked for their opinion on this message. Instead of
showing the same message to all participants, we showed them one of the three
messages sourced from fact-checking websites in India: a COVID misinformation,
phishing scam, and politically polarizing content (see Figure 4). We then
asked participants questions about trust, privacy, and the impact of
deliberation on group dynamics.

The final segment of the interview was a design worksheet titled “Let’s Design
this Bot Together!”222The worksheet is attached in Appendix
LABEL:sec:design_worksheet. which the participants filled. In virtual
interviews, the participant shared verbal responses and the interviewer filled
out the worksheet while sharing their screen. The worksheet contained
questions about design decisions associated with the agent, such as possible
activation strategies, how many people the bot should reach out to, and how
long it should wait for people’s opinions, among others. While some of these
questions had already been discussed by this point in the interview, the
worksheet encouraged participants to carefully consider tradeoffs in different
design choices and to clearly communicate their design preferences. Finally,
we thanked the participants for their time and gave them a gift card of INR
300 ($\approx$ $3.67).

###  3.3. Analysis

All interviews were recorded with the consent of the participants. We
transcribed the interviews, translating them into English if required. This
resulted in around 20 hours of audio recordings and 190 pages of
transcription. The first author then analyzed the transcripts using inductive
thematic analysis. During this process, all co-authors met periodically to
refine the codes, merge similar ones, stabilize the codebook, and discuss
emerging themes. After multiple iterations through the data, we ended up with
$156$ codes, which we categorized into eight sub-themes such as activation of
the agent, perception of the agent, and deliberation as a strategy. Finally,
we mapped these sub-themes into the three components of deliberation defined
by Friess and Eilders (2015): design, process, and outcome. Thus, we used a
hybrid inductive-deductive approach, formally referred to as abductive
analysis (Vila-Henninger et al., 2022; Timmermans and Tavory, 2012).

###  3.4. Participants

We used a combination of convenience and snowball sampling to recruit
participants. We started by advertising the study through our immediate
network and interviewed people who expressed interest in participating in our
study. We also partnered with a non-profit organization based in rural India
to recruit WhatsApp users in rural areas. We tried to recruit participants of
different genders, age groups, and urbanity considering that people with
different demographic backgrounds have different experiences with harmful
content (Varanasi et al., 2022; Feng et al., 2022).

ID | Gen | Age | Edu | Occupation | ID | Gen | Age | Edu | Occupation
---|---|---|---|---|---|---|---|---|---
P1 | F | 27 | M | Technician | P12 | M | 28 | B | Student
P2 | M | 24 | B | PhD Student | P13 | F | 40 | M | NGO worker
P3 | M | 26 | M | Auditing analyst | P14 | F | 25 | M | Coaching teacher
P4 | F | 23 | B | Research Fellow | P15 | F | 39 | HS | Housewife
P5 | F | 21 | HS | Student | P16 | M | 26 | B | Factory worker
P6 | M | 24 | B | Business Consultant | P17 | M | 20 | MS | Electrician
P7 | M | 30 | B | Product Manager | P18 | F | 42 | M | Housewife
P8 | F | 23 | B | Student | P19 | F | 29 | M | Village leader
P9 | F | 28 | M | Consultant | P20 | M | 32 | B | Teacher
P10 | F | 27 | B | Product Designer | P21 | M | 32 | B | Factory worker
P11 | F | 24 | B | Creative Associate |  |  |  |  |
Table 1. Participant ID, gender (M: male, F: female), age, educational level
(M: master, B: bachelor, HS: high school, MS: middle school), and occupation
of all 21 participants. All details are self-identified.

Demographics. Table 1 shows our participants’ demographic details. In total,
our study had 21 participants, of which $12$ identified as female and the rest
as male. The participants were in the age range of $20$–$42$ years with an
average age of $28.1$ years. All of them owned a personal smartphone on
average for $7.3$ years and used WhatsApp on average for $6.9$ years. Our
participants had varying literacy levels: $3$ completed high school or lower,
$11$ had a bachelor’s degree, and $7$ had a master’s degree. Some of them were
students and homemakers and others worked as technicians, finance analysts,
NGO workers, and managers. There was an even split of urbanity: $11$ lived in
urban areas and $10$ lived in peri-urban or rural areas.

Experience with WhatsApp. Participants extensively used WhatsApp to maintain
different aspects of their daily lives, such as work, education, and personal
connections, primarily through participation in different groups. These
included social (friends, family, village, college), professional (e.g., job
opportunities), educational (e.g., college classmates), and organizational
(religious, political, workplace) groups. Group sizes ranged from 4 to 200
members. Social groups of friends and family were smaller in size (4-40
members) and those comprising village residents or college classmates were
larger (up to 200 members). The size of the organizational and professional
groups depended on the scale of the organization, with political or religious
groups being mid-sized (20-50 members). In small groups, members typically
knew each other, while larger groups often comprised geographically dispersed
members with weak social ties.

###  3.5. Ethics and Positionality

This study was approved by the IRB of our institution. We took several steps
to conduct research ethically and responsibly. For example, recognizing the
risks associated with deploying an agent in private online spaces, we opted to
use a design probe instead of other methodologies (e.g., Wizard-of-Oz, working
prototype) to protect the privacy of our participants. Additionally, to avoid
the risks of propagating harmful content through this study, we clarified that
the examples shown in the study are misinformation and should not be passed on
to peers.

Our research team consists of authors who have lived in India for a
considerable amount of time. We also have a decade-long experience in
conducting research with different rural and urban communities in India and
other countries in the Global South. As a result, our team had a good
understanding of local sociocultural norms, sociopolitical landscape, and
sociolegal frameworks. This helped us not only build rapport with the
participants but also understand the context within which harmful content
propagates in online communities in India. This cultural proximity also
enabled us to create culturally relevant design artifacts. We approached this
work from an emancipatory action research mindset to understand users’
experiences with deliberation in end-to-end encrypted online communities like
WhatsApp groups. We aimed to inform design recommendations to build
conversational agents that facilitate deliberation on divisive topics in an
understudied context where sociocultural norms and power dynamics influence
how people engage with and contest harmful content online.

##  4\. Findings

As discussed in Section 2, we organise our findings using the three main
components of deliberation presented by Friess and Eilders (2015): the design,
the process, and the outcome. The design (Section 4.1) includes the
institutional framework under which the deliberation takes place, such as the
environment that enables and fosters deliberation (Beauchamp, 2019). The
process (Section 4.2) encompasses the actions of participants and the quality
of communication. And the outcome (Section 4.3) focuses on the (expected)
results of the deliberation. We use this framework to comprehensively evaluate
the role that conversational agents can play in the deliberation of harmful
content in WhatsApp groups.

