# Examining the Unique Online Risk Experiences and Mental Health Outcomes of
LGBTQ+ versus Heterosexual Youth

Tangila Islam Tanni<EMAIL_ADDRESS>0000-0002-6737-7377 University
of Central FloridaOrlandoFlorida32826USA ,  Mamtaj Akter
<EMAIL_ADDRESS>0000-0002-5692-9252 Vanderbilt
UniversityNashvilleTennessee37212USA ,  Joshua Anderson<EMAIL_ADDRESS>0009-0007-0238-0165 University of Central FloridaOrlandoFlorida32826USA ,
Mary J. Amon<EMAIL_ADDRESS>0000-0003-0026-7568 University of Central
FloridaOrlandoFlorida32826USA and  Pamela J. Wisniewski
<EMAIL_ADDRESS>0000-0002-6223-1029 Vanderbilt
UniversityNashvilleTennessee37212USA

(2024)

###### Abstract.

We collected and analyzed Instagram direct messages (DMs) from 173 youth aged
13–21 (including 86 LGBTQ+ youth). We examined youth’s risk-flagged social
media trace data with their self-reported mental health outcomes to examine
how the differing online experiences of LGBTQ+ youth compare with their
heterosexual counterparts. We found that LGBTQ+ youth experienced
significantly more high-risk online interactions compared to heterosexual
youth. LGBTQ+ youth reported overall poorer mental health, with online
harassment specifically amplifying Self-Harm and Injury. LGBTQ+ youth’s mental
well-being linked positively to sexual messages, unlike heterosexual youth.
Qualitatively, we found that most of the risk-flagged messages of LGBTQ+ youth
were sexually motivated; however, a silver lining was that they sought support
for their sexual identity from peers on the platform. The study highlights the
importance of tailored online safety and inclusive design for LGBTQ+ youth,
with implications for CHI community advancements in fostering a supportive
online environments.

Content Warning: This research discusses sensitive topics, including explicit
sexual content, abusive language, and homophobic slurs, which may cause
discomfort. Reader discretion is advised.

LGBTQ; Online Risks; Youth; Social Media; Self-Harm; Instagram; Mental Health;
Online Safefy; Sexual Risk

††journalyear: 2024††copyright: acmlicensed††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642509††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Human computer interaction (HCI)††ccs: Empirical studies in
HCI

##  1\. Introduction

Social media platforms like Instagram, TikTok, and Snapchat are popular among
teens, especially those who feel isolated or marginalized, such as youth from
the lesbian, gay, bisexual, transgender, queer or questioning communities
(LGBTQ+) (Academy, 2021; Foundation, 2021). As of 2020, an estimated 9.5% of
the youth population in the United States is comprised of LGBTQ+ youth (ages
13-17) (Conron, 2020), which represents a growing demographic of youth with
unique needs related to their sexual and gender identities (Steinke et al.,
2017). Consequently, many LGBTQ+ youth turn to social media platforms as a
means to seek emotional and social support, foster friendships, and find
validation (McInroy and Craig, 2020). Community engagement within the LGBTQ+
community can aid these youth in their identity exploration and foster
confidence in themselves and their sexuality (McInroy et al., 2019; Craig et
al., 2021). However, other researchers have highlighted the negative effects
of online engagement for this vulnerable community, as LGBTQ+ youth also face
unique risks when it comes to navigating online spaces (Abreu and Kenny, 2018;
Schneider et al., 2012; Zych et al., 2016; Hatchel et al., 2021). For
instance, research has shown that they are disproportionately a target of
harassment and other forms of cyberbullying, compared to their heterosexual
counterparts (Abreu and Kenny, 2018). These negative experiences have been
shown to cause mental health issues for LGBTQ+ youth and, at the extreme, may
even lead to suicidal tendencies or self-harm (Lewis et al., 2011).

