# Along the Margins: Marginalized Communities’ Ethical Concerns about Social
Platforms

1st Lauren Olson (she/they)  Software and Sustainability, Social AI
Vrije Universiteit Amsterdam
Amsterdam, the Netherlands
<EMAIL_ADDRESS>2nd Emitzá Guzmán (she/her)  Software and Sustainability
Vrije Universiteit Amsterdam
Amsterdam, the Netherlands
<EMAIL_ADDRESS>3rd Florian Kunneman (he/his)  Social AI
Vrije Universiteit Amsterdam
Amsterdam, the Netherlands
<EMAIL_ADDRESS>
###### Abstract

In this paper, we identified marginalized communities’ ethical concerns about
social platforms. We performed this identification because recent platform
malfeasance indicates that software teams prioritize shareholder concerns over
user concerns. Additionally, these platform shortcomings often have
devastating effects on marginalized populations. We first scraped 586
marginalized communities’ subreddits, aggregated a dataset of their social
platform mentions and manually annotated mentions of ethical concerns in these
data. We subsequently analyzed trends in the manually annotated data and
tested the extent to which ethical concerns can be automatically classified by
means of natural language processing (NLP). We found that marginalized
communities’ ethical concerns predominantly revolve around discrimination and
misrepresentation, and reveal deficiencies in current software development
practices. As such, researchers and developers could use our work to further
investigate these concerns and rectify current software flaws.

General Abstract— In this paper, we identified marginalized communities’
ethical concerns about social platforms. We did this because recent platform
wrongdoing indicates that software teams prioritize profit over user concerns.
Additionally, these platform shortcomings often have devastating effects on
marginalized populations. To accomplish this, we collected Reddit posts from
marginalized communities’ subreddits where users mention social media
platforms. Then, we labeled whether posts contained mentions of ethical
concerns, like privacy or misinformation. Finally, we established trends
within the resulting data and used artificial intelligence (AI) to find these
ethical concerns automatically. We discovered that marginalized communities’
ethical concerns revolve around discrimination and misrepresentation, among
other problems, and reveal deficiencies in current social platforms. As such,
researchers and software engineers could use our work to further investigate
these concerns and rectify present software flaws.

###### Index Terms:

marginalized, communities, ethics, software, reddit, feedback

##  I Introduction

As society transitions online, inequities in the physical world are encoded
into the digital world. Ethical concerns, such as censorship, misinformation,
and discrimination, are common in today’s software products. For example, just
in the past few years, software platforms such as Facebook and Instagram have
played a role in the censorship of Palestine’s protests of Israel’s forced
eviction [1] and the spread of misinformation and hate speech during Myanmar’s
genocide of the Rohingya people [2]. This problem of misinformation and hate
speech causing real-world violence is also relevant in other contexts. Acts of
violence in the US, including mass shootings, indicate that echo chambers and
hate speech online contribute to the systematic dehumanization of women, queer
people, migrants, and racial minorities, with perpetrators radicalized on
Discord[3], Reddit[4], Twitter[5], Instagram[6], and Facebook[5], among other
platforms.

Billions of these marginalized users are at the mercy of development decisions
made by a small demographic of the world population. Most software developers
who curate these platforms are white, middle to upper class, cisgender,
heterosexual, English-speaking men from the United States [7]. Studies show
that developers’ political affiliations affect their design decisions [7].
However, this problem cannot be entirely solved by hiring software developers
from diverse backgrounds. Even when teams are diverse, development decisions
tend to reflect shareholder concerns, rather than user concerns [7].

To recenter the focus of today’s software development from privileged to
marginalized communities, we aggregate marginalized users’ feedback on
software. In doing so, we can capture their perspectives on current ethical
issues. Although we cannot restructure economic systems of power, we can at
least make this information more accessible to developers. While previous
attempts to collect user feedback have a population bias of mostly middle-aged
men [8], we focus on unheard voices. We gather users’ ethical concerns related
to software platforms from Reddit to determine the concerns and desires of
systematically marginalized users, which are not represented during software
development. We chose Reddit as our data source because of its high character-
limit, audience-specification, and anonymity features. Specifically, we
aggregated a large dataset of their perspectives on software, manually
analyzed a sample of their ethical concerns, and developed models to process
and understand their concerns on a large scale to make these viewpoints
available to developers and researchers.

This work contributes to (1) the field of user feedback for software evolution
by disaggregating feedback from privileged idenities and (2) design justice
work by considering a data-driven process to supplement user studies. Although
previous research has considered differences in user feedback concerning
gender[9], language[10], and culture[11], this paper is the first to look at
differences in feedback concerning ability, sexuality, gender identity, sex,
race, and socio-economic status (SES). Furthermore, our work is the first to
include intersectional groups, groups with intersecting marginalized
identities, whose concerns cannot be accurately represented by considering
identity on a one-dimensional scale. In addition, design justice work
typically features small-scale user-centered studies, which are critical for
accurately representing users’ concerns. However, by collating marginalized
group feedback on platforms and identifying common trends, our research could
allow other researchers greater insight into their users’ concerns at the
start of their studies’ designs to allow for deeper and more targeted lines of
questioning.

##  II Research Design

The main goal of this study is to collect and analyze rich user feedback from
marginalized communities detailing their ethical concerns about software.
Furthermore, we aim to obtain feedback containing not just ethical concerns
but context and detail on their experiences to allow developers and
researchers to understand marginalized communities’ feedback and act on it.

###  II-A Data Source

We use Reddit as our data source because of its long-form posts, subreddit
structure, and throwaway accounts. A main challenge in eliciting user feedback
is the lack of actionable content [12]. Previous studies have focused on
social media sites like Twitter, which restricts character length to 280
characters, as well as the Apple App Store and Android Google Play Store,
which cap reviews at 6,000 and 4,000 characters, respectively. Reddit, in
comparison, limits posts to 40,000 characters. This encouragement of lengthy
discussion by design makes Reddit a potentially rich source for user feedback,
making posts more likely to include context surrounding their experiences with
the platforms.

Beyond Reddit’s lengthy character count, another unique feature, compared to
other social media platforms, is the structuring of its content. Instead of
subscribing to content from specific accounts, which typically represent one
user or entity, users subscribe to subreddits, which represent groups of
users. The topic of the subreddit unites these groups; for example, some
subreddits include “r/funny,” “r/movies,” and “r/amsterdam.” This structure
allows users to filter their feeds to only topics and populations of interest.
In addition, these subreddits are communities with content moderators and
rules of behavior. This user-led moderation gives users greater control over
the users they interact with and the content they view. This structuring thus
allows the explicit allocation of platform space for groups who may get
overlooked through other social media platforms’ use of popularity measures to
filter and promote content. For marginalized communities who often face hate
in less regulated online spaces, these subreddits can provide a safe(r) space
to find others with similar situations [13]. For example, women in
r/rapecounseling can find an otherwise non-existent outlet for being heard and
believed [14]. Because of this audience-specifying feature, we expect
marginalized communities to be more willing to share ethical concerns relating
to their marginalized identities.

Additionally, Reddit allows users to anonymize their posts through the use of
throwaway accounts, further ensuring that those with sensitive information can
feel safe posting. For example, Leavitt et al. found “that women are much more
likely to adopt temporary identities than men” [15]. Leavitt et al. posit that
this adoption of throwaway accounts is motivated by users’ fear that people in
their real lives might uncover their identities.

