- Thank you everyone for coming to the talk,
and welcome to Seattle.
The weather in Glasgow is no different than Seattle,
from where some of us are.
So today I'm very excited to present our work.
It examines gender inequity in social media voice forums.
This is a joint work with Abhinav Garg, Richard Anderson,
and Agha Ali Raza, who is also in the audience.
So social computing platforms like these
have completely revolutionized how most of us in this room
produce, consume, and share information.
They play a pivotal role in connecting us
and providing us with up-to-date information.
And despite their imperfections, these platforms
are an integral part of our daily lives
and have deeply impacted our society.
But these platforms currently exclude billions of people
worldwide who are low-literate,
who speak low-resource languages,
who live in severe poverty,
or those who do not have smartphones and connectivity.
And to include these people
in the social computing revolution,
most technology companies have primarily focused
on bringing connectivity to low-resource environments.
For example, these companies are using drones, balloons,
and unused TV spectrum to improve connectivity.
And although these are great first steps,
just improving connectivity is not enough
because there are several other technical, social,
and cultural barriers that makes it hard to bring benefits
of social computing to people
in low-resource environments.
And just to give you a few examples, in India,
a high majority of people there use smartphones,
sorry, use feature phones.
Now, like smartphones, it's very difficult to create
a standardized feature phone application that work
on all feature phones because there are hundreds
of proprietary operating systems,
most without any API documentation or developer support.
Even when people have smartphones,
literacy and language barriers makes it difficult
for many to use them.
For example, 26% of Indian elders who are illiterate
find it challenging to use text-based interfaces.
Using voice-assistance softwares like Google Home,
Alexa, and Siri are also very challenging
because most Indian languages do not have recognition models
and training data.
And even if all these barriers are solved,
there are sociocultural norms that impact the systems,
all the social media platforms and social computing systems
that we've built.
For example, a high majority of women in India
do not have access to smartphones and internet,
and even when they own a simple phone, they make and receive
fewer calls and use the internet sparingly than men.
And these are just few barriers that I've highlighted.
A combination of these and several others
makes it very difficult to connect people
in low-resource environments.
But there is hope.
To bring benefits of social computing
in low-resource environments,
several ETCi, for development researchers and practitioners,
have used voiced-based or have designed and built
voice-based social computing services
by using interactive voice response technology.
So IVR technology is what you use when you call your bank
and press one to access your account information
or press two to speak to a customer service representative.
So these voice-based services let users call
a toll-free number, and record voice messages
in their local language, and listen to messages
that are recorded by others.
And I'll show you a quick example
of how basic service like this work.
(phone ringing)
- [Automated Phone Voice] (speaking in foreign language)
- Now the design of these services
overcomes several barriers that I've highlighted earlier.
For example, literacy barriers by using users'
speaking and listening skills, language barriers
by using local language and dialect of users,
socio-economic barriers by using toll-free lines,
so that even people who are living in poverty
can call these services, and connectivity barriers
by using ordinary phone calls from any types of phones.
And because of their inclusive, accessible,
and usable design, these services have been used
in diverse domains to provide social computing systems,
for example, in health, agriculture, civic engagement,
employment, entertainment, and many others.
And together these services have received millions of calls
and hundreds of thousands of voice messages
from marginalized people.
In fact, these services are often the only way by which
a majority of people connect with each other,
produce and consume information, and get a voice,
agency, and equity.
And all this is great, but the user analysis
of several of these services have revealed
extremely low participation of women.
For example, CGNet Swara, a service that enable rural people
to report and listen to news and grievances
has only 10% female contributors.
Sangeet Swara, a voice-based social media service in India,
had only 6% female contributors.
Ila Dhageyso, a voice-based Reddit-like service
in Somaliland, had only 15% female users.
And similar systems in Pakistan, like Polly and Baang,
also received very low participation from women.
So in this work, we examine why the participation of women
is almost non-existent on these
voice-based social computing services.
And particularly because these services are designed
to be inclusive, accessible, and usable to everyone.
And to do so, we focused our attention
on Sangeet Swara and Baang, two widely popular services
in Pakistan and India.
And I'll now describe these two services briefly
before I jump into what we did.
So Sangeet Swara was a voice-based socia media service
that enabled users to record, listen to, and vote on
songs, jokes, poems in local languages.
To use the service, users called a toll-free number
and once the call was connected, they could press one
on their phone keypad to access how many people
have liked or disliked their messages, press two to record
a new audio message, and press three to listen to
and vote on these messages.
So I would like to quickly show you a demo of what happens
when a user's phone call is connected and they press three.
And you can see the translations
at the bottom of the screen.