###  4.1. Design: The Deliberative Environment

Our participants shared several preferences about the design of the agent,
including how it should get activated and initiate deliberation in WhatsApp
groups, who it should invite to participate in the deliberation, and how long
people should deliberate.

####  4.1.1. Initiating the Deliberation

While describing the agent, we told participants by way of example that the
agent would get activated whenever any harmful content would be shared in the
group. However, we did not specify details about the activation process, for
example, how the agent would know whether a message is harmful or not. This
ambiguity encouraged participants to reflect on different ways in which an
agent can be activated to initiate deliberation and the resulting tradeoffs.
In our analysis, three broad activation strategies emerged: heuristics-based,
AI-based, and manual activation.

Heuristics-Based Activation. Participants initially proposed that the agent
should monitor content and get activated automatically when it detects certain
indicators of harmful content. For example, they suggested the agent could use
WhatsApp-provided tags (“forwarded” or “forwarded many times”) to detect
harmful messages since these tags were considered a “hallmark of harmful
content” (P2). Others suggested using features such as message length,
embedded links, emojis, and font styles (e.g., excessive use of bold or
italics) to detect potentially harmful content. Some participants suggested
that the agent should get activated on “suspicious” phrases such as slurs
(e.g., misogynistic or racial terms), political keywords (e.g., war,
elections), or words commonly appearing in monetary scams (e.g., “send
money”).

However, some participants pointed out that these heuristic-based methods were
susceptible to false positives and may end up flagging content that may not be
harmful. For example, P5 worried that if the agent detects keywords, it might
flag group members’ personal opinions instead of “external” content (e.g.,
news reports), suggesting that users may not perceive personal opinions as
harmful. P5 elaborated:

> “If I say a racist or homophobic thing, it can be my opinion…it may not be
> news. So it will get activated on that…and people will feel that their free
> speech is curtailed.”

Thus, users preferred using the agent to deliberate on forwarded messages
instead of their “personal opinions” even though they may be toxic or hateful
towards others. This raises an important design tension between maintaining
freedom of speech in a private space and calling out harmful content in the
group.

AI-Based Activation. Some participants wanted the agent to be intelligent
enough to detect various forms of harmful content, including graphic images,
sensitive content, non-credible sources, and harmful audio/video messages. P16
stated:

> “There must be something in the app through which it will know [what is
> harmful], the agent must have some capability.”

Upon receiving these suggestions, we asked participants what was the need for
deliberation if we already possessed an omniscient agent that could identify
all harmful content. After reflection, they recognized the challenges in
building such an agent and adjusted their expectations accordingly. P8
suggested monitoring the tone of conversations in the group and activating the
agent when the group fell into a fight or argument. Others suggested using
historical evidence to identify harmful content. For instance, P1 proposed
that the agent could use group members’ opinions to learn the features of
harmful content over time, an approach similar to that used by Jahanbakhsh et
al. (2023). P1 elaborated:

> “Will the agent be learning all this while?… Will it be able to filter out
> misinformed content in the future instead of reaching out every time? … That
> means it won’t message people asking for their opinion every time.”

P1 argued that this would allow the agent to identify harmful content
autonomously, ultimately reducing the frequency of the deliberation process
over time.

This idea exemplified a general suggestion of human-AI collaboration where the
agent would reach out to participants only when it couldn’t identify harmful
content autonomously, thereby reducing their workload. Another such example
came from P19 who suggested that the agent could find relevant sources (e.g.,
news articles, fact-checks, etc.) and compare the message against them to
gauge veracity before reaching out for opinions.

> “This agent will work better if it could search for an authentic source on
> its own, be it any report from the government or international organization
> or a standard newspaper… So if it could match the news with any such sources
> and back its existence. That is better than taking people’s individual
> opinions. That would be more authentic.” – P19

Both the above activation strategies—heuristics-based and AI-based—were
automatic: the agent would have to “see” or “scan” all messages in the group
to get activated. This was perceived as a privacy concern in smaller groups,
where participants didn’t want the agent to monitor their personal or
confidential conversations and family photographs. When we suggested the
option to disable the agent during personal conversations, participants
considered it infeasible in practice.

> “I don’t know how that [disabling the bot] could work because it’s not like
> you want to say something so you’re like, “Okay, let me go and disable the
> agent,” and only after that you’ll say it. You just go with the flow,
> right?”

Manual Activation. Privacy concerns in automatic strategies prompted a
discussion about the manual strategy. Participants suggested that the agent
should refrain from actively monitoring the group. Instead, users could
anonymously invoke the agent if they perceive a message as harmful. The agent
would then get activated and start the anonymous deliberation process within
the group. Even though the agent would still exist in the group, participants
found comfort in thinking the agent would not “scan” all messages in the
group.

Apart from safeguarding privacy, participants felt that, unlike the automatic
techniques, manual activation would give them the agency to activate the agent
and decide when it should be invoked and on what content. As P6 explained, he
didn’t want every harmful conversation to be flagged by the agent:

> “This would be an excellent strategy, because, for instance, the group that
> I have with my close friends…we are accepting of different political and
> individual ideologies of our friends. Normally we don’t talk about
> problematic or offensive things. We just normally share jokes or gossip.” –
> P6

Participants pointed out that this way, the agent would be activated less
frequently and save them the time and effort needed to respond to the agent.
Moreover, they felt that invoking the agent anonymously would enable them to
safely express dissent in the group and nudge group members to not share
problematic content.

However, participants believed that in smaller, close-knit groups, where
members knew each others’ opinions well, people could guess who might have
activated the agent even if it was done anonymously. This concern raised the
potential for accusations and conflicts within the group, negatively impacting
group dynamics. Hence, participants unanimously agreed that manual activation
would be more suitable in larger groups.

Overall, these findings show the inherent trade-off between privacy and
fostering positive group dynamics. While automatic approaches may compromise
privacy, they have limited impact on disrupting group dynamics (since group
members cannot be accused of setting off the bot). Conversely, manual
approaches would preserve privacy but might hurt group dynamics.

####  4.1.2. Participation: Who Should Deliberate?

The probe showed that after getting activated, the agent would reach out to
group members to solicit their opinions. We asked participants which group
members the agent should reach out to. Two primary strategies emerged:
reaching out to all members or a random subset of the group. These strategies
had trade-offs in terms of representation and workload.

Participants agreed that soliciting opinions from all group members would
allow everyone (including gender, political, or other minorities within the
group) to express their views. However, they also recognized that if
everyone’s opinions were taken, the majority viewpoints would dominate each
conversation, overshadowing the perspectives of the minority.