###  II-B Research Questions

This study focuses on three main research questions:
(RQ1) Is Reddit a fertile source of feedback on ethical concerns about
software from marginalized communities?

(RQ2) What types of ethical concerns regarding software do marginalized
communities have on Reddit?

(RQ3) What is the automation potential of extracting and classifying ethical
concerns in Reddit posts from marginalized communities?

##  III Related Work

###  III-A Marginalized Communities

Previous studies designed to capture marginalized communities’ software
preferences have conducted user-centered studies with small groups of
marginalized communities. For example, a less recent study by Koepler et al.
intended to collect the perspectives of homeless people on social media by
surveying 199 people on their preferences [16]. They surveyed these users to
determine which platform features could aid homeless users in sharing
resources and forming communities online.

More recently, user-centered design has sought to give the user a more active
role in the development process. For example, De Vito et al. performed an
interactive study to discover 31 queer users’ design values to combat
stigmatization and harm online. Also, Rankin et al. discovered 10 black
youths’ different persona and utility conceptualizations of a Siri-like agent
during 7 interactive design sessions [17]. These two more recent studies are
samples of critical work in disaggregating development to consider how
marginalized users’ concerns may differ from shareholders’. These studies are
crucial and could be supplemented by our collected data, content analysis, and
NLP models as our data could aid in forming user-centered studies’ design
activities and questionnaires to deepen and facilitate these studies. Our
study, in contrast, aggregates the perspectives of thousands of users and 35
marginalized communities. In addition, ours is the first to analyze
marginalized groups’ ethical concerns from social media.

###  III-B User Feedback

Previous work found that user feedback is essential for software quality and
identifying areas of improvement [18].

TABLE I: Subreddits Chosen for Manual Annotation Physical Disability | Neurodivergent | LGBTQIA+ | Race | Global South | Women/AFAB | Lower SES
---|---|---|---|---|---|---
prostatitis | narcissism | actuallylesbian | asianmasculinity | MalaysiaPolitics | whereAreTheFeminists | almosthomeless
disabledgamers | adhd | asexualdating | southasianmasculinity | asklatinamerica | femalefashionadvice | homeless
sciatica | mentalhealthsupport | meetlgbt | indianSkincareAddicts | askthecaribbean | askfeminists | homelesssurvival
disabled | malementalhealth | transadoption | aznidentity | beautytalkph | girlsgonewired | vagabond
spinalcordinjuries | mentalhealthPH | honesttransgender | BlackGirlDiaries | MalaysianPF | TwoXIndia | vandwellers

With the rise of mobile applications and social media, research proposed to
elicit feedback from crowds of geographically distributed users [19] and
called for the mass participation of software users during different stages of
software development[20]. Pagano and Maalej [21], and Hoon [22] were among the
first to study user feedback in app stores. They performed exploratory studies
and found that this platform contains valuable information for software
evolution. In a similar line, other work [23, 24, 25, 26] found that user
feedback on Twitter also contains valuable information for software evolution.

Recent work [27] studied posts about specific software applications on Reddit
and found that this platform is also a good source of user feedback when
evolving software. Previous work also found that there are cultural
differences in how feedback is given [11, 28] that more men give feedback
about software [8, 9] and that the majority of these are in the 35-44 age
range [8].

There are few studies which focus on ethical concerns in user feedback. Tushev
et al. [29], Besmer et al.[30], Li et al.[31], and Khalid et al.[32], all
consider either discrimination [29] or privacy[30],[31],[32] in user feedback
on software. In addition, Shams et al. [33] and Obie et al. [34] analysed
human values violations in app reviews with the Schwartz theory of basic
values [35], which includes 11 values. Although, this theory and its values
were originally developed for the psychology field so its values don’t align
closely with software. However, later work created an ethical concerns
taxonomy specifically developed for user feedback on software platforms [36].
We use this taxonomy for part of manual analysis (see Section IV-E2).

To our knowledge, no research has studied how marginalized communities give
feedback about software and which ethical concerns these groups have about the
software they use. We address this gap in our work.

##  IV Research Method

Our research method aims to collect marginalized communities’ Reddit posts,
identify and classify ethical concerns regarding software platforms within
these posts, and set up our posts for automated identification and
classification of ethical concerns. First, we select seven marginalized
communities to focus on, search Reddit for their subreddits, and scrape these
subreddits, resulting in a dataset of 459,523 posts. Then, we select the
software platforms to focus on and search for these platforms’ names within
the scraped Reddit posts. This filtering produces a dataset of 23,533 posts
that mention platforms. Next, we manually annotate a sample of 2,201 posts to
identify ethical concerns within these posts. After identifying all ethical
concerns within this sample, we classify each post’s ethical concern by type.
Next, to prepare for the process of automatically identifying and classifying
ethical concerns, we trained a binary and multi-class classifier with our
previously annotated data. In the following, we describe each of these steps
in greater detail.

###  IV-A Marginalized Community Selection

Unless developers are told otherwise, they assume users are “white, male,
abled, English-speaking, middle-class US citizens” [7]. Even if these biases
are unintentional, or corrected by diverse team members, because purchasing
power is held by these same privileged groups, platforms structure their
development around these groups’ priorities. To counter this bias, the seven
demographic groups we consider are (1) race (2) women/AFAB, (3) LGBTQIA+, (4)
physically disabled, (5) neurodivergent, (6) lower socio-economic status
(SES), and (7) Global South.

To create a comprehensive list of subreddits that cover a broad range of
subgroups within the chosen marginalized communities, we used Reddit’s
directory111https://www.reddit.com/subreddits/a-1. First, we scanned the list
of subreddits alphabetically, searching for those related to our seven groups.
Then, we checked the description for the subreddit to ensure that the
subreddit was accessible and related to the perceived category. For example,
the “r/chad” and “r/haiti” subreddits were private, and the “r/fiji” subreddit
is a subreddit dedicated to a fraternity, not the country. Finally, we
included subgroups for the more extensive categories as a general criterion.
For example, we included both the subreddit for Tunisia, “r/tunisia,” as well
as “r/tunisianjobs,” a subreddit for Tunisians looking for jobs; however, we
did not include “meme” or other image-related subreddits as the majority of
posts did not contain text-based content.

Next, we describe more in detail how we found the subreddits for each of the
marginalized communities considered in this study.

####  IV-A1 Race

For the race category, any mentions of race within the title of the subreddit
warranted inclusion in this category. For ease of identification and due to
Reddit’s majority US user base, we used the US’ official racial categories,
“White, Black or African American, American Indian or Alaska Native, Asian,
and Native Hawaiian or Other Pacific Islander,” as well as any sub-categories.

####  IV-A2 Women/AFAB

This category intends to cover the perspectives of those who receive
discriminatory treatment for either (1) being perceived as a woman or (2)
owning a female body. As such, we included feminism, women, and assigned
female at birth (AFAB) related groups. For example, we included groups with
“TwoX” in the title, which is a reference to an AFAB’s genetic composition of
two X chromosomes, subreddits related to pregnancy and breastfeeding, or
“feminism,” “wom[e/a]n,” “female” in the title. It was necessary to check
these subreddits as many groups that may seem for women are groups that post
and rate photos of women (e.g., “r/latinas”).