- [Automated Voice] (speaking in foreign language)
- [Male User] (speaking in foreign language)
(singing in foreign language)
- [Automated Voice] (speaking in foreign language)
- So get an idea that it's pretty much like a voice Facebook
version where people are recording posts, and other people
can like, dislike, share, or play it again.
Now Baang, a similar system in Pakistan, had all features
of Swara, but in addition to let users like, dislike,
or share messages, it also allowed them to record
audio comments and report inappropriate messages.
Swara was deployed in India for three months
and it received nearly 25,000 calls from 1,500 users
who recorded over 5,000 voice messages and cast
nearly 140,000 votes.
And the system grew by word of mouth
from 73 people to 1,500 people.
And Baang was deployed in Pakistan for eight months,
and it received almost in order of magnitude
more participation from users, again all by word of mouth.
And many users of these services were rural residents.
Almost all of them were male
living in low-income environments,
and a sizable number of them were blind.
So given that now we have a background
of the two voice-based social media services
which we are investigating,
to examine reasons for low participation of women
on these services, we used a mixed methods approach.
We conducted content analysis of over 10,000 messages
containing nearly 140 hours of audio data.
We asked 3/4 to categorize messages using a rubric
to determine the type of message, gender of the recorder,
whether the message targets a woman user,
or whether it was abusive or threatening in nature.
We then conducted analysis of usage logs of over 800 users
to examine what male and female users liked, disliked,
shared, and reported.
And we also conducted interviews and surveys with 50 users
to examine their user experience, perceptions,
as well as benefits and limitations they perceived
in using the service.
Our study was IRB-approved and we had explicit permission
from users to analyze their recordings
and usage of the system.
So our analysis indicated that only 5% of messages on Swara
and only 6% of messages on Baang
were recorded by female users.
We also found huge difference in the usage of these services
even among the most fervent users, male and female users.
For example, this graph shows how top 25 male users
recorded significantly more posts than top 25 female users.
And we found similar patterns for number of calls,
number of votes, number of times they shared content,
or number of times they reported a message.
Digging deeper, we found that women on these services
faced systemic marginalization and encountered flirts,
threats, abuses, and blackmail.
And just to give you a sense of what it looks like,
on Swara, 98% of flirty messages,
62% of threatening messages, and 46% of abusive messages
were targeted at women, who were only 6% of all users.
And these discriminations negatively affected
agency of women and further limited their participation.
So I'll go a little bit deeper into each of these factors
to give you a sense of what were flirts, threats, abuses,
and blackmail, but I'll encourage you to read our paper
for more details.
So many male users flooded these services with messages
expressing adulation, admiration, and love for female users.
Some of them were even forceful in sharing their feelings,
and they often recorded messages requesting female users
to give them a call back on their personal number.
For example, a man recorded the following message on Swara.
"Saroj, please call me and tell me how are you.
"You have to become my friend.
"Where are you from?
"Where do you live?
"I am in love with you.
"Call me on this number or give me your personal number."
It's worth noting that this person did not know Saroj,
and the message which he sent to Saroj is actually visible
to everyone because it was more or less
like a Reddit-like service.
Now some flirty messages also had a sexual undertone
and all female respondents reported excessive flirting
as distressful, and on the other hand, male users
perceived these flirty messages as nothing but fun
and disregarded that these messages created
an uninviting environment for women.
Often they fight that teasing and pursing women
is the way to impress her.
One of them stated, "Men see films and t.v. shows
"and think that the only way to gain attention of a woman
"is by teasing and pursuing her."
Many women, on the other hand, engaged in victim blaming
and held other women accountable for flirty behavior of men.
One of them stated, "Some women don't leave
"a good impression based on how they talk and what they say.
"We are also at fault.
"Not all fault is of men."
Now we found three main reasons why men were threatening
and abusing others.
Many male users often threatened female users
who did not respond to their flirty messages.
Female users found these threatening messages
extremely discomforting, they felt angry,
they felt irritated, and they couldn't do anything about it.
One of them stated, "A man posted offensive content
"when I did not respond to his advances.
"It is my wish if I want to talk to him.
"How can he force me?
"When I could not tolerate the misbehavior,
"I left the service."
Male users also threatened other men
who were trying to flirt with or praised the women
they expressed interest in.
And often these exchanges were abusive in nature
and created a really acrimonious environment
on the service for everyone.
A few users reprimanded those who were involved in
such exchanges to maintain the decorum of the service
and requested others to not record inappropriate messages
both for men as well as women.
But often these requests were ignored by those
who were creating nuisance, and in fact they threatened
even those men who tried to stand up against their behavior.
A man who was threatened stated, "I saw some men
"poorly treating a woman.
"I raised my voice but did not anticipate
"the extent of retaliation.
"They said horrible things."
