# “We’re not all construction workers”: Algorithmic Compression of Latinidad
on TikTok

Nina Lutz<EMAIL_ADDRESS>0009-0001-8259-8442<EMAIL_ADDRESS>Department of
Human Centered Design and Engineering, University of
WashingtonSeattleWashingtonUSA and  Cecilia Aragon<EMAIL_ADDRESS>0000-0002-9502-0965<EMAIL_ADDRESS>Department of Human Centered Design and
Engineering, University of WashingtonSeattleWashingtonUSA

(2024)

###### Abstract.

The Latinx diaspora in the United States is a rapidly growing and complex
demographic who face intersectional harms and marginalizations in
sociotechnical systems and are currently underserved in CSCW research. While
the field understands that algorithms and digital content are experienced
differently by marginalized populations, more investigation is needed about
how Latinx people experience social media and, in particular, visual media. In
this paper, we focus on how Latinx people experience the algorithmic system of
the video-sharing platform TikTok. Through a bilingual interview and visual
elicitation study of 19 Latinx TikTok users and 59 survey participants, we
explore how Latinx individuals experience TikTok and its Latinx content. We
find Latinx TikTok users actively use platform affordances to create positive
and affirming identity content feeds, but these feeds are interrupted by
negative content (i.e. violence, stereotypes, linguistic assumptions) due to
platform affordances that have unique consequences for Latinx diaspora users.
We discuss these implications on Latinx identity and representation, introduce
the concept of algorithmic identity compression, where sociotechncial systems
simplify, flatten, and conflate intersection identities, resulting in
compression via the loss of critical cultural data deemed unnecessary by these
systems and designers of them. This study explores how Latinx individuals are
particularly vulnerable to this in sociotechnical systems, such as, but not
limited to, TikTok.

Latinx, TikTok, Content, Identity, Algortihmic Recommendations

††copyright: acmlicensed††journalyear: 2024††doi: XXXXXXX.XXXXXXX††conference:
27th ACM Conference on Computer-Supported Cooperative Work and Social
Computing; Nov 09-13, 2024; San José, Costa Rica††isbn:
978-1-4503-XXXX-X/18/06††ccs: Human-centered computing Empirical studies in
collaborative and social computing††ccs: Human-centered computing Social
recommendation

##  1\. Introduction

Persons of Latin American descent, or Latinx111While many descriptions of
Latine work use phrasing such as “Latino/Hispanic,” this paper purposefully
alternates between Latine and Latinx verbiage because of these terms’ gender-
neutral nature (Scharrón-del Río and Aja, 2020; ElC, 2023) as well as making
an important distinction between individuals who have roots in Latin America
(including the Caribbean Islands) as opposed to Spanish-speaking European
countries. We say diaspora to mean that our participants are individuals who
currently live in the United States, including immigrants and US-born
individuals. are among the fastest-growing demographic groups in the United
States (Zong, 2022), making up 19% of the total US population (Zong, 2022),
with Pew Research expecting them to represent 14.7% of voters in the United
States by the 2024 US presidential election (Jens Manuel Krogstad and
Natarajan, 2024). Latinx workers contribute 3.2 trillion dollars a year to the
US GDP (Terrill and Harth, 2023), but, on average, have less than a fifth of
the wealth of white families (Holzer et al., 2023). On TikTok alone, the
“Latino” hashtag has over 70 billion views and over 4 million videos (has,
2023b). However, within online spaces and particularly in the US context, the
Latine/Latinx diaspora is marginalized and often demonized. In 2016, after the
election of former President Trump with consistent cries to “Build a Wall” and
now in 2024 with unprecedented border crossings due to increasing instability
in the Latin American world, xenophobic rhetoric against Latinx people is
prevalent (Einstein, 2023; Gramlich, 2023), particularly on algorithmically
curated platforms such as TikTok. Despite this negative content, Latine
peoples still flock to these platforms and create their own online spaces
within them, with an estimated 1 in 5 adult users on TikTok being Latinx
(Valencia, 2023). However, we have not found any HCI research that
specifically focuses on Latinx identity formation within the sociotechnical
platform of TikTok.

Without understanding how the large and complex Latine diaspora utilizes and
experiences algorithmically enabled media systems like TikTok, this diaspora
is left vulnerable to problematic targeted information(Moran et al., 2023),
being the subjects of hateful information, and harassment within these
platforms. This project seeks to contribute a necessary bridge between
existing identity work in sociotechnical systems and the Latinx population. We
build on existing identity space work from queer and Black studies scholars,
who have extensively studied how marginalized groups build identities and
navigate online spaces through algorithmic marginalization and media
representation.

Through a bilingual interview study of 19 individuals and survey of 59, we
explore how Latine individuals experience Latine identity, called Latinidad,
within sociotechnical systems and through representative media. Our
contributions are threefold: (1) an empirical understanding of identity
formation as a sociotechnical phenomenon for Latine individuals, (2) an in-
depth qualitative study about individual responses to visual media in systems
like TikTok, gathered via visual elicitation, and (3) the novel concept of
algorithmic identity compression as the Latine diaspora on TikTok experiences
it.

##  2\. Related Work

In this section, we first examine the foundations of Latinidad and existing
literature regarding this population in CSCW and HCI. Then, we review existing
work on online identity construction and media representation for marginalized
populations. Finally, we analyze existing TikTok studies and visual
qualitative research methods for our methodology.

###  2.1. Latinidad and Latinidad in HCI and CSCW

Latinidad is an umbrella term for the identity of Latin American-descended
persons, as defined in our note on language. There are 33 countries in Latin
America, encompassing Central and South America and island nations in the
Caribbean (for Latin America and the Caribbean, [n. d.]). Latine people are
individuals who have ancestry from Latin America but are commonly classified
as “Latino/Hispanic”. This is a misnomer as not every Latin American speaks or
has a relationship with Spanish; over 200 million Latine people speak
Portuguese (por, 2024). Additionally, there are over 300 languages spoken
throughout Latin America, many of them Indigenous (Wor, 2019). Regardless of
language, Latin American peoples have experienced the repercussions of
historic Iberian colonization (Morales, 2019). In the United States, they are
racialized and face systemic barriers that Hispanic European immigrants do not
(Morales, 2019). Because our study focuses on people of Latin American roots
who live in the US, we often say Latine diaspora because this is a diasporic
population in the United States. While not all individuals who fall into this
category resonate with Latine or Latinx terminology, we have chosen this as a
category label for consistency in this paper. The Latine diaspora, given
linguistic, racial, and ethnic differences, is uniquely variegated and the
myraid of intersecting identities needs to be considered in research. However,
we acknowledge that in such a diverse diasporic identity, no label could fully
encompass everyone’s identities and experiences.

Current CSCW and CHI literature around Latinx populations focuses primarily on
education (Braswell et al., 2020; Garcia et al., 2023; Alvarez et al., 2020;
Gutierrez et al., 2020), technology design (Chiang et al., 2017; Navarro Sainz
and Prabhakar, 2021), linguistic studies (Do et al., 2022; Reitcheck and
Antunez, 2022; Bipat et al., 2019), and international research community
translation (Alvarado Garcia et al., 2020; Collective et al., 2021; González
et al., 1999; de Souza et al., 2018; de Paula et al., 2015). Within education
studies, focus has been dedicated to the important task of making sure more
Latinx students have access to computing education, given they make up less
than 10% of STEM fields, and that every community has different education
needs (Funk and Hugo Lopez, 2022; Braswell et al., 2020; Garcia et al., 2023;
Alvarez et al., 2020; Gutierrez et al., 2020). Technology design and
linguistic studies around and by Latinx researchers focus on technical
artifacts that are positioned to unique user needs in Latin America (Chiang et
al., 2017; Navarro Sainz and Prabhakar, 2021) and voice enabled technologies
that teach or focus on working with Spanish language and accents (Do et al.,
2022; Reitcheck and Antunez, 2022; Bipat et al., 2019). International research
community translation within CSCW and HCI seeks to weigh Latin American
contributions equitably as well as understand better research practices for
these communities (Alvarado Garcia et al., 2020; Collective et al., 2021;
González et al., 1999; de Souza et al., 2018; de Paula et al., 2015).

Though not centered on HCI, there is previous work focusing on Latinx peoples
and TikTok, such as Jaramillo-Dent et al.’s explorative work on the visibility
of migrant content on the platform (Jaramillo-Dent et al., 2022) and Sued et.
al’s study of how app deliverers perform collective narratives on TikTok (Sued
and Rodríguez, 2023). These studies utilize content analysis to understand
these specific sub-populations of Latinx peoples and how their posted TikTok
content interplays with their offline roles.

###  2.2. Representation of Marginalized Groups in Media

Recent technological developments have dramatically changed how media is made,
distributed, and consumed. Media experts have long described media as systems,
notably with Ball-Rokeach and Defleaur’s 1976 media system dependency theory,
positing that media must be seen in feedback with societal audiences through
systemic frameworks (Ball-Rokeach and DeFleur, 1976). TikTok is a
sociotechnical system that enables the creation and sharing of video media
through individualized media feeds distributed through an algorithmic
recommendation system (Narayanan, 2023; Tik, 2020), as opposed to other
multimedia social networks that focus on networks of followings or text-based
content. While these feeds are individualized, similar identity groups often
see similar content within this system (Kulshrestha, 2016; Kulshrestha et al.,
2021; Ross Arguedas et al., 2022; Gillani et al., 2018).

Each individual’s voluntary choices (i.e., who they follow, what videos they
like) and involuntary exposure due to media curations by algorithmic systems
influence what they see and therefore contribute to their own understanding of
their representation and identity. Media theorists have developed frameworks
for understanding how populations have been historically underrepresented in
media or stereotyped, reifying real-life social hierarchies (Med, 2023).
Negative mass media representation can result in stereotype advancement and
threat for already marginalized groups (Steele and Aronson, 1995; Appel and
Weber, 2017). Meanwhile, diverse and positive representations of typically
marginalized groups have been shown to have positive impacts on and off screen
(Georgiou, 2010; Allyn, 2021). Any widely used media system such as TikTok can
dramatically impact both sides of media representation and stereotype threat.