> “If it’s asking everyone, then some people will just be able to answer every
> time. ” – P6

Hence, P6 argued to invite a random subset of group members for deliberation.
However, other participants raised concerns that a randomly selected sample
would risk excluding minority opinions, leading to skewed representation and
outcomes. This dilemma highlights the difficulty participants reported in
balancing the inclusion of minority opinions, as both approaches—asking
everyone or selecting a random subset—could result in an unfair representation
of minority viewpoints.

Another common concern among the participants was that participating in the
deliberation would increase their workload. With the first strategy (asking
all members), participants were concerned that the agent would reach out to
them too frequently, which might turn them off. Further, in larger groups,
this strategy would lead to a summary message with a long list of opinions
that would be arduous to read. Therefore, participants suggested asking for a
random subset of group members. However, that ran the risk of not receiving
enough opinions in smaller groups. Considering these trade-offs, P10 suggested
a hybrid approach: asking all group members for their opinions in smaller
groups (up to 10 members) and asking a small random subset of the group in
larger groups. P10 elaborated:

> “So, maybe…you do a cap of 10 [i.e., asking 10 members for their opinions].
> So in the smaller groups [with $\leq 10$ members], everyone gets reached out
> to and in bigger groups, 10 people get reached out to at random.”

We also proposed two other strategies in the design worksheet based on prior
work. The first option was based on crowdsourcing to identify harmful content
(Kazemi et al., 2022; Kim et al., 2018; Bhuiyan et al., 2020) and suggested
that the agent could reach out to people _outside_ the group for their
opinions. The second option involved asking only “senior” group members (e.g.,
elderly members, domain experts, people in positions of power) based on prior
work that found the existence of credentialed “gatewatchers” in WhatsApp
groups (Varanasi et al., 2022). To our surprise, our participants unanimously
struck down both these ideas. They believed that people outside the group
would not have the context of their group and that there was no way to measure
“seniority” and “expertise” in WhatsApp groups. P6 shared:

> “How do you determine senior members? Because WhatsApp doesn’t necessarily
> ask you your age. And if seniority is determined by how long have you been a
> part of a group, that doesn’t mean anything because people might just change
> their number and leave the group and that would affect their seniority. So I
> don’t think seniority [of any kind] should have anything to do with the
> agent.”

RepetitionMoved to 4.2.1.

####  4.1.3. Duration of the Deliberation

We also asked participants how long the agent should wait for responses before
sending a summary back to the group. This was an important design decision
because, as P4 said, “You’re demanding someone’s time who might be busy.”

Participants suggested that the agent could wait until everyone responded.
However, they pointed out that some people may be inactive on WhatsApp and
might never reply. Hence, participants proposed an alternative — that the
agent should wait for a predefined amount of time (a few minutes, hours, or
days). They said that if people are given too little time, they might not get
enough time to read, think, and respond, particularly if the agent requests
them while they are busy with work, personal chores, or sleeping. On the other
hand, if the agent waits too long, the group’s context may change and
conversations might move on, especially in larger groups. P8 described:

> “[If the agent waits too long,] people may start talking about something
> else completely… Maybe people are over it and it’ll re-instigate the whole
> thing again… Like, if people are ready to get over it, there’s no point of
> the agent to [force the deliberation].”

Hence, participants went back and forth in deciding the optimal waiting time,
trying to balance the availability of group members and the context of the
group. After discussion, most participants agreed that if the agent gave them
one day to respond, it would accommodate their work schedules and differences
in the time zones of the group members. When asked about the risks of
resurfacing harmful messages from the previous day, participants commented
that “what matters is whether the harmful content is called out or not” and
that conversations would remain recallable within a day. P14 described:

> “Group members should be given one day because… they can only check when
> they get some off time during the day. For instance, if somebody is doing a
> 10-5 job then after coming back at 5, they will check the group [and the
> agent] in some time, after they have relaxed.”

One participant, P2, also suggested waiting for a certain number of responses,
rather than a predefined time, or a combination of the two: “If a certain
fraction of people reply, the agent should just report back. If not, maybe
after a certain time it should report back.” However, P11 said that ending the
process early would open the doors for bias, as vehement defenders would
respond quickly, fill the spots, and leave no room for opposing views. This
shows that seemingly unrelated design decisions such as the duration may skew
the deliberation process and shape whose voices get included.

###  4.2. The Process: Anonymous Deliberation

Next, we move on to the second dimension of deliberation: the process, which
includes the actions of the participants and the quality of their
communication. Below, we explain how participants envisioned participating in
the deliberation process facilitated by the agent.

####  4.2.1. Responding to the agent

Overall, participants preferred doing careful research before responding to
the agent instead of instinctively responding to it, but were wary of the
effort this might require. As a result, they said that instead of responding
to the agent always, they would respond selectively.

Researching Before Responding. In our probe, when the agent would reach out to
participants to solicit their opinions, it would ask them to rate the message
and provide reasoning behind their rating (see Figure 3b). To our surprise,
very few participants said they would respond to the agent based on their
instincts or other indicators (e.g., emojis, bold, sender’s reputation, etc.).
Instead, most participants preferred researching the topic by looking it up
online or asking friends before responding to the agent. They believed that it
was important to know all the facts before presenting their opinion. P14
mentioned:

> “How can we just make a statement without actually knowing in depth about
> it… But if the agent asks me something that I know about already, I will
> answer immediately.”

Too Much Effort. Participants expressed concern that anonymous deliberation
would increase their workload. In order to reply to the agent, they would now
have to pay attention to the content that they would have otherwise ignored.
Similarly, responding to the agent would require research and typing out an
appropriate response, which would mean more effort. Participants believed that
doing this additional work on something they otherwise could ignore might
cause them more stress. They mentioned that group members might put in this
effort initially, but over time, they would respond briefly or not respond at
all. This theme of additional workload was recurring in our study (also seen
in the previous section), highlighting a design tension between combating
harmful content and minimizing user workload.

Participants suggested some approaches to reduce their workload in researching
content and responding to the agent. For example, they asked if the agent
could help them find credible sources of information relevant to the current
content, instead of having to search themselves. P10 emphasized:

> “Cut out [i.e., automate] the verification part, the part that my
> grandmother can’t do by herself. If that was done for her…[that would be
> better].”

Further, a few participants wanted to reduce their effort in typing out their
opinions. They believed that there would be a finite set of reasons why a
certain message would be harmful; the agent could provide them with options
(e.g., drop-down list, WhatsApp polls) to choose from instead of requiring
them to type their opinions every time. They also suggested that the agent
could help them write reasoning that presents constructive arguments in an
emphatic tone.