####  IV-A3 LGBTQIA+

In this paper, we use queer as an umbrella term for the LGBTQIA+ community. To
generate a list of LGBTQIA+-related subreddits, we searched for subreddits
relating to each letter of the acronym, adding relevant recommended
communities as well, like “r/agender,” “r/abrosexual,” and
“r/Crossdressing_support.”

####  IV-A4 Physical disability

The World Health Organization defines disability as “the interaction between
individuals with a health condition…and personal and environmental factors;”
[37] as we cannot account for personal and environmental factors, we include
all health-related subreddits.

####  IV-A5 Neurodivergence

The requirements applied to the physical disability category are the same as
this category. All subreddits relating to neurodivergent conditions are
included.

####  IV-A6 Lower SES

For the lower SES category, we included subreddits related to homelessness or
near homelessness.

####  IV-A7 Global South

To develop the group of Global South-related subreddits, we referenced a list
of Global South countries222https:worldpopulationreview.comcountry-
rankingsglobal-south-countries to determine whether a subreddit was a mention
of a Global South country.

This marginalized community selection process resulted in a comprehensive list
of 586 subreddits. The community with the largest number of subreddits is the
Global South, with 216 subreddits, and the smallest community by far is lower
SES, with only seven found subreddits.

###  IV-B Data Scraping

We scraped the posts of our gathered subreddits with an existing
scraper333https://github.com/JosephLai241/URS. We scraped the Reddit posts on
November 19, 2021. For each subreddit, we scraped until we collected either
the entire subreddit’s posts or the most recent 1,000 posts. The average
collection period per subreddit is 5.8 years, making the average earliest post
from December 2015. The complete list of subreddits is available in the
replication package444https://doi.org/10.5281/zenodo.7194259. This data
scraping resulted in a dataset with 459,523 posts. Again, the Global South was
the largest category, with 158,590 collected posts, and the lower SES category
was the smallest, with only 4,517.

###  IV-C Platform Selection

In this study, we focus on posts mentioning specific social software
platforms. We chose to focus on social software platforms because these
platforms tend to have frequent claims of platform malfeasance, as referenced
in the Introduction [1],[2], [3], [4],[5],[6]. We selected social platforms
with at least 100 million users555https
//en.wikipedia.org/wiki/List_of_social_platforms_with_at_least
_100_million_active_users#cite_note 11. We decided on this threshold to make
it more likely to find enough posts per platform since our collected
subreddits are not specifically dedicated to software platforms.

After searching our dataset for mentions of these platforms, we found 23,533
posts. We removed all platforms with less than 100 posts in the dataset. We
also removed platforms with names “imo” and “Line,” as their mentions were
likely to be references to the anagram “in my opinion” or the word “line.” In
total, we included 12 popular social platforms in our study. We show them and
their number of posts in Figure 1. Reddit had the greatest number of posts due
to self-references at 10,743.

Figure 1: Number of Posts by Platform

###  IV-D Sampling

Because we could not manually label over 20,000 posts, we needed to create a
sample representing the dataset as accurately as possible. To create this
sample, we scored each subreddit based on its number of platform mentions,
with the intuition of capturing the most salient phenomena. We included the
five highest-scoring subreddits in each category within the sample used during
the annotation since we wanted to pay equal attention to each of the
marginalized communities. We show the used subreddits in Table I
(intersectional groups are italicized). Next, we calculated the sample size
for a 95% confidence level, collecting the posts randomly from an aggregated
pool of the chosen subreddits for each category. The resultant sampling
accumulated 2,201 posts for annotation.

###  IV-E Manual Annotation Setup

####  IV-E1 Ethical Concern Identification

The annotation task was to identify whether ethical concerns were present in
posts mentioning software platforms. To ensure that the task and definitions
were clear, we initially performed six trial rounds of coding. Every round had
two posts from each of our marginalized communities, adding up to 14 posts per
round. Each round had only 14 posts due to our dataset’s average post length
of 962.27 characters, or around 150-200 words for each post. All rounds were
completed with the first author and one other author, and the final four were
performed with all three annotators. For the first round of annotation,
annotators achieved a Krippendorf’s alpha of .381. This initial score reflects
this task’s difficulty, mainly because most Reddit posts’ intent was not to
give direct feedback on ethical concerns about software. To correctly label
ethical concerns, the annotators used an extensive annotation
guide666https://doi.org/10.5281/zenodo.7194259 with definitions and
examples777From actual Reddit posts. detailing how to interpret and classify
the posts. Our utmost concern in the process was avoiding the dismissal of
more implicit ethical concerns.

Figure 2: Manual Annotation Complexity

Post modified to protect author’s privacy.

As the rounds proceeded, we updated this annotation guide to clarify disputes
between annotators. To simplify the labeling process, annotators decided to
assume the users’ correctness, platforms’ responsibility and interpret ethical
concerns as any “worry or care the user or their group faces about what is
right or good, that is not simply a bug report or feature request” on the
platform. For example, in the
post888https://www.reddit.com/r/PregnancyAfterLoss/comments/iux55d/
i_hate_this/ shown in Figure 2, two points of ambiguity were raised by
annotators. First, in our posts, often users raise an issue faced on a
platform, but do not explicitly blame the platform for the issue. So, we
decided that, regardless of user perception of the platform’s role in the
issue, we would classify these implicit ethical concerns as ethical concerns.
Second, it is unclear whether the platform would be able to solve the user’s
concern. Again, we made the assumption that platforms were ultimately
responsible for their users’ experiences. As such, we categorized the example
shown in Figure 2 as an ethical concern. After the three annotators achieved a
Krippendorf’s alpha of $1.0$ in the final round, the first author annotated
the rest of the 2,201 posts on their own. After completing the initial
identification of ethical concerns, we had a labeled dataset with 580 ethical
concerns.

####  IV-E2 Ethical Concern Classification

To enable a more detailed analysis of these ethical concerns, the first author
did a second round of labeling to categorize each of the ethical concerns into
more specific labels like “privacy,” “misinformation,” etc. Before this step,
the first and second author performed two trial runs with the labels used in
this detailed analysis. In their final run, they scored a Cohen’s kappa of
.77. For this step, we used an existing ethical concerns taxonomy for software
applications [36]. Table II shows the categories of this taxonomy; the term
definitions are available in our replication
package999https://doi.org/10.5281/zenodo.7194259. This taxonomy was
supplemented with an “other” category for ethical concerns that did not fit
into any of the existing categories. This category introduces the capability
to capture ethical concerns specific to marginalized communities and newly
emerging ethical concerns. We also added a “social isolation” category which
was removed from the original taxonomy [36] due to lack of relevance, but was
present within the current data, likely due to the different outlets for
posting and our focus on marginalized communities.

TABLE II: Ethical Concerns’ Taxonomy

Ethical Concerns
---
Accessibility | Harmful Advertising | Discrimination
Addictive Design | Identity Theft | Scam
Censorship | Inappropriate Content | Social Isolation*
Content Theft | Privacy | Misinformation
Cyberbullying | Safety | Other*

*Added categories

###  IV-F Automated Extraction and Classification Setup

The steps described in this section prepare our data to act as input for NLP
classifiers, so they can detect and classify ethical concerns in Reddit posts,
allowing software developers to more easily find ethical concerns and
categorize them within Reddit posts. We cleaned the data as an initial step to
create performative NLP models. First, we preprocessed the data to remove
noise and allow for the comparison of similar words. Then, as part of the
training phase, we balanced the data to ensure that our models were not biased
towards one label due to their frequency, allowing them to classify based on
salient features of the textual data.