###  2.3. Identity Theory Online

This project draws heavily from the literature of identity theory and
formation online, particularly from queer scholarship as well as work
regarding racialization in algorithms. We utilize key lenses of identity
construction and affirmation in algorithmically controlled online spaces,
identity-affirming spaces as well as identity work and flattening.

The experience of identity is highly individual and personal, but it is a
social construct structured through membership in and outside social classes
and interpreted in systems and cultural norms (Goffman, 1956; Tajfel, 1974).
These constructs are also manifested in sociotechnical systems, where
racialization and gender categories can be limiting and harmful to those whose
identities are not easily contained in preset, often binary, categorizations
(Scheuerman et al., 2020; Bell, 2020).

Online communities have long served as identity-affirming places for
individuals. Queer people, people of color, and people going through difficult
life circumstances have routinely found affirming and safe spaces in social
media platforms (Dym et al., 2019; Scheuerman et al., 2018; Rho et al., 2017).
Scholars posit that seeking out identity-affirming spaces in sociotechnical
systems is part of doing identity work, where users perform labor to curate
routines, community, and content to reaffirm their identities (Simpson and
Semaan, 2021; Dym et al., 2019; Dosono and Semaan, 2019). This is a fluid
process in which identities shift, and users discover and adapt new ones over
time (Ibarra and Petriglieri, 2010).

Identities also shift when society and systems do not encompass their
complexity. This is seen in Walker and DeVito’s identity flattening (Walker
and DeVito, 2020) work, where users collapse a multifaceted identity into ‘a
less complex presentation of identity that the individuals believe will be
more acceptable within a space’ and DeVito’s expansion on this work with
transfeminine TikTok creators navigating how TikTok reduces their content
categories and identities in the context of content visibility (DeVito, 2022).
Users utilize identity flattening as a protective measure in response to
online harm, to conform to group norms, and to validate identities when
invalidated (Walker and DeVito, 2020; Blackwell et al., 2017).

DeVito (DeVito, 2022) and Karizat et al. (Karizat et al., 2021) define folk
theorization of identity on platforms, where individuals and groups
hypothesize how algorithms function and/or make identity work dangerous and
how individuals try to regulate these algorithmic systems (Simpson et al.,
2022). Finally, Lopez-Leon and Casanova published a case study on theoretical
underpinnings of LGBT+ Latinx counterspaces on social media based on the
social media usage of their (single) participant (Lopez-Leon and Casanova,
2023).

However, we found that a Latine perspective on identity experiences in
sociotechnical systems, particularly TikTok, was missing. Our work delves into
this complex diaspora and focuses on the multiplicity of Latine identities and
the specific harms that algorithms can cause by oversimplifying and
stereotyping such identities.

###  2.4. TikTok in CSCW and HCI

TikTok has become a massively popular platform in the last 4 years (Saric,
2023). Users may create, upload, or scroll through a feed of videos to consume
(tik, 2023), an algorithmically recommended feed called the “For You Page”
(FYP) (Tik, 2020).

The full details of the recommendation algorithm of the “For You Page” are the
private property of TikTok. The system learns from users’ behaviors (choosing
to watch specific videos, like, comment, etc.) and iterates and evolves the
user’s FYP (Tik, 2020). While following creators makes their content appear on
the FYP, users do not control the order or proportions of their feeds, except
for the ability to mute certain content with safety features (Pierce, 2022).

Several empirical and qualitative studies have been conducted on TikTok in HCI
venues. There are studies regarding education and informal learning (Ghosh and
Figueroa, 2023), creative collaboration (O’Toole, 2023), and political
communication on TikTok (Medina Serrano et al., 2020). Most pertinent to our
work are analyses of how specific marginalized groups experience TikTok. Much
of this scholarship has been spearheaded by queer, Black, and neurodivergent
scholars.

Studies of note that this paper draws from include Simpson and Seeman’s work
on LGBT+ users’ routines and experiences with FYPs and algorithmic exclusion
(Simpson and Semaan, 2021) and algorithmic curation (Simpson et al., 2022),
DeVito’s work on trans femme visibility traps and folk theorizations on TikTok
(DeVito, 2022), and Biggs et al.’s work on Queer Farming Utopias on TikTok
(Biggs et al., 2023). All of these studies look explicitly at how LGBT+ users
navigate TikTok regarding identity affirmation and how they face and combat
algorithmic harms from the platform. There are also studies in queer
scholarship that center particular harms that this community faces on TikTok
from shadowbanning (Rauchberg, 2022), a type of algorithmic oppression that
suppresses specific videos. Algospeak, misspelling or replacing letters in a
word, on TikTok has been studied as a mechanism of resilience against
shadowbanning (Klug et al., 2023).

Black studies scholars have studied how Black creators are silenced and face
harassment on TikTok and other platforms (Harris et al., 2023) and how Black
creators and users build resilient, joyful, and healing spaces in these
platforms (Musgrave et al., 2022).

We see a variety of identity hashtags promoting storytelling and
representation in TikTok studies from neurodivergent scholarship (Milton et
al., 2023; Schaadhardt et al., 2023). These studies show the impact and
building of identity-affirming spaces via authentic media representation and
in-group signaling. Nevertheless, there is still a need for specific studies
on the sociotechnical system of TikTok and identity formation for US Latine
diaspora populations. Given the underservice to Latine populations in HCI and
CSCW research, the lack of Latine representation in STEM roles and therefore
decision making regarding the design and development of these systems
(Vergara, 2023; Arellano et al., 2018; Per, 2023; Funk and Hugo Lopez, 2022),
and current political events such as the politicalization of Latin American
migrants at the US Mexico border and media positioning TikTok as a resource
for migrants coming to the US (Turkewitz, 2023; Gerber, 2023), we chose to
focus on how TikTok’s FYP media curation forms identity spaces for United
States diasporic Latine users while simultaneously compressing this identity.

##  3\. Methods

This study was conducted under the approval of our university’s Institutional
Review Board (IRB). It comprised a survey of 59 participants, followed by 19
semi-structured interviews. Study materials (survey, consent forms, interview
proctoring) were available in English and Spanish for all participants. All
participants entered this study through a research survey with clear opt-in
language and written consent in English or Spanish at the start of the survey.
Interview participants signed and returned additional consent forms in English
or Spanish before their interviews via email for permission to record and
transcribe the interview and for use of their anonymized quotes in this
publication. Interviewees also gave verbal consent and had the consent form
summarized verbally at the start of the interview before recording started.

Eligibility requirements for this study were that all participants must be: 18
years or older, live in the United States, understand English or Spanish, use
TikTok, see TikTok content related to Latinidad on their FYP, and identify as
Latinx. Participants were made aware of these eligibility requirements before
starting the survey, and their answers to all requirements were validated
before being included in the analysis or invited to participate in an
interview. Interviewees had the additional eligibility requirement of needing
access to a device that supported Zoom and screen sharing for the interview
protocol.

###  3.1. Survey

The survey served two aims: 1) An entry point for participants to be screened
for eligibility and contacted for a research interview and 2) Gathering data
about how different Latine individuals used TikTok and content they saw.

####  3.1.1. Participant recruitment and population

We utilized snowball sampling to recruit participants across a variety of
sources. These included: the first author’s social media and personal
networks, recruitment emails to listservs of relevant cultural groups at
[Anonymous University 1], [Anonymous University 2], [Anonymous University 3],
and flyers posted around the general [Anonymous City] area and [Anonymous
University 1’s] campus. We solicited TikTok users with public accounts
identified as Latinx in their biographies via direct messages from their
personal TikTok accounts.

119 participants began the survey, and of those, 65 were completed. 59 of
these responses were eligible and used in this study. Ineligible results were
determined by location or age ineligibility, spam responses to open questions,
and selections of mutually exclusive answers.

Survey respondents represented 14 Latin American countries, with the majority
representing Mexico (n=39), El Salvador (n=7), and Cuba (n=5). 11 participants
responded by identifying as Indigenous. Overall, 12 states were represented,
with the largest being Washington (n=19) and California (n=12). Most
participants were female (n=33), with 23 male participants and 3 nonbinary.

####  3.1.2. Survey design and implementation

The survey was designed by the first author and reviewed by the second. The
survey consisted of 33 questions in total, with a mix of multiple choice (23),
Likert rating scale (4), and open-answer (6) questions. The survey asked
participants demographic questions specific to their Latine identities and
then questions regarding TikTok. Questions covered three themes for
participants: their use of the TikTok platform, the content they see on
TikTok, and their feelings about this content. The survey was implemented in
SurveyMonkey with multiple distribution links to track recruitment results
from different recruitment channels.

####  3.1.3. Survey analysis

Complete and eligible responses were pulled from Survey Monkey and analyzed in
private Google Sheets. We followed our IRB’s standards in storing and
anonymizing survey data with participants’ unique identifiers from Survey
Monkey and did not record or store identifying information. We derived
descriptive statistics and frequencies of the multiple choice and Likert
answers to gain insight into the daily experiences of these users on TikTok.
We coded for themes in the open-ended questions which matched the interview
protocol to add breadth and depth to the analysis of this population and their
experiences on TikTok.

###  3.2. Interviews

Participants could provide their information at the end of the survey to be
recruited for an interview via email from the researchers. Subsequently, if
participants responded to this email, following the researchers confirming
eligibility of their survey results, they followed the consent procedure
described earlier. Interviews and consent materials were offered in English
and Spanish.