Participants also pointed out the sheer volume of harmful content they
received across multiple groups and were worried about getting spammed with
too many requests from the agent. They envisioned finding it especially
annoying or distracting if the agent reached out to them while they were
working. As such, participants didn’t want to respond to the agent more than
1-3 times a day and hence expected to sometimes ignore the agent. They also
suggested that the agent could pool requests and send notifications when they
are available (e.g., evening hours) to minimize distractions.

Selective Responding. One strategy participants suggested to reduce workload
was to be selective in responding to the agent. They commented that they would
respond only during “peak events” such as when societal tensions were
heightened (e.g., riots) or when the message could affect big outcomes (e.g.,
elections). P10 distinguished between big and smaller peaks:

> “It depends on the pulse of the nation… During COVID, I cared a lot about
> accessing the credibility of health information because the infection was
> potentially life-threatening. Right now that movie came out, Kerala
> Stories333A religiously divisive film that was touted to have won an Oscar
> when in reality it was only India’s entry for the award., and there have
> been messages about that. That’s like a small peak, so if I had the agent
> right now, it would be helpful…”

Similarly, other participants only cared about debunking content in a few
groups (e.g., family groups) and stated that they would ignore the agent’s
request if the message was received in other groups (e.g., neighborhood
groups). Another set of participants said that they would reply to the agent
only if they were interested in the topic. P11 stated:

> “I think it depends on how much the topic affects you. Like if it’s
> something very personal to you, then you might get quite triggered by it and
> be like, yes, I have to prove them wrong. But if it’s just something you
> find stupid, you’re like, okay, these people are foolish to believe it, but
> you don’t really care to change their opinion.”

Instead of receiving such requests from the agent and having to ignore them,
some participants preferred the option to opt out of the agent’s requests in
groups where they were not active or in topics they were not interested in
addressing.

####  4.2.2. Perceptions of the Summary Message

Our probe showed that after collecting opinions from group members, the agent
would compile them into a summary message to be sent back to the group. While
participants appreciated reading other group members’ opinions, they
emphasized that the summary was only a collection of opinions and should not
be treated as a fact-check on the shared message.

Anonymity. All participants agreed that the opinions presented in the summary
message should be anonymous, i.e., they should not be attached to the names of
the group members. They believed that anonymity would help them speak up
without worrying about repercussions when harmful content is shared by those
in a position of power (e.g., boss, school principal, elderly family member),
a concern also raised in prior work (Varanasi et al., 2022). P13 elaborated:

> “No, the names should not be mentioned. Because if something good happens,
> people will be happy about it, but if it is the other way around, they will
> take offense to that.”

One participant, P10, preferred staying anonymous most of the time but not
always, so she suggested making it the default but optional. She said:

> “I would identify myself in situations where I have expertise and clout,
> where people might be inclined to trust me. Where they won’t trust me, I
> won’t identify myself.”

Participants expressed that anonymity made the agent non-confrontational, that
this was its “biggest” strength. Given this unanimous preference for
anonymity, we asked participants if any features of the summary message might
lead to de-anonymization. In the summary message shown in the design probe,
members’ opinions were displayed in serial order of when they were received.
However, participants felt that the opinions should be displayed in a
randomized order. They argued that some people are chronically online, so it
may be possible to identify people based on their response speed. Similarly,
other participants thought that vehement opposers (or defenders) of the
content would respond quickly, which could cluster similar responses at the
top of the list and bias readers. So, they recommended randomizing the order
to maintain neutrality.

Similarly, many participants noticed that it was possible to identify people,
especially close ties, based on how they composed their messages, e.g., by
their use of punctuation and shorthand. They asked if the agent could rephrase
the opinions so that linguistic patterns could not be used to identify the
person. They suggested that the agent could fix grammar, edit abusive or
attacking words, and remove emojis to protect anonymity. However, some
participants were opposed to the agent rephrasing their opinions and preferred
to see a verbatim copy. They feared that the agent might misrepresent their
feedback and spoil the “sanctity” of user’s opinions. Further, a few
participants felt that people would value human opinions more and rephrasing
by the agent would diminish the significance of the feedback. Some
participants felt that people themselves might write differently to avoid
being identified.

Intelligent Summarization. While some participants were opposed to editing
group members’ opinions, many participants saw value in summarizing opinions
into a single paragraph instead of listing each opinion separately.
Participants felt that doing so would not only safeguard the anonymity that
participants dearly valued, but would also improve readability. These concerns
emerged especially for large groups in which participants felt that listing
each opinion would lead to a long summary message. P7 expressed:

> “There needs to be some sort of summarization I’d imagine, because if it’s a
> larger group… I wouldn’t sit and read 30 different points from people or 50
> different or 100 different points, which may have gone out.”

This way, participants believed that the agent could highlight the “main
points” by putting together the cumulative knowledge gained from different
people. This would also reduce the workload as they would no longer “have to
write everything perfectly.” Further, in creating the summary, the agent could
parse each opinion and prioritize those backed by evidence from credible
sources, filtering out those that are uncivil, divisive, or not well-reasoned.

Some participants did not want to read even a textual summary and recommended
that the agent produce visual summaries. They highlighted that they would only
read the average rating reported by the agent without paying much attention to
the reasoning. They suggested visual summaries where the agent could mark a
positive, neutral, or negative opinion with red, yellow, and green, so that
people could understand the prevailing sentiment with a quick glance.

####  4.2.3. Reflection After Reading the Summary Message

We also asked participants if the agent should do anything after posting the
summary message such as explicitly encourage further deliberation or moderate
deliberated messages.

Encouraging further discussion. Most participants agreed that the agent should
only report the summary message in the group without explicitly encouraging
further deliberation. They wanted the agent to let “the feedback do its own
job” and allow people to reflect and decide for themselves what they want to
believe. This aligns with other decentralized approaches where users are
empowered to form their own beliefs about the content they encounter online
(Jahanbakhsh et al., 2023, 2022). Additionally, participants believed that
individuals would naturally engage in further discussion if they had
additional thoughts, without the agent needing to prompt them explicitly. P10
stated:

> “I don’t think I’ll read ‘you should discuss this’ and then choose to
> discuss it.”

Another reason for this choice was that any further discussion in the group
would not be anonymous and could lead to bitterness. To avoid such conflicts,
some participants floated the idea of a follow-up deliberation session
facilitated by the agent itself. For example, they proposed that a few hours
after posting the summary message, the agent could reach out to group members
again and get feedback on whether their opinions have changed or if a stronger
consensus could be formed given the opinions presented in the summary message.