####  IV-F1 Preprocessing

We first preprocessed the dataset using the NLTK101010https://www.nltk.org/
library. We eliminated special characters and symbols, performed lowercasing
and tokenization, eliminated stopwords using NLTK’s English stopwords
set111111https://gist.github.com/sebleier/554280, and stemmed the resulting
words.

Next, we vectorized the words using the TfidfVectorizer with an n-gram range
of 2. Finally, we also decreased max_df to .5, to reduce reliance on Reddit-
specific terminology, and sublinear_tf to True, which weakens term frequency.

In addition, due to the length of Reddit posts, we implemented a window
algorithm, which only included the sentence mentioning the platform itself,
along with one sentence before and one sentence after this sentence, thereby
removing noise from the posts.

####  IV-F2 Handling Data Imbalance

For the binary classification task, there were less ethical concern platform
mentions than non-ethical concern platform mentions, signalling a slight data
imbalance. In total, 26.49% of the labelled reviews mentioned an ethical
concern. Again, for the multi-class classification task, our distribution
ethical concerns were unbalanced as well, with discrimination (25.88%),
censorship (24.39%), other (10.06%), cyberbullying (9.50%), and addictive
design (7.64%) all at varying percentages. To fix this, we implemented
stratified 10-fold cross-validation and Synthetic Minority Oversampling
Technique (SMOTE). Cross validation is a resampling method which trains and
tests the models on discrete sections of the data, to decrease the models’
dependence on any one section of the dataset. SMOTE oversamples minority
classes by fabricating data points [38]. We only applied SMOTE on our training
data.

##  V Results

This section addresses the frequency of ethical concerns within our
marginalized communities’ subreddits. We disclose general measures and the
frequencies of ethical concerns within our marginalized communities and
platforms. Next, we perform a content analysis on each type of ethical
concern. Lastly, we report the automation potential of identification and
classification of ethical concerns.

###  V-A Frequency of Ethical Concerns Feedback

The selected software platforms were mentioned in 5.1% of the posts, 26.49% of
these posts expressed ethical concerns about software. These posts were
relatively long, with an average length of 911.8 characters, or 140-182 words.
However, although a post mentions a platform, the main subject of the post
might not be the platform. This leads to swaths of irrelevant data within the
posts.

####  V-A1 Marginalized Communities

The three communities with higher-than-average reporting of ethical concerns
are women/AFAB, race, and neurodivergent. The marginalized community with the
greatest percentage of ethical concerns within their platform mentions is
women/AFAB, with 55.05%, as seen in Figure 3. The majority of these complaints
revolve around censorship on Reddit, as discussed in Section V-B7. The next
highest-reporting ethical concerns community was the race community, whose
most frequent grievance was by far discrimination. They are followed by the
neurodivergent community, for whom addictive design was seen most frequently
as an ethical concern.

The four communities who report ethical concerns at a less-than-average rate
are the queer, lower SES, Global South, and physical disability communities.
The queer community’s top ethical concern was also discrimination, with 14
reports of transphobia and homophobia, including a report of color under-
representation in trans spaces. Next, the lower SES community reports
cyberbullying and censorship as the most frequent concerns, with home-insecure
users being harassed online and struggling in their dependence on platforms
and random people online to provide them with resources needed for survival.
The Global South’s top ethical concerns were the other and scam categories,
detailed further in Sections V-B1 and V-B6. The community with the least
amount of ethical concerns proportionally, with 9.71%, is the physical
disability community, which was often positive about the connection that
online spaces brought them, yet still had many claims of cyberbullying and
medical misinformation.

Figure 3: Ethical Concern Frequency by Marginalized Group

####  V-A2 Platforms

Figure 4: Ethical Concern Frequency by Platform

Instagram, Facebook, and Discord all reported ethical concerns at a higher
than average rate, while Reddit, Twitter, and YouTube are related to ethical
concerns at a less than average rate, as seen in Figure 4. To perform this
analysis, we first removed platforms with less than a hundred total posts in
our manually annotated dataset. Additionally, we removed concerns identified
less than ten times from consideration. Finally, we normalize the frequency of
ethical concerns by overall ethical concern type occurrence and the number of
total ethical concerns on the platform in question.

Instagram’s highly occurring concerns are discrimination (28.30%),
inappropriate content (15.09%), and scam (15.09%), described further in
Sections V-B10, V-B3, V-B1, respectively. For Facebook, the two most highly
occurring ethical concerns are misinformation (10.53%) and scam (15.79%). The
misinformation concerns relate to self-diagnoses for mental health concerns
and stereotyping of lower SES and Asian people. Lastly, Discord users report
social isolation (38.10%) and cyberbullying (28.57%) at the highest rate,
signaling Discord’s role in fostering relationships between users.

Reddit’s two highest concerns are censorship (42.32%) and discrimination
(22.87%). Reddit’s greatest ethical concerns have affected the frequency of
ethical concerns we report in this paper as we performed it on Reddit and
therefore elicited the most feedback on Reddit. Next, Twitter’s highest
concerns are cyberbullying (17.50%) and inappropriate content (12.50%).
Twitter’s cyberbullying concern describes constant toxic negativity on the
platform and racism and lesbophobia. Again, their inappropriate content
concern recounts negative content causing harm to users’ mental health.
Finally, YouTube’s most frequent concerns were addictive design (24.39%) and
misinformation (12.2%), described further in Sections V-B5, and V-B2,
respectively.

###  V-B Type of Ethical Concerns Feedback

In this section, we discuss each ethical concern type in detail. However, as
referenced in the previous section, our frequency of types in Figure 5 are
biased by the imbalance of platform posts, with Reddit (293) having over three
times the number of posts in our ethical concerns as the next largest
platform, YouTube (82), and nearly 100 times the amount of posts as our
smallest platform, Snapchat (3). Because of this, we do not consider Figure 5
as representative of the actual distribution of concerns voiced by
marginalized communities on social platforms. Instead, we examine each type of
concern individually and report which marginalized communities they most
frequently originate from, to treat these communities polylithically.

Figure 5: Ethical Concern Frequency by Type

####  V-B1 Scam

For the scam category, 78%, or 28 of the 36 claims, were from the Global South
or race category. Most of these claims detail influencers on YouTube and
Instagram disingenuously endorsing and advertising cosmetic products to earn
financial compensation. All of these claims originated from the
“r/indianskincareaddict,” “r/beautytalkph,” and “r/malaysianpf” subreddits.
Due to the pressures of colorism faced by South Asian women/AFAB, it is no
wonder that they face additional targeting from advertisers through
influencers to buy skincare products [39],[40].

####  V-B2 Misinformation

Out of the 31 ethical concerns about misinformation, 20, or 64.5%, relate to
medical misinformation. Most of this medical misinformation originates from
physical disability and neurodivergent-related subreddits. Half of these
medical misinformation claims relate to material on YouTube.

Based on the descriptions of users, there is a negative feedback loop where
users need medical information and fall into an addictive pursuit of
information relating to their condition in pursuit of a diagnosis. This
medical information is often provided by non-medical professionals, diluting
the reliability and quality of diagnosis and treatment.