####  3.2.1. Interview recruitment and sample

It is worth noting that no sample set can fully represent the entirety of the
diverse Latine diaspora. However, we recruited across multiple venues (email,
flyers, social media) to try to attain as much diversity as possible in our
sample set. Participant demographics represent 7 countries and 9 other Latine
identities. In terms of ethnic representation, the majority was Mexican, with
11 of 19 participants identifying as Mexican. We identified 4 distinct lived
experiences among our interviewees regarding immigration: People who are
immigrants (n=4), US-born individuals who are children of adult immigrants
(n=6), people who have experienced or are experiencing being undocumented
(n=2), and people who are US-born to US-born parents (n=7). Participants were
located in 8 different states, with the majority in Washington (n=5) and
California (n=4). The average age was 25.3, with a minimum of 19 and a maximum
of 34. We acknowledge that our participants skewed young; however, this is
representative of the age range of TikTok users, where more than half of the
platform is aged 18-34 (Winter, 2023). 11 participants identified as female, 8
as male, and all as cisgender. 7 participants identified as part of the LGBT+
community. As Latine is not always a racial category, we asked participants
which US census category they utilize and how they racially identified
themselves. We also asked what identities, if any, from a list of popular
Latine identities informed by literature did participants identify with. This
was part of our interview protocol as well. Table 1 has a summary of these
identities.

Table 1. List of study participants and relevant identifiers ID |  Gender |  Age |  Nationality |  Latinx Identities |  Location |  Census Race Category |  Self Racial Identity
---|---|---|---|---|---|---|---
P1 |  Female |  21 |  Mexico |  Immigrant, No Sabo Kid, Dreamer, DACA |  Seattle, WA |  White |  Mestizo, Latino
P2 |  Female |  20 |  El Salvador, Mexico |  Chicana, No Sabo Kid, Child of Adult Immigrants |  Seattle, WA |  White |  Latino
P3 |  Male |  25 |  Mexico |  Afrolatino, Chicano |  Los Angeles, CA |  Two or more races |  Latino
P4 |  Female |  21 |  Mexico |  Indigenous Descent, Chicana |  Seattle, WA |  White |  Latino
P5 |  Female |  22 |  Mexico |  Chicana, Chola, Child of Adult Immigrants |  Boston, MA |  White |  Latino, Hispanic/Latino
P6 |  Female |  25 |  Mexico |  Chicana, Tejana, No Sabo Kid |  Philadelphia, PA |  Two or more races |  Mestiza
P7 |  Male |  24 |  Brazil |  Afrolatino, Chicano |  Dallas, TX |  Black or African American |  Black
P8 |  Female |  29 |  Peru |  Chola, Immigrant |  Boca Raton, FL |  Two or more races |  Latino, Hispanic/Latino
P9 |  Male |  25 |  Bolivia |  Child of Adult Immigrants, Indigenous Descent |  Dallas, TX |  Black or African American |  Black
P10 |  Female |  34 |  Mexico |  Indigenous Descent, Chicana |  McAllen, TX |  White |  Mexican
P11 |  Male |  32 |  Mexico |  Chicano, Cholo |  San Francisco, CA |  White |  Mexican American, Latino, Hispanic/Latino
P12 |  Female |  22 |  Mexico |  Child of Adult Immigrants |  Portland, OR |  White |  Mestiza, White
P13 |  Male |  25 |  Cuba |  Immigrant |  San Jose, CA |  White |  Hispanic/Latino
P14 |  Female |  19 |  Ecuador |  Child of Adult Immigrants |  Ostego, MN |  American Indian or Alaska Native |  Latino, Hispanic/Latino
P15 |  Male |  32 |  Mexico |  Indigenous Descent, Chicano, Dreamer |  Seattle, WA |  Two or more races |  Latino
P16 |  Male |  25 |  Bolivia, Cuba |  Indigenous descent, Immigrant |  San Francisco, CA |  White |  Hispanic/Latino, Hispanic, White, Latino
P17 |  Female |  26 |  El Salvador |  Afrolatina, Tejana |  Houston, TX |  Two or more races |  Hispanic/Latino, Latino
P18 |  Male |  27 |  Cuba |  Immigrant |  San Francisco, CA |  Two or more races |  Latino
P19 |  Female |  26 |  Mexico |  Indigenous Descent, Chicana, Child of Adult Immigrants |  Seattle, WA |  American Indian or Alaska Native |  Indigenous

####  3.2.2. Interview protocol and moderation

We conducted 19 total interviews via Zoom, lasting approximately 45 minutes
and transcribed by the Zoom software. 17 were conducted in English and 2 in
Spanish. Spanish interview transcripts were translated into English by the
first author before analysis. Quotes from Spanish interviews are marked as
translated in this publication. Participants were compensated with $30 Amazon
gift cards for taking part in the interview process, which lasted an average
of 50 minutes. Survey participants did not receive compensation and the
average survey time was 5 minutes and 11 seconds.

These were semi-structured interviews with a protocol of descriptive, open-
ended questions, followed by a visual elicitation exercise detailed in 3.2.3
(Wengraf, 2011; Glegg, 2019). The questions focused on how the participant’s
Latinidad identities had formed and changed throughout their life, then asking
them about their TikTok usage, the content they had seen on TikTok, and the
experience of their identities on TikTok. For our study, the ability to
understand platform usage in the context of lived experience and self
conceptualization of Latinidad was particularly important in these interviews,
as per other CSCW studies like Simpson and Semaan’s 2020 study on LGBT+
encounters on FYPs that focus on using semi-structured interviews to create
life histories about their identities along with platform usage and content
(Simpson and Semaan, 2021).

####  3.2.3. Interview analysis

We utilized a constructivist grounded theory (Charmaz, 2014) approach to
analyze the interviews, particularly around identity formation online allowing
for the lived experiences and themes in interviews to construct new theories
via relation to existing ones (Simpson and Semaan, 2021; DeVito, 2022). The
first author engaged in open-ended coding of the interviews and utilized
detailed notes and linked text files to associate open codes with direct
quotes from the interview transcripts (Charmaz, 2014). From this, the first
author then used thematic analysis (Braun and Clarke, 2006) to group these
emergent codes into key themes and findings for future relation and analysis
to existing theory and the development of our theory of algorithmic identity
compression (see Section 5). This was an iterative process, with several
rounds of coding as the authors met regularly to discuss emerging themes from
these iterative analyses.

To stay consistent with grounded theory approaches, we were careful not to
impose categories external to the interview data in these emergent codes and
resulting themes. We use the themes to discuss findings in the next section,
within the context of the greater survey data and TikTok platform affordances.

###  3.3. Visual Elicitation via Diamond Ranking

Given the visual nature of TikTok, we chose to incorporate a qualitative
visual research methodology via visual elicitation of TikTok content in our
interviews to understand how participants conceptualized and related this
content to Latinidad. These elicitations served as conversational probes in
our interviews to gather further data about how participants thought about
this content, in line with qualitative research tradition in using visual
media to deepen qualitative insights in interviews (Glegg, 2019; Carlile,
2002; Crilly et al., 2006). This conversational data was analyzed with the
other interview data with the same methodology explained in 3.2.3.

This was also done acknowledging that we wanted to have conversations around
potentially negative media and about the intersection of identities such as
race and gender presentation within Latinidad. Visual elicitation has been
shown to help with potentially sensitive conversations, as per Dr. Stephanie
Glegg’s typology of qualitative visual research methods in interviews in the
medical space (Glegg, 2019). Additionally, the methodology of showing the
participants visual media selected by the research team enabled these
conversations without the researcher having to see their FYP, which may be
incredibly personal and include items irrelevant to the study.

The particular methodology utilized in our interviews is the Diamond Ranking
Method, a visual research instrument initially seen in the classroom and
educational literature to allow children to express feelings regarding
different classroom settings or other concepts (Clark, 2012). This method has
been previously used in HCI literature, particularly at the intersection of
HCI and education, in Strohmayer et al’s 2015 paper on learning ecologies in
homeless populations (Strohmayer et al., 2015). Diamond Ranking features cells
arranged in a diamond shape on one axis from most (the topmost cell) to least
(the bottommost cell) of a metric chosen by the researcher (Clark, 2012),
affording the collection of structured data about how participants
conceptualize visual objects or representations with respect to this axis and
to one another (Clark, 2012). This is illustrated in Figure 1. An example of
this exercise and media from our interviews is in the Appendix in Figure 3.

Figure 1. Example of two Diamond Ranking Method instruments, where users would
place media objects in the ranked cells. The leftmost diamond contains 9 cells
and is not a forced choice, as it has a middle cell highlighted in pink, akin
to odd numbered Likert scales. The rightmost is a forced-choice diamond with
no middle cell, akin to even numbered Likert scales. Here, the number 1
relates to the most extreme affirmative point of the chosen metric, with each
increasing number left to right and top to bottom relating less.

An illustration of two diamond ranking measures. The left is a diamond
arrangement of 9 cells with the fifth cell highlighted as the middle cell in
pink. The right is the arrangement of 20 cells.

We employ this method in our interviews to understand not only how users
reacted to content shown to them in the interview, but also how they organized
their knowledge and reactions to content in relation to other content. We
adopted Diamond Ranking into a web application for use in the interview,
conducting the exercise via Zoom by having the participants open the web
application and share their screens. Participants were asked to choose one of
their identities (e.g. Mexican) to perform two Diamond Ranking exercises while
proctored by the researcher for conversational probing to explain their
thought processes.

The first exercise featured 20 face images and a 12-cell pyramid. This forced
participants to leave some images behind and not have a true neutral ranking
in their diamond, as per Figure 1, furthering conversation behind their
choices. They were asked to rank these individuals from most to least
representative of their conceptualization of the chosen identity (e.g.
Mexican). Participants were told this was a thought exercise and were
instructed to discuss their logic with the researcher, and reflect on the
completed ranking and discuss how their decisions related to their lived
experiences or things they’d seen on TikTok.