Moderating deliberated messages. We also asked participants if the agent
should moderate messages that had received poor accuracy scores during
deliberation. Although WhatsApp currently does not allow autonomous agents to
moderate content, we wanted to explore the capabilities participants would
like the agent to possess. The participants were ambivalent about moderating
content based on the summary message, highlighting that the summary surfaced
only opinions, not facts. For example, when we asked if the agent should
delete a message from the chat if it received negative feedback, participants
commented that actions such as deleting content or warning users might create
a negative atmosphere. Instead, they recommended softer moderation strategies,
such as disabling forwarding on messages while they are under deliberation
(but not after). This balanced users’ agency and safety: it would allow them
to forward a message if they found it trustworthy even after reading the
deliberation summary. P5 described:

> “Once a discussion happens and the results come, people will be more
> sensitized towards it… People who [would have] forwarded it mindlessly will
> [now] be more wary of forwarding if three people are not agreeing to it. And
> the discussion might lead to somebody going on the Internet and finding out
> a more credible source. That will stop many people from forwarding the
> message.”

Another approach that participants recommended was to tag messages that
received low scores. For example, participants mentioned that the agent could
make visual changes, for example, change the font color to grey to reduce
emphasis for future readers or add a red exclamation mark to emphasize the
questionable veracity. They believed that doing so would reduce the illusory
truth effect (Feng et al., 2022) where users start to trust harmful content
the more they see it. However, participants realized that tagging came with
the trade-off that it would not stop unconvinced group members from forwarding
the content. These findings highlight a design tension between maintaining
user agency in sharing content and stopping the circulation of questionable
content.

###  4.3. Outcome: Is Anonymous Deliberation Helpful?

Friess and Eilders (2015) define the outcome as the expected results of the
deliberation. Throughout our study, we were careful to dissociate the approach
(i.e., anonymous deliberation facilitated by an agent) from its specific
instantiation (i.e., our design probe). This was important to understand the
impact of anonymous deliberation in combating harmful content. Participants
noted both the strengths and pitfalls of using conversational agents to
facilitate anonymous deliberation.

####  4.3.1. Strengths of Anonymous Deliberation

Most participants agreed that the agent would help identify and contest
harmful content in their groups. They felt that the agent would help “filter
the right from the wrong” using knowledge within the group, and help them hear
each others’ opinions. They described several benefits of anonymous
deliberation.

Anonymity. Most participants said that the anonymous nature of the
deliberation would allow them to speak out against harmful content. Most of
the participants were part of WhatsApp groups in which they maintained offline
social ties with other members of the group. Hence, they feared that
deliberation without anonymity would jeopardize their relationships with group
members or upset authoritative figures, such as elder family members,
superiors at work, among others. As a concrete example, P15 described how such
an agent can help parents express displeasure in a WhatsApp group in which her
child’s school principal often sends political propaganda, but no parent has
been able to speak up for fear of backlash for their children. P2 elaborated
on how anonymous deliberation might help address power differentials in groups
and help surface more reasoned voices:

> “The most extremist voices are the loudest, and that does not represent the
> group. There are enough people who believe the other way, and just knowing
> that fact can help.”

Some participants emphasized that anonymous deliberation through an agent
would help limit long-drawn arguments and encourage group members to feel more
comfortable speaking up. For example, P11 felt that anonymity would limit
personal responsibility: “If the person is not convinced, you don’t feel
personally responsible to continue defending.”

Neutrality. Participants emphasized that any agent that facilitates
deliberation must be neutral and preserve users’ agency. For example, they
appreciated that the agent in our design probe asked for opinions instead of
telling them what was right or wrong, making them feel respected and
important. They felt that since the agent would be a neutral mediator in the
group, it would encourage people to focus on the facts and avoid personal
attacks. While negative feedback on content could make the sender feel
attacked, participants felt that mediation through an agent would dilute such
feelings compared to direct confrontation with other group members. P10
explained:

> “If I say, ‘Uncle you are wrong,’ that’s an issue. But if the agent states
> that a member said this might be false, that comes across as mild and
> acceptable.”

Participants also felt that a neutral agent would help impartial group members
form an opinion without alienating them. For example, P6 explained that when
arguing about a message, the discussion tends to get heated and people use
less polite language. Such heated conversations drive away people who are yet
to make up their minds as they tend to focus on the argument’s tone rather
than the facts presented. He believed that a neutral facilitator would
encourage people to comment on the veracity of the content instead of
expressing their political opinions. He said:

> “An agent which is not politically inclined gives context that it’s not
> about different political opinions, it’s about the truth, it’s about
> decency, it’s about morality…”

Accountability. Participants felt that not only will deliberation help people
identify harmful content propagating in their groups, it will also encourage
them to “think twice” before sending suspicious or misleading messages to
avoid being flagged by the agent. Hence, P4 believed that the deliberation
process would create accountability within group members. P3 elaborated:

> “It’s a common tendency of human psychology that whenever they see negative
> information, without even giving it a thought, they circulate it to a large
> number of people… The agent will stop people from forwarding the messages
> blindly… What the agent does is at least it gives people a second thought
> and [a chance] to develop interest around that information…”

Strength in Numbers and Diverse Opinions. A strong argument for deliberation
was the strength in numbers. Participants emphasized that the groups they
inhabited had diverse people who likely knew whether something was harmful or
not, but hesitated to speak out to avoid conflicts with other group members.
For example, P13 mentioned that her groups have journalists, advocates, police
officers, media people, social workers, and doctors, who had opinions they
felt uncomfortable sharing. Participants felt that the deliberation process
would help surface the opinions of such group members. They drew a parallel
between the agent and the panchayats444A panchayat comprises elected
representatives in Indian villages who collectively make decisions on local
governance and conflict resolution., highlighting that both processes are
based on decision-making through deliberation. P14 articulated:

> “In a group of ten people, all of them are never going to label something
> accurate if it is not…And if all ten consider it accurate, then there must
> be some authenticity to it.”