Recently, social media interactions have been trending towards private message
communication instead of public postings due to several reasons, including
privacy concerns (Domanski, 2019; Hu, 2020; Zilles, 2019), to escape public
scrutiny, and to reduce data visibility (Sanders, 2021). Private forms of
communication have unique benefits (e.g., social support, advice from peers,
etc.) but may also subject users to new harms (e.g., targeted harassment, hate
speech, sexual grooming). Private conversations allow increased anonymity,
sometimes facilitating inappropriate or uncharacteristic behaviors online
(Zimmerman and Ybarra, 2016). For instance, prior research suggests that most
of the abuse on social media platforms comes in the form of Direct Messages
(DMs) from strangers (Khalid, 2019). As DMs are considered private
conversations, the majority of social media platforms do not address hate
speech or bullying experienced through these channels (Instagram, 2021).
Therefore, these private chats represent a potentially unique threat yet to be
examined, particularly in the context of LGBTQ+ youth in comparison with
heterosexual youth. Yet, a common theme among existing research studies is
that they have historically relied heavily on youth self-reports (Almeida et
al., 2009) or publicly scraped social media data (Saha et al., 2019; Rieger et
al., 2021), which constrains what we know about LGBTQ+ youth’s experiences in
private spaces on social media. Therefore, to better understand the online
risks encountered by LGBTQ+ youth in private social media spaces and how this
may influence their overall mental health, the current study poses the
following high-level research questions:

  * •

RQ1: Do LGBTQ+ youth experience more frequent and/or severe online risks in
their private social media conversations compared to heterosexual youth?

  * •

RQ2: Do LGBTQ+ youth who experience negative online experiences within their
private conversations have more mental health challenges compared to
heterosexual youth?

  * •

RQ3: What is the unique nature of the online risks LGBTQ+ youth experience in
private online chats?

To answer our research questions, we conducted a user study with 173 youth
(ages 13-21), including 86 LGBTQ+ participants. We measured several pre-
validated constructs regarding the youths’ mental health and had them upload
and flag their Instagram DMs that they felt uncomfortable or unsafe about. To
answer RQ1, we first conducted between-group tests to evaluate LGBTQ+ youth
versus heterosexual youth’s self-reported online risk experiences and mental
health challenges. For RQ2, we also applied regression models to analyze the
relationship between online risk experiences and the mental health outcomes of
youth participants. Finally, for RQ3, we conducted a qualitative analysis of
the flagged messages for deeper insights into LGBTQ+ youth’s online risk
experiences.

Overall, we found that the private online experiences of LGBTQ+ youth differ
significantly from the experiences of heterosexual youth in several key ways.
For RQ1, we found that while LGBTQ+ youth do not report more online risk
experiences overall, the experiences that they report are of higher risk
severity and are more likely to involve messages that contain sexual or self-
injurious content. For RQ2, we confirmed findings from prior literature that
found LGBTQ+ youth report significantly worse mental health outcomes (e.g.,
Self-Harm and Injury, Depression, and decreased Mental Well-being) compared to
heterosexual youth. In addition, we uncovered two significant interaction
effects; LGBTQ+ youth who receiving more online harassment report
significantly more self-harm and self-injurious behavior, while this effect
was not seen among heterosexual youth. In contrast, LGBTQ+ youth who received
more sexually risky messages reported higher levels of mental well-being,
while this effect was in the opposite direction for heterosexual youth. Our
RQ3 qualitative analyses revealed additional insights into our quantitative
findings. The conversations flagged as risky by LGBTQ+ youth often contained
sexual undertones, even when the intent was to harass. However, some of these
risky messages gave LGTBQ+ youth the opportunity to explore their sexual and
gender identities. Importantly, Instagram DMs gave them a channel to share
about their struggles with others who provided needed support. Our results
provide useful insights for supporting the unique online experiences of LGBTQ+
youth, which we unpack in our discussion.