Participants then completed a non-forced choice 9-element pyramid of 9 TikTok
videos, also proctored by the interviewer. Participants watched all 9 videos
and were told to rank them on the same axis of most to least representative of
their chosen identity and talk through their ranking logic and reaction to the
media with the interviewer.

The full set of identity options for this exercise was determined from our
intake form of our participant demographics, as seen in Table 1. Each identity
had different media collected by the research team and were screened to avoid
any inappropriate content, such as violence or sexually explicit content. For
the first exercise, the first author searched these identity terms on the
TikTok desktop search page and took the first 20 faces from the results on
each page via screen cropping. For the second exercise, a random sample of 9
videos 30 seconds or less from this first page was selected.

As this methodology served as a conversational probe and allowed for
particular reflections, we did not analyze the rankings themselves, but only
the conversation elicited from them.

###  3.4. Researcher Positionality

Both authors identify as women and members of the Latine and Hispanic
diasporas in the United States. However, they acknowledge their racialized,
linguistic, and citizenship privileges (both authors were born in the United
States) within the Latine diaspora, along with the power dynamics of being
researchers at a university. While the authors come to this research with
cultural and linguistic competencies to analyze this qualitative data, they
lack full context for some members of this diverse community, particularly
certain members of color, members who have little or no access to the English
and Spanish languages, and members who are not US citizens. The first author’s
positionality also helped with recruitment, given her Latinx networks on
LinkedIn and Instagram, but may have been biased towards certain geographic
and ethnic identities and other lived experiences in this diaspora. The
authors are of different generational demographics: the first author is a
Millennial and the second is GenX, giving them different generational reads
and perspectives to this data analysis.

##  4\. Results

In the following subsections we address our survey and interview findings. We
first describe in 4.1 how Latine users experience and use TikTok and in 4.2
users’ folk theorizations about how and why TikTok shows them Latinx content.
We then report themes from our visual elicitation exercises with the Diamond
Ranking Method; in 4.3 users sort which content creators look and feel Latine
to them and in 4.4 we summarize narratives of Latine content from this
exercise.

###  4.1. Latine users’ content exposure and consumption

####  4.1.1. Finding and curating positive, representative spaces

Overwhelmingly, our participants find great joy in their FYPs, particularly
regarding the Latine representation and cultural content they see on TikTok.
The most frequently encountered content by Latine users in our study was
creative content, broader cultural content, and content about Latine lived
experiences and identity. We provided in Table 2 in the Appendix of this paper
a full frequency distribution of different categories from our closed codes of
our survey and in Table 3 a frequency that combines these closed codes with
interview categories of content.

It is worth noting that 10% of participants do not feel represented at all by
their FYPs, with 10% reporting ‘Almost never’ seeing Latinx people who share
their racial identity on their FYPs, particularly Indigenous and Afrolatine
participants. 23% of participants, who identify as LGBT+, do not see Latine
people that share their sexuality on their FYP and 7% of participants do not
see their gender identity represented on their FYP. Outside of representation,
participants have also encountered negative content, with 42% of participants
encountering false information and 53% having seen violent content about
Latinx people and culture on their FYP.

However, participants still enjoy their overall TikTok FYP experience with 86%
of participants reporting their FYP page makes them feel happy. Interviewees
describe feeling represented and included, and they postulate that TikTok is
good for representation of the Latine community on and offline. They attribute
much of their joy to connecting to shared culture online and seeing others
participate in cultural trends. In both surveys and interviews, participants
also described feeling joyful learning about their culture and feeling
included in the content they see.

  1. “When I see the Samba dance [on a TikTok challenge]…it makes me…feel like I could actually engage in this challenge to express my Latino identity…it gives me the sense of belonging. It brings me joy.” - P7

  2. “TikTok has positively impacted my identity by allowing me to learn about my culture’s history, new books to read or recipes to try, events happening around the world or near me, etc. This is greatly shaped my viewpoint and cultural experiences of my identity.” - Survey response

  3. “I don’t think I feel alienated. If anything, I feel included…I really do think it’s [TikTok] has helped put us [Latine people] out there more than ever.” - P10

This feeling of connection also came from participants deeming much of their
FYP content as authentic, and seeing some of this content with in-group
signaling via cultural artifacts and practices, and specific TikTok stylings
of these signals, like particular music or sounds over videos. They correlated
these types of signals with videos being may by Latine creators for Latine
audiences to celebrate and share culture and life moments. During the visual
elicitation activity, participants saw videos they deemed to be authentic and
discussed how much they valued these types of videos, describing their
familiarity with aspects of the videos.

  1. “A lot of these were very authentic sounds. I’ve heard music, I listen to…and like, my Cuban friends who would go back [to Cuba] would have taken those pictures.” - P13.

####  4.1.2. Mixed language FYPs of Latine Users

TikTok’s FYP can be multilingual, showing videos in different languages in the
same feed, creating unique experiences for Latinx users. Participants have
experienced not just the mixture of English and Spanish content, but also
discourse videos in English or Spanish about the the Spanish language and its
relationship to Latinidad, with several interviewees (n=9) reporting seeing
English content about the Spanish language.

Based on our survey, only 10% of our participants have a TikTok FYP in one
language, with only 8.5% having only English FYPs. 44% had mostly English with
some Spanish and 36% report about an even split between Spanish and English.
It is important to note that not all our participants have a connection to
Spanish, as not all Latin American countries are Hispanic. These participants
reported having primarily English content in their FYPs, along with some
Spanish. Interviewees also report frequently seeing videos that feature both
English and Spanish in the same video.

91.5% of all survey participants report seeing content in Spanish on their
FYP, grouping them into a Hispanic identity they may not resonate with. In
interviews, only 2 participants reported being asked by popup if they knew
Spanish, but 18 out of 19 received content in Spanish and English. Spanish
language comprehension varies among participants. In our survey, 22% reported
not being fluent or fully comfortable with Spanish, despite being of Hispanic
heritage. It is worth noting that this 22% does not include the 7% of
respondents who have no Hispanic heritage due to being from Brazil, Aruba, or
Jamaica.

Participants report mixed feelings about seeing Spanish content and the
Spanish language itself. Some participants feel represented when they see
Spanish content on their FYP, as they see Spanish as an important community
unifier. Other participants did not feel this representation due to feeling
like imposters at various points in their life for not knowing Spanish well.
They discussed resonating with the “No Sabo Kid” identity that is increasingly
popular on TikTok, referring to individuals who do not speak or know much
Spanish (Lavin, 2023). However, ”No Sabo Kid” and Spanish content are further
problematized in Indigenous contexts, with indigenous participants feeling
frustrated at the ”No Sabo” trend and other interviewees feeling conflicted
about the Spanish language’s association with Latinidad, given that it is a
colonial language.

  1. “I see Spanish [as]…a sense of pride and unity to the Latino community because it connects us quite a lot.” - P9 (Translated from Spanish)

  2. “Not being able to confidently speak in Spanish has made me question my identity…but Spanish is colonial.” - P2

  3. “There’s a lot of words that…in Spanish, that Indigenous communities say: ’That’s kind of a slur to us.’. Everyone says ‘No Sabo’, and Indigenous communities, they’ll kind of say: ’I didn’t learn Spanish at all, I only know English and my native dialect, please don’t call me that.’” - P4

###  4.2. Latine users’ folk theorization and FYP experiences

In a form of folk theorization inquiry, as defined by DeVito (DeVito, 2022),
participants hypothesize how an algorithmic system works. Interviewees were
asked their hypotheses as to why they are shown Latine content on their FYPs.
All (19/19) interviewees report they take deliberate actions to curate an FYP
that includes Latine content, but they still see some negative content and
conceptualize these as interruptions to their curated pages.

####  4.2.1. User actions based on folk theorization

Participants expressed a strong desire to have Latine content on their FYP and
used active measures to try and keep that content. The most popular active
measure is following Latinx creators when they see them on their FYPs,
something all interviewees report doing and survey respondents also report at
a high rate of 83%. We label active measures users take to influence the
algorithm to show them Latine content as voluntary user factors. Participants
also described involuntary factors (e.g. location) they believe TikTok takes
into account when showing them this content. We summarize both types of
factors in Table 4 in the Appendix.

Participants conceptualize these active measures as a responsibility and
method to stay connected to their community and identity. This is largely
driven by participants expressing great joy at seeing Latine content from the
algorithmic recommendations, as they have not seen it as regularly on other
social media. One participant specifically mentioned how the algorithmic
recommendations of Indigenous content are helping her reclaim her Indigenous
identity and the happiness she felt regarding this.

  1. “I’m also Indigenous. And I see a lot of [Indigenous videos] on TikTok which is really cool…it means a lot to me when I see something that reflects the Indigenous tribe, or like region where my family is from.” - P4

  2. “But when they come up on my For You Page, I do go out of my way…to follow like more Hispanic accounts just to kind of stay in the culture and keep like my language…up to date.” - P13

When probed about what factors they hypothesize TikTok’s algorithmic
recommendation system has access to, no participant discussed feeling uneasy
about these involuntary user factors being collected by TikTok, such as
location tracking to feed them Latine content that reflected their lived
experience. In fact, participants often enjoyed being shown very location
specific Latine content that they related to and could identify.

  1. “And I think maybe they [TikTok] just track my location. But I’ve gotten a lot of suggest like “for you” suggestions…to follow people. And I can tell immediately…’Oh, I see those walls, and like you’re in Texas’. And that’s really fun.” - P6

####  4.2.2. User data hypotheses

Participants overwhelmingly feel that TikTok has data confirming they are
Latinx (6/19), but not always data regarding more granular identities such as
Indigeneity or nationalities. Regarding ethnic identities, 13 out of 19
participants felt TikTok had data about their ethnicity and of these, 7 were
Mexican. Only 4 participants reported that they suspected TikTok’s algorithm
had them associated with a non-ethnic identity, such as “Chicano”, an identity
associated with Mexican-Americans (Tatum, 2017). Of these, 2 were Mexican and
2 were not. Overwhelmingly, participants felt TikTok categorized them as
Latinx but not anything else, with the exception of Mexican participants who
felt strongly TikTok knew they were Mexican.