Participants emphasized the importance of hearing diverse opinions. They
believed that a “collective thought process was better than everyone’s
individual opinions” (P20). They emphasized that hearing everyone’s opinions
would enable them to understand other perspectives, which would either help
them recognize misunderstandings, confront their own biases, or, if they’re
right, find more support for their worldview. P6 elaborated:

> “In big groups, there is necessarily some kind of diversity. People of
> different political inclinations are members of the group. So, if there is a
> problematic news article shared by a person and I find it offensive…then I
> may receive the deliberation from others who are not of the same political
> inclination as me but they also perhaps find it problematic, right? So it
> would just increase the veracity of the deliberation, that the deliberation
> is not propelled by one group. The deliberation itself is diverse…All sorts
> of people have problems with this message. And then, a neutral party should
> be aware that this message perhaps is not in the best of their interests…”

####  4.3.2. Pitfalls of Anonymous Deliberation

While acknowledging the strengths mentioned earlier, participants also pointed
out the drawbacks of engaging in anonymous deliberation through a
conversational agent. In addition to privacy concerns about the content that
the agent would have access to, they were worried about the impact on group
dynamics and limited usefulness in certain contexts.

Impacts Group Dynamics. Many participants said that the deliberation process
could upset group members. The sender could feel attacked by the agent getting
activated or by receiving unfavorable feedback from others. In small groups of
friends and family in which people have strong social ties and know each
other’s ideologies or linguistic styles, participants worried that it would be
easy to identify who said what, which might lead to bitterness, accusations,
and conflicts. They were also concerned that some group members may misuse the
veil of anonymity to make disrespectful comments and personal attacks. In line
with the amplification theory proposed by Toyama (2011), participants
emphasized that an agent can only amplify the group’s existing intent to
contain harmful content, not replace it. P11 said:

> “But I think it ultimately comes down to how the discussions in general go
> in the group. If it’s a little hostile environment in general, it is going
> to be headed that way, you can’t really avoid it.”

Going beyond groups with close ties, participants pointed out that the agent
may lead to fights even in large public groups where people may not know each
other or their ideologies. P19 compared such groups to public social media
platforms:

> “Anonymity doesn’t matter… In large groups, in YouTube comments, people
> don’t know each other, so it’s sort of anonymous, but fights still break
> out. It’s about whether something was said that’s against their opinion.”

Figure 5. Summary of findings from our study

Flagging More Helpful than Deliberation. Some participants worried that the
deliberation process would surface opinions—which could be biased—instead of
facts. Hence, they wouldn’t trust the process to debunk fake news or
misinformation. However, they found the agent useful for _flagging_ harmful
content. They believed that an indication of a message being potentially
harmful was more useful than the deliberation itself. They felt that a message
being flagged would encourage people to do their own research and reflect on
its contents. P14 stated:

> “If the agent gets activated, it tells me that there may be something fishy
> going on and then I will go and search for it.”

In this sense, the flagging of content by the agent can be construed as
accuracy nudges, which have been shown to improve sharing attitudes (Pennycook
et al., 2021). Another participant liked how Instagram and Twitter would
similarly flag posts containing potentially harmful content. P7 articulated:

> “The approach that everybody [other platforms] seems to be taking online is
> that here’s some questionable content, so I will write ‘questionable
> content’ in bold letters below it. Now… I am less likely to forward it
> because then whoever gets it will see that I sent them something that’s
> ‘questionable content’. So there’s some social reputation or social currency
> which is at risk…”

Participants felt that the deliberation process was a very roundabout way of
achieving the benefits that could be achieved simply by flagging harmful
content. They argued that WhatsApp could simply show a popup asking users to
confirm if they wanted to forward a message to others, a strategy also
recommended by Feng et al. (2022). However, some participants felt that simply
flagging content would lose value over time as people would ignore the flag,
much like how people ignore the labels (“Forwarded” and “Forwarded many
times”) used by WhatsApp (Hall et al., 2023). One participant emphasized that
deliberation is better than flagging as it helps surface why something is
questionable and not just whether it is questionable.

Futile for Echo Chambers. Some participants believed that deliberation would
not be helpful when people have strong opinions. They did not expect the
quality of content to improve in extremist political or religious groups that
“relish in sharing problematic content.” They emphasized that deliberation was
only helpful in moderate groups where some individuals were neutral. P7
mentioned:

> “If you’re talking about the extremes of hate speech, things which lead to
> lynchings and stuff, I feel a majority of that information is in groups
> which are echo chambers… There is a bell curve of people, where there are
> some people on the fringes and a majority of them are in the middle and
> those are the people you’re targeting this agent for.”

Similarly, participants felt that deliberation would not prevent harmful
content from users who may genuinely believe in the information they encounter
on WhatsApp. P7 gave one such example:

> “This person probably thinks, ‘Oh my god, vaccines are going to kill us… I’m
> genuinely concerned for my friends and family, so I need to share this
> information with them.’ In that case, I don’t believe they’re going to not
> post it.”

While the agent would flag such content and invite deliberation upon it, P7
was doubtful that it would teach naive users how to identify such content in
the future or discourage them from posting such content. For this reason, some
participants advocated building media literacy through conversational agents
rather than countering each specific instance of potentially harmful content.

##  5\. Discussion

Design Tension | Description | Findings
---|---|---
Privacy | Group dynamics |  | Is it acceptable to risk privacy in
---
order to maintain group harmony?
4.1.1
Freedom of speech |  Circulation of harmful content |  | Should an agent call out hate
---
speech in informal conversations?
4.1.1
Burden on user |  | Should users have to put in effort
---
to combat harmful content online?
4.2.1
User agency |  | Should users be allowed to forward
---
content known to be harmful?
4.2.3
Table 2. Design tensions revealed in our study. The first two columns show the
dimensions that may be in conflict while designing systems to deliberate on
harmful content.

Our findings are summarized in Figure 5. They surfaced several strengths and
limitations of using conversational agents to facilitate deliberation on
harmful content in WhatsApp groups. They also revealed several design
tensions, summarized in Table 2. Using deliberative theory as a lens, we now
discuss how deliberation was actualized in our study and its efficacy in
combating harmful content. We then compare deliberation with other approaches
to combat harmful content. Then, we distill implications for designing an
effective deliberation system for combating harmful content in end-to-end
encrypted online spaces like WhatsApp groups that are outside the oversight of
platform-mediated moderation. Finally, we discuss the limitations of our study
and directions for future work.

###  5.1. Actualization of Deliberation

Deliberation is a discussion between individuals to better understand an
issue, form opinions, and inform decision-making (Beauchamp, 2019). The
mechanics of deliberation that emerged in our study context had some
differences compared to conventional deliberation spaces imagined by scholars
of deliberative theory (Habermas, 1985; Cohen, 1989; Fishkin, 2011). These
differences emerged primarily due to the participants’ unwavering preference
for anonymity in the deliberation process. For example, the agent collated
members’ opinions into a summary message to allow them to read others’
opinions, reflect on them, and set the stage for further deliberation. While
our participants appreciated reading diverse opinions in the summary message,
they were ambivalent about further free-flowing deliberation as that
discussion would not be anonymous.