The analysis of votes revealed systemic bias.
Most male users often up-voted messages when men
were threatening or abusing women, but down-voted messages
heavily when women recorded threatening or abusive message
as a response to messages from men
who were behaving inappropriately.
Now in addition to flirt, threats, abuses,
we also have found evidence of blackmail on the system.
As I mentioned earlier, several men shared their numbers
with everyone and requested women to call them
for a one-on-one conversation outside the portal.
Some women indeed called those numbers,
but when these conversations became uncomfortable
or unfriendly, they just stopped talking to the men
who they were talking to earlier.
But in two cases we found that a male user threatened
to release the number of a female user publicly
on the portal if she does not continue
one-on-one conversation with him.
And when she ignored the threat,
he posted her phone number on the service,
recorded messages suggesting that they were involved
in a romantic relationship, and said negative things
to assassinate her character.
One of the two women shared, "The man told me
"that if you stop talking to me,
"then I'll share your number with others.
"When I did not pick his phone call, he recorded a post
"saying that I am not a good woman
"and people should stay away from me."
Presence of such threatening, abusive, and flirty messages
tremendously damaged the perception of both inclusivity
and safety of women on these services.
And as a result, many male users prevented women
in their family and social circle
from using these services.
One of them stated, "I'll not allow women in my family
"to get exposed to these abusive messages."
In response to these messages, female users
stopped recording messages because they were afraid
that their participation will be known
to their male family members,
which would again have negative repercussions for them.
And from active contributors on this service,
they became passive listeners.
Also, these services grow by word of mouth
and spread virally, as I mentioned earlier,
but because of the uninviting atmosphere on the service,
women stopped sharing information about the service
with others, which significantly limited the number
of new female users joining the service.
One of them stated, "How can I ask my family and friends
"to listen to these posts where men are abusing women
"and other men?
"I would be in trouble if my family learns
"that Baang has such posts.
"Family members are accepting of these behaviors
"if a man does it, but not when it is done by women."
So there are interesting implications of this work.
Our work highlighted that there are
significant secondary barriers to women's digital inclusion
beyond the basic hurdles of literacy, connectivity,
and availability of devices.
Existing work on voice-based social computing services
highlight how accessible, inclusive, and usable
these services are.
And while this is largely true,
there is an alternate reality that these services
are not welcoming,
not inclusive for other user groups, like women.
While both Baang and Swara empowered low-literate people,
rural residents, and blind people, at the same time
they disenfranchised the rights, agency, and liberty
of women on these societies.
Our work highlighted how certain user groups
within marginalized communities are more marginalized
than the other.
And as designers and builders, we need to reflect
on how our tools and interventions widen the existing gaps
in this society.
Going forward, using techniques from information retrieval,
machine learning, and public policy, we plan to explore
multiple ways to make these voice-based social computing
services more inclusive, accessible, and usable for women.
And some of the ideas which we have is, for example,
can we design a gender recognition filter
so that once we know that a woman is calling the service,
can we give them more incentives?
Can we change, can we design certain
simple women-only forums?
Because that model exists both in Western
as well as in Eastern societies.
And can we design the services in a slightly different way
where prompts are recorded in female voice, or when prompts
explicitly encourage participation from women?
Would it help?
Or can we design better community moderation algorithms
where votes from women are equally represented
as votes from men even when they are not equal
in absolute number?
So to conclude, in this work we examined the reasons
for low participation of women on voice-based services
that are designed to be inclusive, accessible,
and usable for everyone.
Using a mixed methods approach,
we investigated participation of men and women
on two popular voice-based services in India and Pakistan.
And we found that women on these services
faced patriarchy-driven systemic marginalization,
and encountered flirts, threats,
abusive content, and blackmail.
And this discrimination's negatively affected
their participation on these services,
limited their participation, and reduced their agency
and gender to access any additional content.
Thank you.
- [Moderator] Thank you.
(crowd clapping)
We have time, sorry, my mic wasn't on,
thought I was gonna have to shout.
We have time for one question
while we switch to the next speaker.
Do we have a question?
Please, step over to use my mic.
- [Questioner] I wanted to ask if there is any
existing system for female-only voice-based communication?
You suggested it, but is there anything that is ongoing,
just woman-only groups or anything like that?
- So, not that we know of.
Having said that, it's also very difficult to design
such systems because, you can design the system
and promote it as, this is only for women,
but what stops men from calling the service?
So even if you have some sort of a gender-recognition filter
they can always modify their voice to sound like a female
and can get an unauthorized entry into the system.
So these are exactly some of the things
which we are thinking to build
and hopefully we'll be presenting it in a few years.
- [Moderator] Thank you, let's give Aditya
and his colleagues--