  1. “I know they [TikTok] know Latino, but I think they’re still trying to guess more.” - P6

  2. “I felt like it [the algorithm] already knew I was Mexican, for some reason.” - P12

Some participants report they believe the algorithm is confused or has
conflicting data that is changing for them – i.e. showing them one ethnicity
for some time, then switching (n = 4). This is largely due to observations
that they interact with content that spans identities (i.e. Spanish language
content or sports) or interactions with other users of other sub-identities
(i.e. Cubans and Mexicans sending TikToks to one another), pushing different
content onto their pages.

  1. “I confuse it [the algorithm] so much…it doesn’t tell you where I’m from.” - P16

  2. “I think they [TikTok] think I am mixed Hispanic, because I get a lot of Mexican content, yes, but I also get like broad general Latinx content. I get some because my partner’s Peruvian, so I get a decent amount of Peruvian content. Because one of my closest friends is Guatemalan, I get a lot of Guatemalan content as well.” - P5

####  4.2.3. Negative interruptions and associations to Latine identity spaces

Our participants believe they have curated positive and often accurate FYPs
and affirming identity spaces for their Latinidad. However, several
participants (n=7) have experienced interruptions of various degrees of this
positive and specific FYP space.

They theorize this interruption comes from particular content being associated
with the content they do enjoy via the recommendation system, but feel very
sad and betrayed about this interruption. Three key negative interruption
types were identified from interviews: (1) Seeing cultural stereotypes and
humor they feel is harmful to Latine communities, (2) news and current events,
and (3) seeing Spanish language content when they have not reported knowing
Spanish.

Negative, stereotypical humor was a frustrating content interruption
experienced by participants on their FYPs, often in the form of skits that
belittle Latine femmes, play into other negative stereotypes around Latine
people (i.e. drunken, unintelligent Latino men), or other stereotypical
content coming from outside of the community. Participants expressed
disappointment but not surprise when they encountered this type of content on
their FYPs. They theorize that this content is more popular on different parts
of TikTok and is recommended to them by related hashtags.

  1. “…every now and then too, my For You Page is like messed up, I will see some where they kind of do like stereotypes. And I don’t like…the funny stereotypes when it isn’t that actual person…who’s from the community….who’s like doing these stereotypes of like…the drunk Tio and stuff like that.” - P1

  2. “I would categorize that as misogynistic, personally. And I’m sure there’s comments on there that are misogynistic as well. Those types of skit videos lead to a lot of negativity.” - P4

Similarly, participants describe news and crises events as further content
intrusions recommended to them. Especially when they see negative comments
about Latine people, reminding them their feed exists withing a larger
platform of other content and features, including harmful viewpoints towards
Latine people.

  1. “…there was like some news story I saw where people were saying that like border control officials had shot across the river, and hurt someone on the Mexico side…it’s just something I felt really negative about. And going through the comments…there was a lot of like mixed emotions, both positive and negative about the situation. And I was like you would think…going through my For You Page, that you wouldn’t see those kinds of comments like that. The fact that you are just kind of like reminded…this really is like an Internet space. It’s not just your niche space.” - P5

Participants feel another feature of content recommendation that can be
interruptive is when participants who are less comfortable or do not have a
relationship to Spanish see Spanish language content. Participants found this
invasive and frustrating on TikTok, as they have not signaled to the app that
they speak Spanish and may not be fluent in it. This discomfort was not
reported for English content, but our two non-English speaking participants
had not encountered English content on their FYPs.

  1. ”…when I see content that is intensely Spanish…like I speak a little bit of Spanish…I’m not super fluent…So when people just run off with it…that can be a little frustrating.” - P6

###  4.3. Sorting through who “looks” and “feels” Latine

We utilized the Diamond Ranking Method, as explained above in Section 3.3, to
understand how interviewees conceptualized Latine TikTok content.
Particularly, we wanted to know how representative popular creators were to
their concepts of identity. This served as a conversational probe, revealing
themes not exposed in other parts of the study. The diamond enabled and
facilitated difficult conversations around race, class, featurism, sexism, and
stereotypes, and vignettes that would have been difficult to attain without
visual elicitation.

####  4.3.1. Organizing subjects as more and less Latine

When ranking human subjects of popular videos as most to least representative
of their identity, participants most heavily relied on facial features and
hairstyles (16/19). After phenotypical usage, association with family members
(14/19) and friends along with the use of accessories and apparel was most
common (14/19). Participants also related ‘emotions’ and ‘vibes’ they read or
felt from the visual media to their lived experiences in this identity group.
The full breakdown of the thematic strategies used by interviewees is in Table
5 in the Appendix.

  1. “I think the face structure is very Peruvian….and she has black hair, but she does some highlighting, and that’s what we do. We do the highlighting like a lot.” - P8

  2. “…[he] kind of reminds me of my brother just when he was younger.” - P2

  3. “I associate Cuban people with a lot of expressiveness and…facial reactions behind your emotions.” - P13

All participants acknowledged that any of the faces present could be their
identity, acknowledging the wide diversity within the diaspora. However, some
faces were left behind more often than others. This was particularly salient
for pictures of faces who were identified as ‘Black’ or ‘Afrolatinx’ by
participants, who commented that while they knew Afrolatinx individuals were
part of the diaspora, they were less represented and discussed this dissonance
in the exercise.

  1. “Any of these people can be Mexican…Mexicans can be any color, shape, or size….But I do feel just predominantly what most people consider Mexican, these people definitely fit that more than the other people.” - P10

  2. “I mean, I know there’s like Black Mexicans, I mean Mexico is super diverse, but just from stereotypes…[people] don’t know that.” - P15

Afrolatinx participants (n=3) in our study ranked these images higher, but
also acknowledged that these images are not what most people envision when
they think of Latine peoples and they purposefully have had to seek out
Afrolatinx content on TikTok as they did not find it was recommended to them
as often. Participants discussed how they have faced anti-Black racism and
bias within the Latine diaspora and have not always seen their identities
represented in Latine spaces, and have had their Latine identities questioned
due to their Blackness.

  1. “You know how we [Latine people] have a whole lot of issues that arise when it has to do with skin color. So most times people might have a different perspective and belief about you.” - P9 (translated from Spanish)

  2. ”People are always like ‘Are you both your parents Salvadorian? Are you sure?’ And I’m like: ‘I’m sure.’” - P17

####  4.3.2. Default Latino == Mexican

A key theme that surfaced across the Diamond Ranking of videos and pictures
was many participants spoke about Mexicans being the “default Latino” in the
United States and how they see this being repeated in TikTok videos as well.
Many participants discussed this in relationship to appearances of individuals
in the exercise. They felt overall Latinx people and content are associated
with being Mexican, and Mexcians are overrepresented on and offline. Many non-
Mexican participants have been assumed to be Mexican by non-Latine people and
in turn felt unseen and not reprensented in and out of Latinx discourse.

  1. ”[When] we think about Latinidad..It’s very Mexican centric.” - P2

  2. “I think what most people think when they think of Latinx people, they think about like Mexicans…when I was small they [schoolmates] used to just think that I was Mexican…which it did kind of irritate me.” - P14

However, many non-Mexicans have found community with Mexican individuals over
commonalities, such as language and food and seeing that content on TikTok.
Participants discussed that this community and content feel more familiar to
them than much white American content, and add to their feeling on belonging
on their FYP and in real life.

  1. “…Mexican community…through the language, and food…lots of things bridge the [cultural] gap, or make me feel like I’m part of the larger sort of group.” - P13

Seven Mexican participants discussed how TikTok knew about their ethnicity and
suggested that TikTok may be primed to guess Mexican over other ethnicities,
acknowledging their privilege in being frequently represented in Latine
content and spaces, including TikTok, where they frequently see content that
is specific to Mexicans or Mexican Americans.

  1. ”I feel like I have the privilege to feel seen by a lot of Latinx community in the US. I don’t think it’s all-inclusive of the greater Hispanic community.” - P5

However, some Mexican participants (n = 6) discussed feeling targeted by this
default and visibility by non-Latine people, such as assumptions around
Mexicans being undocumented and uneducated, while other communities may not be
as predominantly associated with these negative stereotypes due to less
visibility.

  1. “…like a lot of people associate [being undocumented] with Mexican, specifically …that’s hard to navigate.” - P1

###  4.4. Evaluating popular and limiting narratives in Latine content

Along with discussing which creators were seen as more or less Latine,
participants also ranked content videos. Common themes and tensions were
identified in this portion of the interview, with two key narratives emerging:
(1) Negative stereotypes of Latina women and their intelligence, (2)
associations with Latino men and manual labor.

Many recipients felt ‘stuck’ in these narratives and similar ones, which they
felt portrayed Latine people as always being poor with limited options. While
they acknowledged that many Latine people have less wealth than white
households, they still crave more diverse representation about Latine lived
experiences. Some participants even felt that seeing these limiting narratives
repeatedly in social media could feed into predestination, with people feeling
they have less options and and not pursuing alternatives due to this
representation.

  1. “But there’s literally always going to be more to us than those two socioeconomic labels.” - P4

  2. “There’s a narrative of hard work which implies physical labor. And I think as Mexicans and Latinos, we’re really proud of that. But I think that also gets us stuck in not believing that we can do other things….Not necessarily bigger things. That might be exactly what you want to be doing. But knowing that you’re capable of doing whatever it is that you want to do and that you see other races…or other groups of people do.” - P15

####  4.4.1. Sexy, dumb Latinas

In many of the Diamond Ranking Method datasets, there were skit videos
portraying femme presenting Latine individuals or individuals portraying them
in negative ways. Particular content of note for participants was the “Hot
Cheeto Girl” stereotype enacted in one of these skit videos, a negative
archetype of Latina women that displays them as rude, stupid, and disruptive,
reifying existing biases against women of color (Froio, 2023).