This actualization of deliberation was different from the traditional
conventional process that would typically involve a back-and-forth among the
discussants. Hence, the complex sociocultural context of WhatsApp groups
manifested deviations from traditional deliberation processes, especially when
the content being discussed could be divisive. That said, the deliberative
process that emerged still adheres to the key principles of deliberative
theory (Fishkin, 2013; Habermas, 1985). The anonymous deliberation allowed
public reasoning by facilitating the exchange of ideas asynchronously. The
anonymity afforded inclusivity and equality by allowing members lower in
social hierarchies to present their views without fear of upsetting power
differentials. Finally, the process encouraged critical reflection by asking
users to rate and reason about harmful content. Hence, while deliberation
might actualize differently in this context, it still adheres to key
properties of the deliberative process and holds promise in allowing
individuals to inform opinions on harmful content propagating in WhatsApp
groups.

###  5.2. Efficacy of Deliberation

Most participants in our study believed that anonymous deliberation was useful
in improving the quality of discourse in their groups. They perceived many
benefits of it, such as anonymity and neutrality, nudging people to improve
sharing practices, and strength in the diversity of opinions. Participants
also highlighted several limitations, including its futility in echo chambers.
This concern about the _efficacy_ of the approach requires a deeper
discussion. Our participants emphasized that anonymous deliberation surfaces
individual opinions which may be biased or misinformed, and not facts. Hence,
they were concerned about its efficacy in combating fact-based content such as
fake news and misinformation. This raises several important questions: Is
deliberation effective when it primarily surfaces opinions rather than facts?
Why would people change their behavior without robust fact-checking, based on
potentially conflicting opinions? Is there intrinsic value in the deliberation
process even if it does not reveal facts? We turn to deliberative theory to
answer these questions.

Of the three components of deliberation (design, process, and outcome), early
deliberative theory (Habermas, 1985; Cohen, 1989) focused disproportionately
on the outcome. The goal of deliberation was to produce a rational discussion
that necessarily led to a consensual outcome that all participants agreed
upon. Soon, however, this “consensual” view of deliberation gave way to a
“procedural” view: In the second wave of deliberative theory, the process
itself, not the outcome, became the fundamental part of the deliberation. This
resulted from a growing understanding that consensus is difficult or even
impossible to achieve when interests are fundamentally opposed (Beauchamp,
2019). In addition, focusing on the outcome was futile because a fake
consensus was easy to reach without following a deliberative process at all,
for example, by forcing people into agreement (Beauchamp, 2019).

As a result, modern definitions of deliberation emphasize the process rather
than the outcome. For example, Beauchamp (2019) defines it as an extended
conversation between people to gain a better understanding of some issue.
These definitions are procedural instead of outcome-oriented. Shifting the
focus from reaching a consensus to emphasizing the process enables
deliberation within groups of self-interested actors (Mansbridge et al., 2010)
or individuals with moral disagreements (Gutmann and Thompson, 1998), such as
in WhatsApp groups where people may have conflicting opinions. Moreover,
deliberation prompts individuals to confront problems, consider the context,
and reflect on their own biases instead of overlooking them.

Thus, drawing on scholarly work on deliberative theory (Kim et al., 2021;
Beauchamp, 2019), we argue that even without fact-checking or achieving
consensus, the deliberation process brings value by fostering fairness,
authenticity, diversity, and reasoned dialogue among participants. As
Beauchamp (2019) noted, it allows people to “learn facts, ideas, and the
underlying conceptual structures relating between them.” In addition to our
findings, which provide empirical evidence in support of deliberation, other
studies also note that deliberation motivates people to read materials that
aid in individual learning and attitude change (Davies and Chandler, 2012).
Thus, deliberation becomes an end in itself, allowing individuals to gain
knowledge and shape opinions within the complex landscape of WhatsApp groups.

###  5.3. Deliberation, Moderation, and Fact-Checking

Two main approaches have been identified by researchers to combat harmful
content on social media: content moderation and fact-checking. However, both
these approaches have proven ineffective on WhatsApp. Due to end-to-end
encryption, platform-led moderation is infeasible in WhatsApp groups, while
sociocultural factors deter group members and admins from moderating content
(Shahid et al., 2024; Shahid and Vashistha, 2023). On the other hand, since
fact-checking is reliant on human effort, it is difficult to scale given the
sheer amount of harmful content online (Juneja and Mitra, 2022; Micallef et
al., 2022).

Deliberation can serve as a complementary approach to address the shortcomings
of these existing infrastructures. For example, deliberation can help identify
potentially divisive content that needs fact-checking; only the content
unresolved through group deliberation can be sent to professional fact-
checkers. Deliberation could also effectively address and contest hyperlocal
misinformation and propaganda (e.g., news about the local community) that
fact-checking organizations may not know about (Seelam et al., 2024), but
group members may have the collective knowledge to debunk. Similarly,
deliberation amongst community members can serve as an effective tool to
inform opinions on multimodal content which is hard to debunk automatically
and unscalable to debunk by human fact-checkers (Juneja and Mitra, 2022;
Machado et al., 2019).

Scholars have also highlighted challenges in disseminating fact-checked
information (Micallef et al., 2022; Juneja and Mitra, 2022). A community-
driven deliberative approach can increase the reach of fact-checks, as users
exposed to fact-checks can share them with others during deliberation. This
will not only increase the reach but also boost effectiveness, as receiving
fact-checks from acquaintances has been proven more powerful (Pasquetto et
al., 2022). Another limitation of fact-checking is language accessibility
(Juneja and Mitra, 2022; Kazemi et al., 2022). Since fact-checking is often
manual and a centralized effort, it requires knowledge not only of the local
context but also of languages (Seelam et al., 2024), posing another challenge
to scalability. In contrast, a decentralized approach where group members
deliberate among themselves avoids this issue, as they are likely to share a
common language.

###  5.4. Implications

Building on our empirical findings and theoretical underpinnings of
deliberative theory, we now discuss the implications of our study on the
design and process of deliberative systems tailored to tackle harmful content
in WhatsApp groups. We omit discussion on the outcome, as deliberative theory
underscores the significance of deliberation regardless of its specific
outcomes.

####  5.4.1. Design of the Deliberative Environment

Our study underscores that a deliberation system must respect the private and
intimate nature of WhatsApp groups. Participants emphasized the importance of
anonymity in calling out harmful content. However, since instant messaging
platforms like WhatsApp do not allow anonymous texting, there is a tension
between what’s needed to combat such content and what’s possible on the
platform. In such a scenario, anonymity can be maintained through asynchronous
communication via an intermediary. A conversational agent can be this
intermediary, facilitating deliberation in a forum-like and anonymous manner,
even if instant messaging platforms may not provide these affordances by
design.