In addition to medical misinformation, users also describe misinformation for
newly homeless people. For example, according to a post, telling newly
homeless users to “[c]all the cops” or apply for Section 8 can be dangerous as
police often act violently towards homeless users, and the waiting list for
Section 8 can be over 15 years long.

####  V-B3 Inappropriate Content

Within the inappropriate content concern, 50% of the claims originate from the
neurodivergent communities, with the majority of these posts describing
anxiety or depression induced from online content. Oftentimes, unrealistic
standards, popularized and elevated by “the algorithm” to increase clicks,
engender this negative mental reaction. The next largest group of users
reporting inappropriate content is women/AFAB. Some women/AFAB feel inundated
by wedding and fashion content, either feeling unable to live up to the
standard, or trapped by their family’s expectations for them. Otherwise,
women/AFAB report misogyny online making them feel subhuman.

####  V-B4 Cyberbullying

We address cyberbullying as a more user-driven form of inappropriate content;
however, both are forms of content which harms users.

First, it is critical to address the fetishization faced by women/AFAB of
color and trans people online. Perpetrators post nonconsensual pornography to
fetishize bodies and their connected identities. In our dataset, exclusively
from the “r/twoxindia” subreddit, a subreddit for Indian women/AFAB, there are
six reports of nonconsensual pornography, the majority of this pornography
hosted on Reddit.

A difficulty in forming relationships online for trans users is their exposure
to “chasers,” people who fetishize trans bodies. According to some users, it
can be difficult to determine whether connections with other users online are
genuine or potential harassment.

####  V-B5 Addictive design

For the addictive design category, over 80% of the claims originate from the
neurodivergent community, suggesting that those with mental health struggles
may be more vulnerable to online addiction. Of these, half of the complaints
derive from the ADHD subreddit. Additionally, nearly 50% of these claims
concern YouTube. As many mental health disorders often co-occur with addictive
behaviors, we can conclude it is likely that addictive design targets the
mentally ill. Many of these claims describe platform use as a means to escape
from and avoid their real-world problems.

####  V-B6 Other Ethical Concerns

A common concern of users from the Global South subreddits is the lack of
required information that is available online. For example, when looking for
information on politics and finance, users in the Global South often can only
find information relating to the US rather than their own home countries. This
lack of information is part of a broader trend of misrepresentation online
expressed by all groups, whether it simply concerns false representation or
the over or under-representation of certain groups.

According to users, there are both instances of under and overrepresentation
online, with women/AFAB reporting difficulty finding other women/AFAB users on
Reddit and some users from our race group describing overrepresentation of
popular members of their racial group on Twitter.

Another trend in misrepresentation is the assumption of the privileged group
as default. On LinkedIn, some users describe receiving messages which start
with “Hello Mr. ____,” despite not being a man. On Pinterest, multiple users
complain that fashion-related images only feature white women, despite
explicit searches for non-white women.

####  V-B7 Censorship

For the censorship category, the most significant concern is voiced in
response to the moderation of “r/Feminism” by a male moderator, whom we will
refer to as userX. Most users within this concern claim userX removed their
posts and banned them after posting about common feminist issues.

Of the 135 censorship complaints, 106, 79%, are about userX. These claims
started in 2012 and dropped off after 2015. However, userX, a man, remains the
head moderator of the “r/Feminism” subreddit. According to the
“r/WhereAreTheFeminists” subreddit, another feminist was banned from
“r/Feminism” as recently as June 2022.

These posts raise the question of whether censorship is inevitable when a
centralized group controls a marginalized community’s discourse. A similar
claim was made by “r/askacaribbean” regarding a Caribbean Facebook group which
has two representatives who are not from the Caribbean region. The poster
claims, therefore, that “they don’t understand the local culture and
politics.”

####  V-B8 Social Isolation

Nearly 47% of social isolation concerns emanate from the neurodivergent group,
with the following largest proportion, 27%, arising from the queer community.
The neurodivergent group reports exclusion and negativity online, two
principles integrated into the structure of today’s internet, with non-
physical connections making it easier to neglect relationships and clickbait
overpowering quality.

According to the queer communities’ posts, it can be unsafe to connect with
other queer users in real life due to the abuse and harassment many queer
people face. As a result, many can connect online to users facing similar
struggles. However, these users still report harmful inundation and
inauthenticity within online relationships.

####  V-B9 Privacy

The privacy concerns originate mainly from the neurodivergent and queer
communities. The mental health-related communities report online stalking from
ex-partners and abusers and feel uncomfortable forming online relationships
with people they know in real life. In the queer community, posts discuss
whether and to what degree they should reveal their queer status. Giving more
significant control over users’ privacy from their own online and offline
connections seems to be a safety issue, especially for women/AFAB,
neurodivergent, and queer users facing potential stalking and abuse.

####  V-B10 Discrimination

Within the discrimination concern, 57% of claims originate from the race
group. Of these claims, 33% focus on inter and intra-group conflicts,
specifically between races and genders within a race. Racism from white users
is more pervasive, with 53/79, or 67%, of reports of racism towards Asian or
black users. The reports of discrimination from “r/asianmasculinity,”
“r/aznidentity,” and “r/southasianmasculinty” often mention online communities
and platforms not disavowing racism and thus invalidating racism against Asian
users. As discussed in Section V-B3, content online for women/AFAB of color,
like those in “r/blackgirldiaries,” targets, dehumanizes, and reminds them of
past trauma. This systematic exposure to degrading content leads to an
inequitable experience online. Another trend among queer users, people of
color, and those from the Global South is the invalidation of their
identities.

###  V-C Automated Extraction Potential

Our binary classifier identifies ethical concerns within platform-mentioning
posts, while our multi-class classifier discerns the type of the ethical
concern. The intention of the automatic extraction of ethical concerns was to
determine whether it is possible for software practitioners to aggregate and
process this data on a large scale, to allow them to utilize these complaints
within their software development process. Our top-performing binary and
multi-class classifiers performs with f1-scores of .805 and .713,
respectively.

####  V-C1 Classifiers

The Naive Bayes and SVM classifiers were chosen based on the recommendations
from [41] for predicting categories with labeled text data under 100k rows .
The more specific instantiations of these models were chosen based on
empirical validation.

TABLE III: Binary Classification Results for Ethical Concern Presence Model | Precision | Recall | F1-score
---|---|---|---
Logistic Regression | 0.773 | 0.611 | 0.681
Support Vector Machine | 0.829 | 0.568 | 0.673
LinearSVC | 0.715 | 0.645 | 0.674
NuSVC | 0.812 | 0.800 | 0.805
Gaussian Naive Bayes | 0.731 | 0.889 | 0.802

####  V-C2 Binary Classification for Ethical Concerns

These classifiers identify whether a platform-mentioning post contains an
ethical concern or not. Table III shows the main results for each classifier.
For this task, the Support Vector Machine had the highest precision score at
0.829. The Gaussian Naive Bayes classifier had the highest recall score at
0.889. Finally, the NuSVC classifier had the highest f1-score at 0.805.