Participants all responded negatively to the “Hot Cheeto Girl” skit, and other
content related to it. They reflected on having seen this content on their own
FYPs and other social media networks and felt many of these videos portray
Latinas as dumb and overly sexual. Participants discussed how this stereotype
had effected them and people they had known negatively in real life, but also
highlighted how so many Latinas in their lives counteracted this stereotype
while having some of the traits that are over-exaggerated in these videos.
Participants underscored wanting this narrative to end and be able to claim
some of these traits of femininity and humor for their own without negative
assumptions about their intelligence for liking spicy food and having acrylic
nails.

  1. “They’re never portrayed as being intelligent. It’s just like screaming and…eating Hot Cheetos…it just sucks. I feel like I was never taken seriously in school because of like stereotypes like this”. - P12

  2. “I think it definitely could be harmful because it just like downplays, a lot of a person’s like inner attributes…especially downplays like intelligence. And a lot of my friends I know that have these Hot Cheeto-like personalities, are actually some of the smartest people that I know. They’re all in grad school right now…these stereotypes about those personalities downplays that.” - P5

####  4.4.2. Manual laborer Latino men

Participants felt torn regarding the association between Latino men and manual
labor (construction, landscape, etc). For many participants (n = 7) this type
of elicitation reminded them of loved ones, particularly fathers. Participants
expressed a desire for this type of labor to be valued and respected, and that
they felt pride in the hard work that this labor requires being embedded in
their cultures and lives. However, like the ”Hot Cheeto Girl” stereotype, they
do not want other traits like intelligence to be extrapolated from this
association, and more representation for the vast array of Latine lived
experiences beyond manual labor, without discounting it.

  1. “I feel like sometimes people think that Mexicans being considered hard workers or just construction or just laborers is derogatory. And I’m never gonna think that that’s derogatory. I embrace it. And I love the fact that people think we’re hard workers and…I know a lot of people don’t like seeing, you know, brown people in hard hats…but…we’re not all construction workers….Every year the population with degrees that are Hispanic grows and grows. I love that. But also I take pride in the trade and the blue-collar side of of our people. And you know the people picking our food, deserve all the respect in the world. So I will never see that as a negative.” - P10

  2. “…a lot of us in the community tend to work…hard labor jobs…my dad also has a vest like this one.” - P1

##  5\. Discussion

Through our empirical interview and survey findings, thematic analysis, and
existing literature, we define 4 key tensions the Latine diaspora experience
in sociotechnical systems, and how they manifest on TikTok: 1) Machismo and
gender based discrimination, 2) Linguistic assimilation, 3) Intersectionality
of immigration and representation in sociotechnical systems, and 4)
Racialization in sociotechnical systems. We evaluate these tensions in the
context of social and cultural theory and affordances of sociotechnical
systems, particularly TikTok. From these tensions and affordances, we build
and present our theoretical contribution of algorithmic identity compression
in Section 5.2, and show how TikTok and similar sociotechncial systems result
in algorithmic identity compression for Latine diaspora members.

###  5.1. Tensions in content and sociotechnical systems for Latine diaspora

####  5.1.1. Machismo and gender-based discrimination

Machismo is a unique cultural manifestation of misogyny in the Latine
diaspora. Many marginalized groups experience misogyny differently given
interlocking identities and forces. An example of this is misogynoir, coined
by Moya Bailey, to describe the unique intersection of misogyny and racism
that Black women face (Bailey, 2021), and has been built upon by Turner et al.
in understanding how it manifests in sociotechnical systems (Turner et al.,
2021). Machismo has been attributed to Spanish colonialism and studied at
great length by Chicanx scholars in how it impacts family dynamics and the
life experiences of Latinos (male Latine people) (Zinn, 1982). Recent
literature has shown that younger Latine people are moving toward less
stringent patriarchal roles and expectations, as this rigidity is linked to
negative social and physical health outcomes for Latinos (Valdez et al.,
2023). Our interviewees echoed this sentiment, expressing how they have seen
machismo content on and offline that harms Latinos, limiting their attitudes
about their potential career and educational paths, and in expressing their
emotions. Participants wanted to see alternative representations and for their
Latine cultures to stop claiming and reinforcing machismo.

  1. “…men felt like they couldn’t be emotional, because, you know, they have to provide, they can’t focus on anything else…we shouldn’t claim…machismo, as part of Chicano culture…we should change it.” - P19

  2. “But then it also can be negative, because I have seen like videos where it will be maybe one of the construction worker…and they’ll say stuff like, ‘No, I’m Mexican. I don’t go to university. I don’t go to college.’” - P11

Machismo enables a culture that is unsafe for Latinas and Latinx femmes
(Garcia, 1989). This is both by devaluing Latinas as inferior to men and
forcing the female gender role into Marianismo (APA, 2018), a gender role of
submissiveness and chastity. In the United States, Latinas must navigate a
double standard of this submissive role with over sexualization thrust upon
them from media tropes such as “Spicy Latinas” (Lopez, 2021). The “Spicy
Latina”, an archetype that stresses the exotic sexuality of Latinas, often
with a ‘rough’ background and confrontational personality, and has been linked
to stereotype threats of Latina women in modern media and in real life (Rachel
Godsil and MacFarlane, 2016; Merskin, 2007). This stereotype has evolved into
sexist online memes like the “Hot Cheeto Girl”, (Froio, 2023), creating
content that devalues Latinas as unintelligent, inferior, and disruptive.
These ”Hot Cheeto Girl” skits appeared in our study, as discussed in 4.4.1,
with participants finding them sexist and problematic.

These skit videos are not only harmful in content, but also in scale, as they
which have great potential to go viral. A brief search of hashtags of
“#HotCheetoGirl” and “#LatinasBeLike” shows these hashtags are massive and
linked to several trending audios (has, 2023a). TikTok presents a formulaic
platform where this content can quickly be reposted, stitched, and recreated
as it gets popular, reifying existing stereotypes at massive scales.

This devaluation and stereotype threat have real-life consequences. In the
United States, Latina and Black women are more likely to experience violent
crimes than white women, with Latinas making up 37% of missing women and 42%
of homicides in 2021 and 2022 (lac, 2023). Latinas also have less economic
mobility, making only 55 cents on every dollar that a white man makes
(California, [n. d.]), often being stereotyped into difficult and low-earning
labor associated with their gender roles.

Participants felt that other video content was very sexualized towards
Latinas. While there was no sexually explicit content in our study, there was
content with sexual undertones. A key media element was a video about “Spicy
Latinas” wearing sexy pajamas to bed for their white American boyfriends,
which many interviewees connected to the underlying sexism of the ”Hot Cheeto
Girl” and explicitly felt frustration and discomfort regarding this video.

  1. “That’s a Latina stereotype…I think that was very pervy.” - P8

These trends feel like transgressions, as many participants associate feminine
beauty and sexuality with positive cultural aspects, like dance and dress.
Brazilian Carnaval is an epitome of this, with many scholars and activists
citing it as an example of reclaiming sexuality and gender expression for both
femme and masc presenting Latine peoples (car, 2013; bra, 2023).

  1. “She looks very beautiful. She is sexy…it’s a cultural stuff. And anyway, and you watch this person you’re definitely going to, you know, feel more, close to culture because of the dance…” - P7

  2. “She looks amazing, beautiful! I can say she represents all the girls… because I see some dance there I can see some of the beauty there. I can see the Vibe” - P3 (translated from Spanish)

Participants felt that social media and systems reinforce the negative
stereotypes and impose hypersexuality on Latinas without consent. On TikTok,
participants felt this manifested in popular videos they had seen on the app.
Indeed, across TikTok and other social media platforms, results for #Latina
are full of sexually explicit videos that are near the very top of the search
page (Noble, 2018). This type of harm against women of color has been studied
at length by scholars such as Safiya Noble investigating search algorithms
proliferating sexualized and explicit images of Black women (Noble, 2018). A
study by Ghosh and Caliskan confirmed this bias and over-sexualization of
women of color, particularly citing Latina examples, has proliferated from
online spaces to the machine learning models behind text-to-image generators
(Ghosh and Caliskan, 2023).

####  5.1.2. Assimilation

Participants discussed a feeling of assimilation and loss of culture, both
from the colonial loss of Indigenous languages from Iberian colonization but
also from having to assimilate to English upon moving to the United States.
The loss of Indigenous languages in Latin America is stark; the World Bank
estimates 26% of indigenous languages are at risk of disappearing (Wor, 2019).
This is a result of a long history of colonialism that consistently
marginalized, discriminated against, and harmed Indigenous communities
throughout the Americas (Deal and Hartt, [n. d.]). This has resulted in
Indigenous families having to learn colonial languages and even hide their
Indigenous heritage due to discrimination (Deal and Hartt, [n. d.]). By the
time these families moved to the United States, their Indigenous knowledge was
gone and many participants in our study were unsure of the details of their
Indigeneity and had lost the language entirely.

  1. “I know my dad’s side comes from Indigenous background. But they don’t really know, where or how.” - P15

  2. “My grandma, she speaks it [Indigenous language] fluently, and I think Spanish is actually her second language. My dad…can speak a bit. But…they [other people] would treat you lower…If you had any resemblance to like anything Indigenous…back when I was growing up there, it was pretty bad.” - P16

Some participants report reclaiming this Indigenous identity and having
important learning experiences on TikTok as described in 4.1.1 where
participants feel that algorithmically curated Indigenous content brings them
joy and that TikTok is a safe and affirming space for them to consume this
content. Some participants have also used hashtags to learn about Indigenous
identities, but while TikTok supports Spanish language codecs, there is no
sense of Indigenous language in TikTok or video codecs for many other
platforms (Tik, 2024), showing further language loss even as the World Bank
and other institutes make efforts to preserve these languages (Wor, 2019).