While such an agent could work theoretically, it is still an open question if
users will stop engaging with such an intermediary as the novelty wears off.
To ensure continued adoption, designers must consider the practical challenges
of deploying such an agent including the workload on group members and the
potential for bias, particularly in underrepresenting minority opinions. To
reduce the burden on group members, the agent must consider users’ interests,
expertise, and availability to discuss a topic.

In addition to ensuring continued adoption, designers of such systems must
also strive to inspire effective discussion. The choice of communication
medium can be crucial in this endeavor. Research has shown that people are
more productive when speaking rather than writing due to the lower cognitive
load (Gould, 1978). Even within the context of harmful content, prior work
found audio debunking messages more effective in correcting beliefs than text
(Pasquetto et al., 2022). In addition, research shows that speech is more
emotive and less hostile than text (Kraut et al., 1992). These studies provide
a strong argument for using audio as a modality deliberating upon harmful
content, which would be particularly useful in groups containing low-literate
individuals and older adults who often struggle with typing (Jain et al.,
2018; Hagiya et al., 2016). However, this choice becomes complicated in our
context due to the requirements of anonymity since individuals in small groups
can be easily identified by their voices. As also recommended by Davies and
Chandler (2012), future work could consider translating spoken words into text
to reduce the cognitive load of deliberation while preserving anonymity.

####  5.4.2. Deliberation Process

Our findings reveal the following suggestions for the deliberation process in
the context of harmful content on WhatsApp.

Human-AI Collaboration. Past work has shown that both facilitation and
structure improve deliberative quality and evenness of participation in online
communities (Lee et al., 2020; Kim et al., 2021). Our study extended these
findings to the context of anonymous deliberation on WhatsApp and showed that
a facilitator agent can encourage participants to research content and hear
others’ opinions. In addition, our study surfaced new avenues for facilitation
through human-AI collaboration. Instead of construing facilitation as mere
logistical support (e.g., structuring the conversation, time management,
encouraging participation), future deliberation systems could use AI to ease
the deliberation process for individual participants. For example, an agent
could help find reliable sources, intelligently summarize opinions, filter out
personal attacks, and write constructive feedback. However, these benefits of
AI must be weighed against issues of bias: which sources are “reliable”, which
opinions to leave out in the summary, what constitutes personal attacks, and
what makes feedback constructive? Not doing so carefully may create
accusations of ideological bent and cause distrust in the agent. However, the
answers to these questions are complicated as they may change with the
sociocultural context of the group. For example, the agent may need to filter
out sarcasm in a culture that considers it offensive but not in one that
considers it friendly. To circumvent these problems, future work could imagine
AI agents as personalized deliberative partners that adhere to each user’s
personal opinions and biases. We discuss this next.

Agents as Deliberative Partners. Findings from our study and prior work
(Davies and Chandler, 2012) show that deliberation might benefit each
individual more than the group as a whole. Given these individual benefits,
future efforts can focus on positioning a conversational agent as a partner in
deliberation rather than merely facilitating logistics. In our probe, group
members expressed their opinions to an agent, and group deliberation occurred
when they read each others’ opinions in the summary message. Instead of
deflecting back to the group, the agent could hear members’ opinions, pose
counter-questions, and encourage deeper thinking. While individual reflection
might not be useful to inform others’ opinions, it would help users reflect on
their own biases and worldviews. Self-reflection could be an effective way to
achieve the same benefits achieved from group deliberation without risking
group dynamics (e.g., in-fighting) and maintaining users’ anonymity.

Negative Effects of Anonymity. Kim et al. (2021) recommended designing
deliberative systems that respect interpersonal and social power dynamics. An
emerging body of work shows that the existence of social ties in WhatsApp
groups prevents deliberation of harmful content (Pasquetto et al., 2022;
Varanasi et al., 2022; Feng et al., 2022). Cultural norms also impact
participation (Min, 2009), for example, confronting elders is looked down upon
in certain cultures. Anonymity counters these effects and boosts both
participation in (Davies and Chandler, 2012) and quality of deliberation
(Baltes et al., 2002). Our study further provides empirical evidence that
anonymity can improve deliberation when social dynamics are at play. However,
anonymity may also increase “troublemaker” behavior (Tucey, 2010) such as
goofing off, hampering discussion, or making personal attacks, especially when
discussing divisive content. Hence, an anonymous deliberation system must also
introduce appropriate moderation strategies such as removing or rephrasing
opinions with expletives or involving group admins to ensure the civility of
the discussion.

###  5.5. Limitations and Future Work

We used a design probe to explore the utility of conversational agents to
facilitate the deliberation of harmful content in WhatsApp groups. There are
limitations to this approach. First, while our probe helped participants
conceptualize the design, we acknowledge that they did not have the
opportunity to actively engage with or experience the features. Consequently,
our findings are based on participants’ anticipated behaviors and responses
rather than their actual interactions with the system. Our decision to conduct
an exploratory design study was equal parts ethical and practical. We were
wary of deploying technology in private sociotechnical spaces in the absence
of evidence of its efficacy. Hence, our study aimed at exploring the design
space first. Having done so, a promising avenue for future research involves a
Wizard-of-Oz study within real WhatsApp groups. This would enable a
comprehensive evaluation of the agent using a variety of metrics devised to
measure the efficacy of the design, process, and outcome of deliberation
(Friess and Eilders, 2015; Beauchamp, 2019).

##  6\. Conclusion

We presented a qualitative study to explore the design of a conversational
agent to facilitate the deliberation on harmful content in WhatsApp groups.
Using a design probe, we asked WhatsApp users in India about the strengths and
limitations of using deliberation to combat harmful content, their interaction
with such an agent, and issues related to trust, privacy, and group dynamics.
Participants found this an effective approach to combat harmful content by
hearing diverse perspectives, though they perceived a potential risk to group
dynamics. They envisioned researching the content and recommended AI
capabilities to reduce their effort. We also revealed tensions in the design
of such an agent. We then turned to deliberative theory to examine the
efficacy of deliberation. We argued that deliberation encourages reasoned
dialog and critical thinking among participants, thus bringing value despite
surfacing individual opinions and not facts. We then compared deliberation to
other approaches such as moderation and fact-checking, discussing how
deliberation can help increase the efficacy of those existing infrastructures.
We concluded by distilling design recommendations, limitations, and future
work.

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