####  V-C3 Multi-Class Classification for Ethical Concerns

These classifiers identify the post’s ethical concern type. For this task, we
removed ethical concerns with less than 40 posts from the dataset used for
prediction, only leaving the top 5 most frequent ethical concerns in our
dataset. These concerns are discrimination, censorship, other, cyberbullying,
and addictive design. Table III shows the main results for each classifier. In
all measures, the Gaussian Naive Bayes classifier was the most highly
performing for this task, with precision=0.743, recall=0.725, and
f1-score=0.713. When considering each individual ethical concern’s ability to
be categorized, censorship, across all models has the best performance.

TABLE IV: Multi-Class Classification Results for Ethical Concern Presence

Model | Precision | Recall | F1-score
---|---|---|---
Logistic Regression | 0.739 | 0.647 | 0.666
Support Vector Machine | 0.694 | 0.591 | 0.597
LinearSVC | 0.695 | 0.690 | 0.684
NuSVC | 0.621 | 0.598 | 0.579
Gaussian Naive Bayes | 0.743 | 0.725 | 0.713

Precision, Recall, and F1-scores are all macro-averaged

TABLE V: Multi-Class Classification Results for Individual Labels | LR | SVM | LSVC | GNB
---|---|---|---|---
Addictive Design | 0.402 | 0.188 | 0.485 | 0.738
Censorship | 0.868 | 0.857 | 0.868 | 0.809
Cyberbullying | 0.210 | 0.130 | 0.086 | 0.766
Discrimination | 0.672 | 0.636 | 0.649 | 0.633
Other Ethical Concerns | 0.151 | 0.095 | 0.067 | 0.590

##  VI Discussion

Our results demonstrate that (RQ1) Reddit is a valuable source for feedback on
ethical concerns from marginalized communities, (RQ2) marginalized
communities’ ethical concerns center around manipulation and oppression, and
(RQ3) there is potential for this data to be collected and classified on a
large scale, to collate ethical concerns.

To address Reddit’s potential as a source of marginalized communities’ ethical
concerns on software (RQ1), we report that marginalized communities’
subreddits are a potent source of feedback on ethical concerns. Despite only a
5% platform-mention rate in marginalized users’ posts, the substantial amount
of Reddit data still leaves us plenty of content for manual and automated
analysis. Additionally, the level of ethical concerns is high, with 26.49% of
platform-mentioning posts referencing an ethical concern, comparable to Obie
et al’s rate of 26.5% human values violations found in user feedback[34].
However, although Reddit generally contains a high rate of ethical concerns
about software, Reddit’s efficacy shifts for different communities. For
example, women/AFAB (55.05%) had a far higher percentage of ethical concerns
than physically disabled users (9.71%). Additionally, we found that our posts
with ethical concerns were on average 1906.8 characters long. This high
average exemplifies Reddit’s ability to facilitate contextualizing detail in
its posts through its high character limit. Within posts, there is often
detail on why and how these ethical concerns arise as well as the effect of
these ethically-concerning situations on users.

Next, to address marginalized communities’ ethical concerns (RQ2), we discuss
the manipulation and oppression faced by users online. By structuring platform
development around profit, platforms have placed undue burdens on marginalized
communities. Through manual analysis of posts, we discovered that there seems
to be a tentative relationship between inappropriate content, cyberbullying,
scams, misinformation, and addictive design. In this relationship, platforms
elevate inappropriate and misleading information due to its volatile nature,
making it addictive to consume. This phenomenon allows platforms and content
creators to profit via advertising and manipulative practices [42]. Our data
shows that online manipulation and harassment may target the most vulnerable
and desperate for resources. For example, medical misinformation targets users
who have stigmatized medical problems like those relating to mental health or
sex. To assuage some online manipulation, platforms could add stricter
regulations for advertisers and harsher rules regarding scams by independent
creators. Moreover, they could try to identify and aid potentially targeted
vulnerable populations.

Another example of vulnerable groups being targeted is women/AFAB, especially
women/AFAB of color. It can be challenging to remove nonconsensual pornography
from platforms, with one user describing a situation where a 16-year-old’s
naked pictures were posted online. However, she could not file a police report
out of fear that her parents would find out. According to another user,
women/AFAB’s fully-clothed photos uploaded from Eid were used to
“auction…[women/AFAB] on live stream on youtube.” Despite their lack of
nudity, they were still being sexualized and rated [43]. This sexualization
points to the issue not being with women/AFAB and what they decide to do with
their bodies but how those bodies are perceived and treated. As such,
developers should change how they deal with nonconsensual pornography to
remove the burden from the victims and put it back on the perpetrators.

Finally, a frequent narrative in the zeitgeist seems to be that everything can
be found online. However, this is certainly not the case for all users, who
lack basic means and may depend more heavily on the Internet for resources and
information. Despite this greater need, marginalized communities often find
inaccurate information or information geared toward centralized groups rather
than marginalized ones. Like Wikipedia, other software platforms could devote
more resources to equitable and objective informational resources.

In addition, as relationships and identities shift online, software engineers
create their structures, evaluatory metrics, and interaction capabilities.
According to users, these tools often fall short and provide connections once
impossible. Platforms should reassess privacy threats by considering how
marginalized communities may need to shift their identity expression
contextually for safety purposes. Furthermore, they could create design
features that mimic real-life social interaction more closely to help users
build healthier relationships.

In Simone de Beauvoir’s The Second Sex, she argues that the man is regarded as
the default, while the woman is regarded as the “Other;”[44] unfortunately,
this phenomenon extends beyond gender into other marginalized identities.
Through the development of online identities and relationships, the
“Other[ing]” and further misrepresentation of marginalized users seems to
follow. Under-representation of an identity’s full diversity and over-
representation of a small sample of an identity lead to the misrepresentation
of these identities. As a result, pervasive stereotyping simplifies their
identities to often distorted and inaccurate perceptions. Creating a
democratic process for electing moderators could help solve the control of
marginalized communities’ online spaces by their more privileged counterparts
to allow these groups to represent themselves. By making sure to elevate
greater amounts of marginalized group members, platforms could help more
accurately represent their complexity. Additionally, stopping pre-selection
for and assumption of privileged identities in forms and other areas may help
decouple marginalized communities’ representation as “Other” from design.

To address the discrimination in content moderation, we discuss the inter and
intra-group conflict found in our posts. The cause of this conflict may be
what Du Bois defines as a “double consciousness,” where marginalized
communities see themselves through the view of the privileged group in
addition to their own. Marginalized communities adopt the harmful centralized
perspectives of, in this case, their and others’ races and treat themselves
and others with this viewpoint[45]. Intra-group conflict is also frequent in
trans spaces online, where trans users also deal with a “double
consciousness,” which derives from the survivalist need to fit into
cisheteronormative society and the right to be one’s actual self. This
friction also leads to conflict over how to define and qualify the trans
experience. Based on the posts, this conflict may also partly arise from
intergenerational conflict, where queer users feel this “double consciousness”
at varying levels based on when and where they were born. Currently, content
moderation algorithms over-police non-white [46] and queer communities [47]
because of these communities’ use of their vernaculars. However, examining
instances of differing inter and intra-group language types reported by users
in our posts and developing content moderation techniques centered around
marginalized users’ perspectives could make these algorithms more just.