Along with Indigenous language loss, participants often discussed Spanish
language loss from their family’s assimilation in United States. Participants
spoke of how their parents did not teach them Spanish, prioritizing English,
often to avoid discrimination. Some families saw a loss of their indigenous
language and Spanish within only two generations. This assimilative experience
left some participants discontent with TikTok’s association of Latinidad with
Spanish language videos, but finding community in the “No Sabo Kid” trend
discussed in 4.1.2.

####  5.1.3. Immigration tensions and privileges

Another tension within the Latine diaspora that manifests within TikTok and
other sociotechncial systems is that of immigration status. In 3.2.1 we define
the 4 types of immigration experiences our participants had, with 8/19 being
immigrants themselves, including 2 who had experienced being undocumented. The
rest of our study were US-born children of adult immigrants (6/19), as are 50%
of Latinx US-born people (His, 2022) or US-born Latine children to US-born
parents (7/19), having families that have been in the US for generations, with
regional identities like Chicano and Tejano that serve as foundations of
Latinx culture in those areas (Tatum, 2017).

Both groups experience fluidity and tensions around their immigrant or
citizenship identities, but in different ways and systems. For US-born Latine
participants, much of this tension came from experiences in college education.
Meanwhile, immigrant participants discussed the labor, precarity, and
isolation of navigating the immigration system.

44% of Latinx students are the first in their family to attend university
(Flores, 2021), where many realized they were part of a minority in a way they
were not in their home community. They also learned that within the Latine
community, there were wider socioeconomic realities, echoing back to their
frustration with media and platform representation painting Latine people as
only lower income as seen in 4.4. This countered their experience of meeting
adult Latine immigrants who were more economically privileged and did not grow
up within the disproportionate racial and economic discrimination experienced
by the US Latinx diaspora (Holzer et al., 2023). This manifests within higher
education with the underrepresentation of US-born Latinx professors, who make
up less than 10% of Latinx engineering professorships in the United States
(Arellano et al., 2018), while adult Latinx immigrants comprise the other 90%,
adding a particular tension within these groups in many other systems, such as
who goes on to achieve influence in various STEM sectors. This was a source of
frustration for many participants, who described these tense encounters during
their university experiences.

  1. ”…[people would] keep saying: ’Oh, you wouldn’t understand. You’re not like an immigrant.’ I was like: ’Well, also, you don’t understand being like a poor brown person in America.’” - P6

However, many US-born Latine people, including our participants, understand
the privilege that comes with US citizenship and the precarity of the
immigration experience, particularly in being undocumented. An estimated 35%
of the Latine diaspora in the US are undocumented (Millet and Pavilon, 2022),
and 1 in 4 US-born Latine children have a parent who is undocumented (His,
2022). Over 500,000 Millennials and Generation Z are DACA (Deferred Action for
Childhood Arrivals) recipients, individuals who were undocumented as children
but can attain specific work, educational, and citizenship possibilities (mig,
2023). Despite this, there is a large stigma around DACA and immigration
status in the US and Latine diaspora. Many immigrant participants discussed
the fluidity of this, the challenges of navigating the US immigration system
throughout their lives. A particular experience was when achieving more
privilege and security, through measures like greencards, many expressed grief
and guilt in losing immigrant experience communities and camaraderie.

  1. “But once I got my green card, I felt like I wasn’t able to go anymore. And I didn’t tell them but I just stopped going [to an undocumented support group].” - P1

####  5.1.4. Racial tensions and privileges

There is a vast range of intersectional identities within the Latine diaspora.
Participants honed in on this during their Diamond Ranking exercises,
reflecting on how different individuals within the diaspora were treated both
in and outside of the group, particularly based on race and anti-Black
sentiments.

Whiteness has unique underpinnings in the Latine diaspora, deriving from
colonial histories like the castas system that placed white colonizers at
higher social strata than Indigenous and African descended peoples. This
resulted in practices such as blanqueamiento by which families strategically
tried to marry into whiter ones to advance their positionality (Rahier, 1999;
Deal and Hartt, [n. d.]). While the casta system is no longer de jure,
colorism and anti-Black discrimination remains in Latin America and the US
(Charles, 2021). This results in unique discrimination against members of the
Latine diaspora, especially those of African descent or with darker skin (Noe-
Bustamante et al., 2021). This colorism is reaffirmed in media, which affects
other systems such as image-to-text generators, which favor lighter skin tones
and oversexualizes or demeans Latine people of color (Ghosh and Caliskan,
2023).

While participants regularly noted they were aware that anyone could be
Latine, both their lived experiences and Diamond Ranking exercises echoed
existing biases towards Afrolatinx and Indigenous people, as discussed in 4.3.
These biases contribute to an exclusive model of what a Latine person looks
like – associating Latinidad with lighter-skinned people. This reifies and
adds to representation power within media consumption, as most users consume a
never-ending stream of this content and engage with it. This can have
repercussions within the creator economy. If Afrolatine users are not
considered representative by many Latine users, algorithms may suppress this
content if people they classify as Latine engage with it less. This cycle
continues and reifies who is not shown as Latine online.

Many Latine people identify as white racially due to mixed ancestry or being
white-passing. All participants who who identified as white or white-passing
(7/19) in our study acknowledged their whiteness, or proximity to whiteness,
grants them privilege as they navigate through society, but they did not want
to distance themselves from their Latine identities. Additionally, whiteness
was not seen as the most representative of Latinidad by participants in the
ranking exercises. But while many participants do not conceptualize themselves
as white nor Latinidad as white, they often default to marking “White” in
various forms and databases because of Latine not being a racial category and
because of the loss of their Indigeneity, not wanting to fill out forms as
Indigenous.

  1. ”When I still fill out the forms, I just put like, white just because that’s what I’ve been told to do.” - P1

  2. ”I feel a lot of people within my culture either put like Native American or white, depending on how much they identify with their Indigenous identity.” - P5

Latine people are no one race, having gone through multiple waves of
colonization by European settlers and interactions with the transatlantic
slave trade (Deal and Hartt, [n. d.]; Rahier, 1999; Morales, 2019). Latine
people being encoded as ”white” in sociotechnical systems is an emergent
property that ignores this complex history and can reify exclusionary
practices. This can create unintended consequences in systems, ranging from
the type media recommended on platforms like TikTok, but also in ignoring the
racialized experience of many Latine people in important databases used to
make real-world decisions.

###  5.2. Algorithmic identity compression

These experiences of our participants affirm the Latine diaspora is not
monolithic and that affordances of systems like TikTok do not encompass the
multifacted realities of this group. Subsequently, the Latine diaspora is
simplified to unrepresentative categories and associations that can cause harm
through reification of biases, problematic content, and erasure of
intersecting identities in data systems. These harms must be reckoned with by
the designers of these systems

To account for this phenomena, how it arises, and its ramifications in
sociotechnical systems, we propose the concept of algorithmic identity
compression. We define this as the simplification, flattening, and conflation
of intersectional identities to a larger grouping due to the affordances of an
algorithmic system and existing societal biases at the cost of losing critical
cultural community features that are deemed unnecessary by these systems and
designers of them. We argue that Latine users experience algorithmic identity
compression within TikTok and other sociotechnical systems, such as
aforementioned biasing of AI systems against Latinas or defaulting to
whiteness in racial data categories.

This theory builds upon concepts such as Simpson’s algorithmic exclusion on
TikTok for LGBT+ users (Simpson and Semaan, 2021), Donna Haraway’s concept of
informatics of domination (Bell, 2020), Walker and DeVito’s identity
flattening concept, and critical scholars’ work on identity classification
limitations and instabilities in database and information systems (Scheuerman
et al., 2020; Bivens and Haimson, 2016; Dym et al., 2019; Keyes, 2018). These
aforementioned works explore how sociotechnical systems reify Western and
hegemonic binary categories and do not make space for non-normative or
marginalized identities, and enact harm onto these individuals, even in their
own spaces. We add to this work with algorithmic identity compression in that
this compression is imposed by interlocking choices in systems and how
features are compressed into monoliths with the loss of critical cultural
data.

We purposefully use the metaphor of compression informed by how image
compression shrinks files within an ‘acceptable’ threshold (Gonzalez and
Woods, 2019). We argue that sociotechnical systems are setting this threshold
very low for marginalized experiences. Furthermore, image compression also
reduces file size to make the content faster to deliver (Gonzalez and Woods,
2019). In a sociotechnical system like TikTok where more content being
recommended and engaged with provides more revenue, this speed and shrinkage
are ingrained as an acceptable tradeoff for important metadata, such as not
moderating explicit imagery associated with #Latina. But why do more complex
identities need to be shrunk for faster mass content delivery? And what
essential societal information is lost with this compression? And given the
paucity of Latines in technical positions (Vergara, 2023; Arellano et al.,
2018; Per, 2023), who gets to set this threshold of algorithmic identity
compression through the system?

TikTok enacts algorithmic identity compression of Latinidad through 3 key
affordances: (1) Linguistic complexities via singleton video codecs, (2)
ethnic and gender identities through hashtag conflations, and (3) dominant
negative stereotypical narratives through trending videos and audio. These
tensions are summarized in Table 6 in the Appendix.

In most digital platforms, including TikTok, videos are labeled, or encoded,
in only one language (Tik, 2024), with some languages, like Indigenous ones,
excluded entirely. This singularity leaves little to no room for the
multilingual videos that are so common in the Latine diaspora, such as English
videos that feature Spanish phrases, erasing this multilingualism and
associated labor. Any linguistic nuance is compressed to one language in the
video codec, most likely based on a classification of the audio which may not
be able to identify less served languages such as Indigenous languages,
leading to them not being codified, algorithmically suppressed, and not
reaching their audiences, obscuring content around Indigeneity. By extension
videos codified in one language are recommended by algorithms to individuals
associated with that language – regardless of their personal comfort or
association.