Our research makes a distinct contribution to the CHI community, particularly
in the context of inclusive design and research advocating for LGBTQ+
populations. Building upon the LGBTQ+ advocacy research within the CHI
community (c.f., (Acena and Freeman, 2021; Gatehouse et al., 2018; Carrasco
and Kerne, 2018)), we advance this body of knowledge by delving into youths’
private social media experiences through the use of their Instagram Direct
Messages. Focusing on this timely subject, our research furthers
understandings of the challenges faced by a particularly vulnerable
demographic, LGBTQ+ youth, in the realm of online threats and bullying
experiences but also critically evaluates the efficacy of existing filtering
tools and protective measures for vulnerable users in the face of
cyberbullying, urging a call-to-action for risk prevention and mitigation
program. The broader implications of the research extend to the advancement of
Human-Centric Machine Learning (HCML) algorithms designed to detect and
address online sexual risks, thus contributing significantly to the ongoing
efforts to enhance LGBTQ+ youth’s digital safety and well-being. Specifically,
we make the following unique research contributions:

  * •

Through a mixed method study, we disentangled how LGBTQ+ and heterogeneous
youth’s online risk experiences differed, finding that LGBTQ+ youth report
higher risk conversations containing sexual and self-harm content.

  * •

Our research highlighted a range of online risks experienced by LGBTQ+ youth
and demonstrates how the multi-faceted risk factors go beyond online public
spaces and occur in private contexts.

  * •

We examined social media direct message data to underscore the association
between receiving harassing messages and the self-harming behaviors of LGBTQ+
youth.

  * •

We gained deeper understanding of how LGBTQ+ youth did not only experience
sexual harassment and unsolicited sexual content from strangers and peers,
they also sought support from their peers.

Content Warning: This study discusses delicate subjects such as sexual
content, offensive language, and derogatory remarks aimed at LGBTQ+
individuals, which might evoke unease. Caution is advised for readers.

##  2\. Related Work

In this section, we synthesize related work regarding LGBTQ+ youth and their
social media usage. Next, we introduce literature highlighting how LGBTQ+
youth are more vulnerable to online risks and how the negative online
experiences impact mental health.

###  2.1. LGBTQ+ Youth, Social Media, and Support

LGTBQ+ turn to online platforms to seek information related to gender,
sexuality, and romantic relationships, as well as to seek support for
physical, mental, and sexual health needs (Craig and McInroy, 2014; DeHaan et
al., 2013; Harper et al., 2009; Razi et al., 2020a; Lucero, 2017; Pyle et al.,
2021). Yet, LGBTQ+ youth reported having smaller online social networks
compared to heterosexual youth (Charmaraman et al., 2021). In contrast, they
reported joining online communities or groups more frequently to feel less
isolated or lessen social isolation compared to heterosexual youth
(Charmaraman et al., 2021). Furthermore, the likelihood of LGBTQ+ teenagers
having friends they only know online is higher as they often view online
friends as being more supportive than their in-person peers (Ybarra et al.,
2015).

Past research highlighted the role of online social networks as safe havens
for LGBTQ+ youth who live in hostile environments. They found that the
anonymity and lack of geographic restrictions in digital spaces offer an ideal
platform for coming out, engaging with a communal gay culture, experimenting
with non-heterosexual intimacy, and socializing with other LGBTQ+ youth
(Hillier and Harrison, 2007). Furthermore, LGBTQ+ youth also interact and
build connections with a specific community and engage themselves with content
that validates and recognizes their unique experiences with being an LGBTQ+
individual. However, seeking social support in online communities often
requires disclosing personally sensitive information, such as one’s gender
identity and personal struggles (Haimson et al., 2015; Hillier and Harrison,
2007). In the early phases of developing their LGBTQ+ identities, LGBTQ+
individuals may use social media platforms as informal learning environments
(Fox and Ralston, 2016). For example, previous research findings identified
three educational purposes associated with online information seeking on
social media: traditional learning about LGBTQ+-related issues, social
learning involving mirroring role models or other LGBTQ+ people’s behavior,
and experiential learning with online dating sites and dating apps. These
learning behaviours were common during the coming-out process (Fox and
Ralston, 2016).