Finally, concerning the tasks’ automation potential (RQ3), we attempt to
explain our relatively high F1-scores, considering the different
manifestations and words by which the same ethical concerns are described.
First, our experiment is lightly comparable to Iqbal et al.’s work,[27], which
identifies user feedback, like bug reports and feature requests, from
platforms’ subreddits on Reddit. Like us, they report on performance in the
[.7, .9] range for binary classifiers. Both our high scores suggest that
developers could collect this data on a larger scale to identify gaps in
current feedback elicitation practices. Also, our Gaussian Naive Bayes
classifier has a recall of .88. Recall is a more critical measure for this
binary task, as we would like to collect all possible ethical concerns rather
than leave some undiscovered. Additionally, our multi-class classifiers
categorize ethical concern type with an F1-score of .743. Although, we can
surmise that the censorship category performed most highly likely due to its
high occurrence (84.5%) among a single subreddit, “r/WhereAreTheFeminists”.
With a more variable dataset, the performance of the multi-class classifiers
would drop. Although, a fully classified dataset could give developers even
more insight into correlations between ethical concerns’ types, occurrence on
platforms, and different marginalized communities. Although we do not see our
current model as highly generalizable due to our small dataset and limited
selection of subreddits, a more generalizable model could give fine-grained
insight into the 586 different marginalized communities we targeted in our
study, among other potential marginalized communities.

##  VII Threats to Validity

A base shortcoming of this paper is the demographics of the authors. While
each community is represented in some manner by one of the authors, our only
POC author is white-passing, which may weaken our discussion of colorism and
racism online users face. Furthermore, we do not represent each of the 586
different communities and did not include these users in the analysis of their
concerns.

Another weakness of this paper is our ability to handle feedback from the
Global South. We only annotated posts in English, which creates a language
barrier between our work and non-native English speakers. Again, as we desired
to capture marginalized feedback, this is a crucial weakness. Future work
could analyze similar posts in other languages.

Additionally, Reddit tends to have the exact demographic we are looking not to
represent. According to Statista, most Reddit users are white, American men
[48],[49],[50]. However, we attempted to mitigate this issue by explicitly
focusing on subreddits that originated from marginalized communities rather
than from platforms or regarding technology. Unfortunately, this demographic
inequality and our use of a popularity metric for subreddit selection may have
biased group selection towards men. For example, the analyzed subreddits
“r/malementalhealth,” “r/asianmasculinity,” “r/southasianmasculinity,” and
“r/prostatitis” are geared toward men.

Future work could also handle intersectionality more adeptly than this paper.
For example, we have multiple intersectional subreddits. Although, for
simplification’s sake, we had to categorize subreddits into just one identity
based on the time of discovery. So, for example, “r/twoxindia” was included in
the women/AFAB category rather than the Global South category. Future work
could also endeavor to exclude Reddit as a social platform from the filtering
process as we posit that our results are skewed by our data source’s
influence. More generally, our results are highly dependent on the chosen
platforms and future work should seek to eliminate this bias; however, these
platforms are ubiquitous amongst users.

It is critical to note that in this paper, not every ethical concern detailed
in our dataset was analyzed and discussed. Even the ethical concerns that made
it into this paper could likely generate their own research directions. Beyond
the labeled data that was left out, and although we scraped 586 groups’
subreddits, less than a tenth of those subreddits made it to the data
annotation process. Although we posit that the concerns described in this
paper are general, critical phenomena faced by billions of users, these
concerns do not affect every user equally.

Considering our machine learning models, we cannot conclude that this model is
generalizable to a larger dataset when the data we trained the model on is
specific to a smaller group of subreddits. Although as is the trend within
software engineering, we cannot create a model that fits all users, all
people. Instead of insisting on creating top-down approaches to software
design, we ought to shift, more generally, to bottom-up approaches, which are
founded on the concerns of different groups and end in alternate products and
experiences.

##  VIII Conclusion

There is a long history of corporations exploiting consumers and privileged
groups benefiting from the abuse of marginalized communities. Unfortunately,
this trend is now mirrored in the transition to the online world. Though, with
it, has come a democratization of access and expression. Importantly, making
user feedback actionable, and doing so with a prioritization of those along
the margins, gives those oppressed potential to change the software which
affects them deeply. As the development process becomes more agile, software
can change quickly, and the views of those often overlooked can drive those
updates. Our work contributes to this pursuit by collating and analysing
marginalized communities’ ethical concerns in mentions of platforms in Reddit
posts.