Hashtags are another mechanic of algorithmic identity compression for
Latinidad on TikTok, particularly for ethnic identities. As hashtags become
more popular, more videos are grouped under them. Additionally, on a platform
like TikTok, we see many, many hashtags being used on videos, such as
“#Latino” followed by #Mexico, #Cuba, #PuertoRico, #Bolivia, etc. – making
sure to capture many sub-identity hashtags with the main #Latino (has, 2023b).
An example of this is visible in Figure 2. TikTok’s hashtag structure obscures
individual Latine identities into the greater umbrella of “Latino” or “Latinx”
through how hashtags and the FYP algorithm are structured. This reinforces
biases around Latinidad being a monolithic experience and aims content at as
large of an audience as possible, with no embedded knowledge or acknowledgment
of the group. This can directly cause negative interruptions due to malicious
actors realizing they can hijack these large community hashtags for negative
or violent imagery.

Figure 2. Example of hashtag accumulation under common #latino videos
featuring a variety of ethnicities for more engagement.

The bottom of a TikTok video showing #latino conflated with other hashtags of
Latin American countries.

We illustrated the effects of compression of “Latina” identities with
sexualized images in 5.1.1, which is an example of lossy compression, likened
to lossy image compression where images cannot be restored. The association of
hypersexuality and Latinas has become so ubiquitous across many sociotechnical
systems and representations that while it is enabled by platforms via
mechanics like hashtags, it is a manifestation of offline biases, including
creators and audiences.

Influencers are pressured to chase trends and reach as many views as possible,
often by recreating these popular negative narratives. These narratives,
normalized via societal biases and so easily shown via platform affordances,
are often accepted and engaged with. Thus, Latine influencers continue to
uphold these hegemonic narratives about and within their own communities to
build audiences, often including non-community member audiences. In this
structure, non-Latine influencers are also incentivized to appropriate and
uphold these trends.

No sociotechncal system will ever capture the nuances of every lived
experience within a diverse group, but we desire systems to be critical in
who, and by what metrics, makes compression decisions. There are some
pragmatic improvements for platforms, such as increasing moderation of
explicit sexual content of Latinas or introducing more language codecs and one
to many database relationships to foster multilingualism and Indigenous
language promotion. We hope this scholarship around algorithmic identity
compression will be a useful tool to design such improvements and in examining
this phenomena for other pluralist groups in other sociotechnical systems.

##  6\. Limitations and future work

Like any qualitative empirical study, no sample can be or is meant to be
generalizable across a diverse group like the Latine diaspora. Our study
sample was limited in age range, ethnicities, political viewpoints, and racial
diversity.

The age range of our sample, while in line with over half of TikTok users
being 18-34 (Winter, 2023), excluded perspectives of older Latinx persons.
This impacted findings, such as reflecting on more recent educational
experiences and on how the social concept of race has changed, not
representing the mixed-race tension of the second author’s GenX experience.

Interviewees represent 7 countries and 9 other Latine identities, with a
majority Mexican representation (11/19) and only 3 interviewees identifying as
Afrolatinx. We suspect political viewpoints and snowball sampling contributed
to selection bias, as evinced by so many of the interviewees’ awareness of
white privilege and intersectionality. While we offered Spanish interviews,
most participants chose English, which may have biased findings such as
multilingualism as in 4.2.3. Additionally, offering the study in more
languages, like Portuguese, could have brought more perspectives.

Also, as we specifically recruited people who interact with TikTok regularly
and live in the US, another selection bias was introduced, as participants
with positive experiences with TikTok would be more likely to self-select for
this study, perhaps leading to the overall positive view interviewees had of
their FYP feeds.

Future work could target recruitment to include more diverse perspectives,
such as more nationalities, racial identities, political viewpoints, and age
ranges. Future studies focused on Latine individuals that faced harm on TikTok
may reveal insights and address some of the selection bias this study could
have faced. Additionally, these methodologies and the application of
algorithmic identity compression to other identity groups could strengthen
this theoretical framework.

##  7\. Conclusion

This study explored for the first time how members of the US Latine diaspora
experience identity formation on TikTok through sociotechnical and media
representation lenses. Our findings show that the Latine experience is unique
and worthy of further study, and while users curate joyful feeds on TikTok,
its algorithmic affordances create tensions through content and affordances
that reify existing biases.

Participants desire to see quality, nuanced, and diverse representation of
Latine people online and via TikTok. This brings them joy. It is in online
spaces that they are currently seeing this representation, but they are being
forced to accept it at a cost of transgressions such as explicit content
associated with their identities and violent news stories that intrude upon
their curated feeds. Participants expressed a common desire to dismantle the
existing negative stereotypes this type of recommended media reifies and bring
nuance and authenticity to their lived experiences online. We describe this
reification and lack of nuance as algorithmic identity compression.

We found algorithmic identity compression of the Latine diaspora is
specifically enacted by TikTok through 3 key affordances: (1) Singleton video
codecs, (2) hashtag conflations, and (3) trending videos and audio (see 5.2
and Table 6).

As algorithmic systems advance in power and reach, it is imperative that the
nuances of identities of marginalized populations are captured and that the
negative stereotypes around them are moderated. This work reveals how
interconnected these negative stereotypes are to not just media
representation, but other informatic data categorizations and sociotechnical
systems and how they have acute and real-world impacts on Latinx individuals.

##  8\. Acknowledgments

We are sincerely grateful to our participants, without whom this work would
not exist. We are grateful to our department, for supporting this work via the
[Anonymous University] [Anonymous Department]’s doctoral research grant.

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##  Appendix A Tables and Figures

Table 2. Frequencies of closed codes of categories on surveys Content Category |  Participants |  Percentage
---|---|---
Content about Latinx culture - music, visual art, dancing, book, movie, or other media reviews, food, sports, traditions, and other cultural displays |  52/59 |  88.1%
Creative content around Latinx - skits, POVs, and other freeform creative content surrounding one of the above categories or with Latinx coded characters in fantasy worlds |  43/59 |  72.9%
Content about Latinx identity and lived experience - adoption from Latin America, Latinx families, Latinx cultural exchanges, Latinx identity, Latinx stereotypes, etc |  40/59 |  67.8%
Content about Spanish language, but not specific to Spain - Spanish language instruction, content about bilingualism and Spanish, etc |  36/59 |  61.0%
Content about Latinx locations - Latinx neighborhoods in the US, Latin American countries, Latin American points of interest (i.e. monuments, memorials, etc) |  33/59 |  55.9%
Content about Latinx historical or current events - politics, natural disasters, historical analyses, current events (i.e. migrations), laws, etc |  32/59 |  54.2%
Table 3. Open and closed codes of categories Open emergent code |  Related closed code from survey |  Interviewees
---|---|---
Sports |  Content about Latinx culture |  2/19
First generation college content |  Content about Latinx identity and lived experience |  3/19
Indigenous content |  Content about Latinx historical or current events, Content about Latinx identity and lived experience |  4/19
News/political events |  Content about Latinx historical or current events |  6/19
Fashion |  Content about Latinx culture |  7/19
Food |  Content about Latinx culture |  9/19
Spanish language content |  Content about Spanish language |  9/19
Informational Latinidad content |  Content about Latinx identity and lived experience |  10/19
Acting out/POVs/Skits |  Creative content around Latinx |  11/19
Comedy/Memes |  Creative content around Latinx |  11/19
Day in the life/story times |  Content about Latinx identity and lived experience |  13/19
Music and Dance |  Content about Latinx culture |  14/19
Table 4. Grouping of voluntary and involuntary user factors that interviewees hypothesize lead to Latinx content being on FYPs User factor |  Voluntary or Involuntary factor |  Interviewees
---|---|---
Uses reposting, stitching, and other creator tools |  Voluntary |  4/19
Posts own content about Latinx experiences |  Voluntary |  7/19
Uses hashtags to look up content |  Voluntary |  8/19
Uses search bar to look up Latinx content |  Voluntary |  9/19
Intentionally follows Latinx creators’ |  Voluntary |  19/19
Intentionally likes Latinx content |  Voluntary |  19/19
Location of device and self |  Involuntary |  7/19
TikTok eavesdropping on them |  Involuntary |  9/19
Having a Hispanic name |  Involuntary |  4/19
Friends sending them Latinx content on the TikTok app |  Involuntary |  11/19
Table 5. Breakdown of thematic strategies for image organization by participants  Method of organization |  Interviewees
---|---
Visual facial features/hair |  16/19
Association of family members/friends |  14/19
Used apparel/accessories |  14/19
Used perceived role of people |  12/19
Stereotypes/media |  12/19
Used context of background of images |  9/19
Known influencer/figures |  5/19
Table 6. Compression affordances and examples on TikTok Compression Effect |  TikTok Affordance |  Example
---|---|---
Linguistic identity compression |  Singleton video codecs |  A video in Spanish and English only being codified in English and being shown to any Latine assigned user
Ethnic and gender identity hashtag conflations |  Having large amounts of hashtags possible on videos |  A video featuring #latino and then hashtags of over 10 Latin American countries, but the video only pertains to Mexican Americans
Dominant, negative steoretypical narratives |  Trending videos and hashtags that are pushed to more FYPs |  The #HotCheetoGirl is a very popular hashtag that keeps getting recommended and reposted in this media ecosystem.
Figure 3. Example of a Diamond ranking exercise used in the interviews
described in 3.3. In this activity, participants were shown photos of creators
(blurred here for privacy) and were asked to drag them into boxes based on
most to least representative based on their conceptualization of their Latine
identity. This activity served as a conversation probe during the interview.