Decisions of coming out are further complicated by the potential risks and
negative consequences of disclosing one’s LGBTQ+ identity in unsupportive
environments (DeVito et al., 2018; Haimson et al., 2015). Violations of
privacy (Geeng and Hiniker, 2021), being disowned by family members (Cho,
2018), and rejection from society are potential consequences of coming out.
Regardless of these fears, youth often choose to disclose their sexual
identity online to obtain needed support and social connections (Geeng and
Hiniker, 2021; Fox and Warber, 2015). Coming out about one’s sexual identity
is positively associated with relationship satisfaction, self-efficacy, self-
esteem, lower anxiety, and lower levels of depression (Mustanski et al., 2011;
Meyer, 2019; Andalibi, 2019). Thus, LGBTQ+ youth have multiple co-acting
motives for seeking online support and also experience a range of benefits and
risks within online spaces. However, most prior research in this area has
focused on how LGBTQ+ youth self-report on their experiences in reference to
online public spaces (e.g., via surveys, focus group studies, interviews, or
analyzing public social media data). Further research is required to
understand the online risks in private one-to-one communication, particularly
through the analysis of real-world direct messages.

###  2.2. LGBTQ+ Youth Online Risks and Well-being

Although the Internet provides LGBTQ+ youth numerous opportunities to form new
relationships and explore their sexual identities (Geeng and Hiniker, 2021;
Fox and Warber, 2015), it can pose a range of online risks, such as privacy
violations (Maurice, 2021; Space, 2017; News, 2021), cyberbullying (Ybarra et
al., 2015; Schneider et al., 2012; Li et al., 2020), sexual risks (O’Keeffe et
al., 2011), and other types of abuse (Powell et al., 2020). Social media
platforms provide social visibility, connectivity, feedback, and ease of
accessibility, which can increase cyberbullying compared to traditional
bullying (Fox and Moreland, 2015). In particular, LGBTQ+ youth experience
higher rates of cyberbullying ((Schneider et al., 2012; Ybarra et al., 2015))
and are often targeted due to their nonconforming sexual identities (Aoyama et
al., 2011; Abreu and Kenny, 2018). Moreover, a greater number of
polyvictimization incidents, where youth who experience one type of
victimization are more at risk of experiencing other types as well, are
prevalent among LGBTQ+ youth (Finkelhor et al., 2007, 2009; Turner et al.,
2010; Sterzing et al., 2017). LGBTQ+ youth report being bullied or harassed
online nearly three times as frequently as non-LGBTQ+ youth (42% vs. 15%)
(Greytak et al., 2013). Other forms of online risks, such as hate-based abuse,
violence, and discrimination are also prevalent among sexual minority youth
(Guasp et al., 2013; Hiller et al., 2005).

As a result, LGBTQ+ youth experience ”minority stress,” a chronic form of
stress caused by negative social experiences associated with identifying with
a minority group (Aoyama et al., 2011; Meyer, 2003; Saha et al., 2019), which
can be amplified in social media contexts (Saha et al., 2019). According to
the minority stress theory, sexual and gender minority health disparities may
be traced to stressors brought on by hostile, homophobic, and transphobic
cultures. These stressors frequently result in harassment, abuse, and
victimization and may ultimately affect access to care (Saha et al., 2019;
Denton et al., 2014; Frost et al., 2015; Hendricks and Testa, 2012). In
particular, stigma, prejudice, and discrimination create a hostile social
environment that can lead to mental health difficulties (Meyer, 1995). LGBTQ+
youth reported higher rates of suicidal thoughts and depressive symptoms than
their straight peers, which are influenced by negative experiences, including
discrimination and victimization (Marshal et al., 2011). These mental health
issues, which result from bullying and other online risk factors, can
significantly impact well-being and relationships with others (Keighley,
2022).