## References

  * [1] S. Biddle, “Facebook report concludes company censorship violated palestinian human rights,” Sep 2022. [Online]. Available: https://theintercept.com/2022/09/21/facebook-censorship-palestine-israel-algorithm/
  * [2] “Myanmar: The social atrocity: Meta and the right to remedy for the rohingya,” Sep 2022. [Online]. Available: https://www.amnesty.org/en/documents/ASA16/5933/2022/en/
  * [3] K. Chayka, “The online spaces that enable mass shooters,” May 2022. [Online]. Available: https://www.newyorker.com/culture/infinite-scroll/the-online-spaces-that-enable-mass-shooters
  * [4] N. Woolf, “’puahate’ and ’foreveralone’: Inside elliot rodger’s online life,” May 2014. [Online]. Available: https://www.theguardian.com/world/2014/may/30/elliot-rodger-puahate-forever-alone-reddit-forums
  * [5] “Digital hate - hrc-prod-requests.s3-us-west-2.amazonaws.com.” [Online]. Available: https://hrc-prod-requests.s3-us-west-2.amazonaws.com/CCDH-HRC-Digital-Hate-Report-2022-single-pages.pdf
  * [6] K. Tiffany, “The women making conspiracy theories beautiful,” Aug 2020. [Online]. Available: https://www.theatlantic.com/technology/archive/2020/08/how-instagram-aesthetics-repackage-qanon/615364/
  * [7] S. Costanza-Chock, _Design justice: Community-led practices to build the worlds we need_. The MIT Press, 2020.
  * [8] J. Tizard, T. Rietz, and K. Blincoe, “Voice of the users: A demographic study of software feedback behaviour,” in _2020 IEEE 28th International Requirements Engineering Conference (RE)_. IEEE, 2020, pp. 55–65.
  * [9] E. Guzman and A. Paredes Rojas, “Gender and user feedback: An exploratory study,” in _2019 IEEE 27th International Requirements Engineering Conference (RE)_ , 2019, pp. 381–385.
  * [10] E. Oehri and E. Guzman, “Same same but different: Finding similar user feedback across multiple platforms and languages,” in _2020 IEEE 28th International Requirements Engineering Conference (RE)_ , 2020, pp. 44–54.
  * [11] E. Guzman, L. Oliveira, Y. Steiner, L. C. Wagner, and M. Glinz, “User feedback in the app store: A cross-cultural study,” in _2018 IEEE/ACM 40th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)_ , 2018, pp. 13–22.
  * [12] D. Martens and W. Maalej, “Extracting and analyzing context information in user-support conversations on twitter,” in _2019 IEEE 27th International Requirements Engineering Conference (RE)_ , 2019, pp. 131–141.
  * [13] H. Workman and C. A. Coleman, ““the front page of the internet”: Safe spaces and hyperpersonal communication among females in an online community,” _Southwestern Mass Communication Journal_ , vol. 29, no. 2, 2014\.
  * [14] T. O’Neill, “‘today i speak’: Exploring how victim-survivors use reddit,” _International journal for crime, justice and social democracy_ , vol. 7, no. 1, p. 44, 2018.
  * [15] A. Leavitt, “” this is a throwaway account” temporary technical identities and perceptions of anonymity in a massive online community,” in _Proceedings of the 18th ACM conference on computer supported cooperative work & social computing_, 2015, pp. 317–327.
  * [16] J. A. Koepfler, K. Shilton, and K. R. Fleischmann, “A stake in the issue of homelessness: Identifying values of interest for design in online communities,” in _Proceedings of the 6th International Conference on Communities and Technologies_ , 2013, pp. 36–45.
  * [17] Y. A. Rankin and K. K. Henderson, “Resisting racism in tech design: Centering the experiences of black youth,” _Proceedings of the ACM on Human-Computer Interaction_ , vol. 5, no. CSCW1, pp. 1–32, 2021.
  * [18] D. Pagano and B. Bruegge, “User Involvement in Software Evolution Practice : A Case Study,” in _Proc. of the International Conference on Software Engineering_ , 2013, pp. 953–962.
  * [19] E. C. Groen, N. Seyff, R. Ali, F. Dalpiaz, J. Doerr, E. Guzman, M. Hosseini, J. Marco, M. Oriol, A. Perini _et al._ , “The crowd in requirements engineering: The landscape and challenges,” _IEEE software_ , vol. 34, no. 2, pp. 44–52, 2017.
  * [20] T. Johann and W. Maalej, “Democratic mass participation of users in Requirements Engineering?” in _Proc. of the International Requirements Engineering Conference (RE)_ , aug 2015, pp. 256–261.
  * [21] D. Pagano and W. Maalej, “User feedback in the appstore: an empirical study,” in _Proc. of the International Requirements Engineering Conference_ , 2013, pp. 125–134.
  * [22] L. Hoon, R. Vasa, J.-G. Schneider, J. Grundy, and Others, “An analysis of the mobile app review landscape: trends and implications,” _Swinburne University of Technology, Tech. Rep_ , 2013.
  * [23] E. Guzman, R. Alkadhi, and N. Seyff, “A Needle in a Haystack: What Do Twitter Users Say about Software?” in _Proc. of the International Requirements Engineering Conference_ , 2016, pp. 96–105.
  * [24] E. Guzman, M. Ibrahim, and M. Glinz, “A little bird told me: Mining tweets for requirements and software evolution,” in _Proc. of the International Requirements Engineering Conference (RE)_ , 2017, pp. 11–20.
  * [25] M. Nayebi, H. Cho, and G. Ruhe, “App store mining is not enough for app improvement,” _Empirical Software Engineering_ , vol. 23, no. 5, pp. 2764–2794, 2018.
  * [26] G. Williams and A. Mahmoud, “Mining twitter feeds for software user requirements,” in _Proc. of the International Requirements Engineering Conference (RE)_ , 2017, pp. 1–10.
  * [27] T. Iqbal, M. Khan, K. Taveter, and N. Seyff, “Mining reddit as a new source for software requirements,” in _2021 IEEE 29th International Requirements Engineering Conference (RE)_. IEEE, 2021, pp. 128–138.
  * [28] R. A.-L. Fischer, R. Walczuch, and E. Guzman, “Does culture matter? impact of individualism and uncertainty avoidance on app reviews,” in _2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)_. IEEE, 2021, pp. 67–76.
  * [29] M. Tushev, F. Ebrahimi, and A. Mahmoud, “Digital discrimination in sharing economy a requirements engineering perspective,” in _2020 IEEE 28th International Requirements Engineering Conference (RE)_. IEEE, 2020, pp. 204–214.
  * [30] A. R. Besmer, J. Watson, and M. S. Banks, “Investigating user perceptions of mobile app privacy: An analysis of user-submitted app reviews,” _International Journal of Information Security and Privacy (IJISP)_ , vol. 14, no. 4, pp. 74–91, 2020.
  * [31] Z. S. Li, M. Sihag, N. N. Arony, J. B. Junior, T. Phan, N. Ernst, and D. Damian, “Narratives: the unforeseen influencer of privacy concerns,” in _2022 IEEE 30th International Requirements Engineering Conference (RE)_ , 2022, pp. 127–139.
  * [32] H. Khalid, E. Shihab, M. Nagappan, and A. E. Hassan, “What do mobile app users complain about?” _IEEE software_ , vol. 32, no. 3, pp. 70–77, 2014.
  * [33] R. A. Shams, W. Hussain, G. Oliver, A. Nurwidyantoro, H. Perera, and J. Whittle, “Society-oriented applications development: Investigating users’ values from bangladeshi agriculture mobile applications,” in _2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)_. IEEE, 2020, pp. 53–62.
  * [34] H. O. Obie, W. Hussain, X. Xia, J. Grundy, L. Li, B. Turhan, J. Whittle, and M. Shahin, “A first look at human values-violation in app reviews,” in _2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)_. IEEE, 2021, pp. 29–38.
  * [35] S. H. Schwartz, “An overview of the schwartz theory of basic values,” _Online readings in Psychology and Culture_ , vol. 2, no. 1, pp. 2307–0919, 2012.
  * [36] L. Olson, N. Tjikhoeri, and E. Guzmán, “The best ends for the best means: Ethical concerns in app reviews,” 2024.
  * [37] “Disability and health.” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/disability-and-health
  * [38] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,” _Journal of artificial intelligence research_ , vol. 16, pp. 321–357, 2002.
  * [39] N. Mishra, “India and colorism: The finer nuances,” _Wash. U. Global Stud. L. Rev._ , vol. 14, p. 725, 2015.
  * [40] K. J. Norwood and V. S. Foreman, “The ubiquitousness of colorism: Then and now,” in _Color Matters_. Routledge, 2013, pp. 9–28.
  * [41] “Choosing the right estimator.” [Online]. Available: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
  * [42] C. M. Gray, Y. Kou, B. Battles, J. Hoggatt, and A. L. Toombs, “The dark (patterns) side of ux design,” in _Proceedings of the 2018 CHI conference on human factors in computing systems_ , 2018, pp. 1–14.
  * [43] A. Khan, “The invisible trauma of ’bhabhi’ porn in the lives of indian women,” Jun 2022. [Online]. Available: https://www.vice.com/en/article/pkgdzg/bhabhi-porn-trauma-on-indian-women-savita-bhabhi-fetish-sex
  * [44] S. d. Beauvoir, _The Second sex_. Vintage Classic, 2015.
  * [45] W. E. B. Du Bois and M. Marable, _Souls of black folk_. Routledge, 2015.
  * [46] T. Davidson, D. Bhattacharya, and I. Weber, “Racial bias in hate speech and abusive language detection datasets,” _arXiv preprint arXiv:1905.12516_ , 2019.
  * [47] T. Dias Oliva, D. M. Antonialli, and A. Gomes, “Fighting hate speech, silencing drag queens? artificial intelligence in content moderation and risks to lgbtq voices online,” _Sexuality & Culture_, vol. 25, no. 2, pp. 700–732, 2021.
  * [48] S. Dixon, “Global reddit user distribution by gender 2022,” Mar 2022. [Online]. Available: https://www.statista.com/statistics/1255182/distribution-of-users-on-reddit-worldwide-gender/
  * [49] ——, “U.s. reddit user share by ethnicity 2016,” Feb 2016. [Online]. Available: https://www.statista.com/statistics/517229/reddit-user-distribution-usa-ethnicity/
  * [50] [Online]. Available: https://worldpopulationreview.com/country-rankings/reddit-users-by-country