Whereas much of the current research is focused on young LGBTQ+ adults, more
research is needed to understand how younger LGBTQ+ adolescents (ages 13-17)
experience these online risk factors. This is especially true given that the
permissible age for having a social media account (e.g., for Facebook or
Instagram) is 13 (Holloway and Green, 2016; Montgomery et al., 2017), a time
during which LGBTQ+ youth may be particularly susceptible to online dangers
and associated mental health consequences. Moreover, the risk factors
associated with online engagement highlight the critical need to go beyond
just examining public online communities, but also understanding the multi-
faceted risk factors that occur in private contexts as well. As such, our
research complements and extends beyond previous work by examining the online
risk experiences and associated mental health outcomes of adolescent LGBTQ+
youth between the ages of 13 to 21. Our study adds depth by adopting a mixed-
method approach and pairing the self-reported mental health data of LGBTQ+
youth with their real-world social media private conversations that they
flagged as unsafe or risky. By triangulating these two data sources, our
analysis is one of the first to deeply examine the association between these
two facets of LGBTQ+ youths’ personal and social experiences, contributing to
a more holistic representation of LGBTQ+ youth’s online experiences.

##  3\. Methods

In this section, we provide a detailed description of our study, including the
survey design, Instagram data collection procedures, and the data analysis
approach we used to answer each of our research questions.

###  3.1. Study Overview

We conducted a user study of youth (ages 13-21), who first completed a web-
based survey, then were asked to upload their Instagram data and subsequently
flag their DM conversations for interactions that made them feel uncomfortable
or unsafe. We chose Instagram as our social media platform of choice because,
according to Pew Research, 72% of teens use Instagram, making it one of the
most popular social media platforms among youth (Anderson et al., 2018). We
recruited participants, who met the following eligibility criteria: 1) English
speakers based in the United States between the ages of 13-21, 2) Had an
active Instagram account at while they were a teen (ages 13-17), 3)
Communicated with at least 15 people through Instagram direct messaging (DMs),
and 4) Had at least two conversations with other users that made them feel
unsafe or uncomfortable. If participants met this eligibility criteria,
parental consent was obtained for those under 18; otherwise, participants
consented to participate themselves. Participants were compensated with a $50
Amazon gift card for their time and data. This study was approved by our
university’s Institutional Review Board (IRB).

###  3.2. Ethical Considerations

We took the utmost care to protect the participants’ anonymity and privacy due
to the complicated and sensitive nature of the data collected. First, we
obtained IRB approval for our work, declared our position as mandated child
abuse reporters within the statement of informed consent in the case of an
impending danger posed to a minor, and followed the guidance of Badillo-
Urquiola et al. (Badillo-Urquiola et al., 2021) on conducting risky research
with minors. For instance, we provided explicit instructions NOT to share
digital images that depicted the nudity of a minor and outlined our statutory
responsibility to report child pornography to the proper authorities. We also
provided clear directions to delete such materials prior to data sharing.
Additionally, we secured a Certificate of Confidentiality from the National
Institute of Health, which further protected participants by precluding the
subpoenaing of the data during legal discovery. To further protect the privacy
of our participants and everyone else who participated in direct message
conversations, we removed all personally identifiable information from any
textual data reported in our results and paraphrased all quotations. To
prevent sharing the data to outside parties, we also did not analyze our data
using any cloud-based services. Researchers were prohibited from downloading
the data onto personal devices and were required to complete IRB Human
Subjects CITI training. We also gave research assistants, who assisted in
verifying and qualitatively analyzing the data, mental health support, and
adequate breaks.

Table 1. Summary of participants demographics |  n (%)
---|---
Total (N) | 173 (100%)
Age (M (SD)) | 17 (2.14)
Gender Identity |
Female | 117 (67.63%)
Male | 39 (22.54%)
Non-binary | 14 (8.09%)
Prefer to self-identify | 3 (1.73%)
Race |
White/Caucasian | 92 (53.18%)
Black/African-American | 45 (26.01%)
Asian or Pacific Islander | 36 (20.81%)
Hispanic/Latino | 26 (15.02%)
American Indian/Alaska Native | 7 (4.04%)
Prefer to self-identify | 5 (2.89%)
Two or more races or ethnicity | 32 (18.49%)
Sexual Identity |
Heterosexual/straight | 87 (50.29%)
Bisexual | 47 (27.16%)
Homosexual/gay | 17 (9.82%)
Self-identify | 22 (12.71%)
Pansexual | 10 (45.45%)
Omnisexual | 2 (9.00%)
Asexual | 2 (9.00%)
Biromantic asexual | 1 (4.55%)
Neptunic | 1 (4.55%)
Panoromantic and demisexual | 1 (4.55%)
Fluid | 1 (4.55%)
Curious | 1 (4.55%)
I don’t know | 3 (13.64%)

###  3.3. Participant Recruitment and Demographics

A total of (N = 173) participants completed the study. In the survey,
participants were asked to report several demographic characteristics,
including their gender identity and sexual identity (e.g., Heterosexual or
straight, Homosexual or gay, Bisexual, or Prefer to self-identify). Details of
participant demographics based on age, gender identity, race, and sexual
identity can be found in Table 1. Since our participants could select multiple
races, the total percentages of all categories can be greater than 100%. We
grouped homosexual/gay, bisexual, and individuals who preferred to self-
identify as LGBTQ+ youth, as the purpose of the term ”LGBTQ+” is to be
inclusive of all same-gender attracted and trans people (Cherry, 2020). Among
all participants who completed the study, 50.29% (n = 87) of youth identified
as heterosexual, whereas 49.71% (n = 86) were LGBTQ+. Participants also
responded to the social media usage questions, which helped us better
understand their Instagram use. For almost half (49.71%) of our participants,
Instagram was their most used social media platform with 64.16% (n = 111)
participants having more than one Instagram account. 47.97% (n = 83) of the
participants used Instagram several times a day, 24.27% (n = 42) used every
day or almost every day, 21.96% (n = 38) used several times an hour, 4.04% (n
= 7) used once or twice a week, 1.15% (n = 2) used less than once a month, and
0.57% (n = 1) used less than once a week. 97.68% (n = 169) participants never
met their Instagram followers in person.

###  3.4. Data Collection and Risk-Flagging

After completing the web-based survey, participants uploaded their Instagram
data to a secure web-based system (Razi et al., 2022a). This web-based system
was developed using PHP with an Amazon Web Services (AWS) back-end
infrastructure to encrypt and store the data. Participants requested their
data from Instagram and uploaded it in the form of JSON files that were
translated and stored on our AWS server. Then, participant’s DMs were
presented through our web-based interface in reverse chronological order for
them to review and flag the uncomfortable or unsafe (i.e., “risky”) messages
of each conversation. Participants marked individual messages by risk type
(e.g., sexual content, harassment, spam, hate speech, violence, illicit
actions, and self-injury) and risk level (low, medium, high). While aligned
with Instagram’s reporting features (Facebook, 2022), participants could
denote their own risk type. We provided a benchmark for risk level: low for
discomfort without harm, medium for potential emotional or physical harm, and
high for actual harm. The dataset included 32,055 DM conversations with over 6
million (6,863,161) messages. Participants marked 2,515 conversations as
’risky,’ flagging a total of 3,023 messages by risk type and level. High-level
risks comprised 402 messages, while medium and low risks were 821 and 1,813,
respectively. Table 2 displays message counts based on risk types and
severity. Past studies (as synthesized in section 2.2) suggested that LGBTQ+
youths face bullying and harassment more on both offline and online, including
on social media platforms. Based on prior findings from the literature, we
hypothesized that:

H1: LGBTQ+ youth will report encountering more online risks than heterosexual
youth.

H2: LGBTQ+ youth will report encountering more high-risk online experiences
than heterosexual youth.

Given that the literature was not conclusive regarding the specific types of
online risks LGBTQ+ youth encountered compared to their heterosexual
counterparts, we examined these relationships in our results without
formalizing hypotheses based on risk types.

###  3.5. Mental Health and Well-being Measures

In the survey, participants were also asked to report on several pre-validated
constructs to assess their mental health and well-being. These survey
constructs included: The Short Warwick-Edinburgh Mental Well-Being Scale
(SWEMWBS) (Tennant et al., 2007), the Patient Health Questionnaire (PHQ)-9, a
commonly used measure for Depression (Kroenke et al., 2001), and the Inventory
of Statements about Self-injury (ISAS) (Klonsky and Glenn, 2009). These
constructs were measured on a 5-point Likert scale. To ensure alignment with
other survey measures, we modified the (PHQ)-9 scale from a 4 to a 5-point
scale. This adjustment was validated through factor analyses, incorporating
Horn’s parallel analysis in exploratory factor analysis ((Homrighausen, 2015))
and taking into account prior research on the 4-point scale (Vu et al., 2022).
The subsequent confirmatory factor analysis produced favorable fit indices,
surpassing the 0.9 threshold. Notably, both RMSEA and SRMR scores exceeded the
recommended cutoff of 0.05, confirming the validity of our refined 5-point
(PHQ)-9 (University of Miami and Development, 2020; Goldberg, 1992; Kline,
2023). We tested for internal consistency using Cronbach’s alpha and all
values were above the acceptable threshold (0.7) (Cho and Kim, 2015). Table 4
shows the scale reliability and statistical description of the mental health
constructs for both LGBTQ+ and heterosexual youth who participated in our
study. As discussed in the prior literature, many studies have found evidence
that LGBTQ+ youth showed greater depressive symptoms and, in some cases,
suicidal ideation as a result of the negative consequences of stigma,
prejudices, discrimination, and abusive attitudes held by the predominately
heterosexual society (Clarke et al., 2010; Yi et al., 2017; Stojisavljevic et
al., 2017). Generally, LGBTQ+ youth are found to have poor mental health
conditions due to the negative experiences faced in both offline and online
settings. Based on this literature, we formulated the following hypotheses
related to the well-being and mental health of LGBTQ+ versus hetersexual
youth:

H3: LGBTQ+ youth will report worse mental health outcomes, including a) lower
Well-Being, b) higher Depression, c) and higher Self-Harm and Injury than
heterosexual youth.

H4: Youth who experience more high-risk online experiences will report worse
mental health outcomes than those who report experiencing more low-level
online risk.

H5: The negative mental health outcomes associated with online risk
experiences will be moderated by sexual identity, such that the negative
effect will be stronger for LGBTQ+ youth than for heterosexual youth.

In the next section, we describe our statistical methods for testing our
research hypotheses, as well as our qualitative approach for gaining
additional insights.

###  3.6. Data Analysis Approach

In this section, we first describe how we conducted our statistical analyses
to answer our hypotheses related to RQ1 and RQ2. Then, we describe our
qualitative analysis approach for investigating RQ3.

####  3.6.1. Examining the Differing Online Risk Experiences of LGBTQ+ Versus
Heterosexual Youth (RQ1).

To investigate differences in online risk experiences encountered by LGBTQ+
and heterosexual youth, we first analyzed the risk-flagged messages provided
by youth. Among the total (n=3,023) messages, 48.56% (n=1468) were flagged by
LGBTQ+ youth, while 51.44% (n=1555) were flagged by heterosexual youth. Table
2 summarizes the number and percent of risk-flagged messages by risk type and
level for both LGBTQ+ and heterosexual youth. We conducted a between-group
chi-square test ($\chi^{2}$) to compare the difference in total number of
online risks experienced by these two youth groups (H1). The $\chi^{2}$ test
of independence is employed for between-group comparisons involving two or
more groups (McHugh, 2013), and previous studies contrasting sexual minority
groups with heterosexual individuals have successfully employed this approach
(Diemer et al., 2015).

Furthermore, we explored significant between-group differences between LGBTQ+
and heterosexual youth based on risk level to test H2. Specifically, we
utilized standardized residuals to provide insights into cells significantly
impacting the chi-square value. Cells with standardized residuals exceeding +2
are considered major contributors, while those surpassing -2 weakly contribute
to the overall chi-square calculation (Walker, 2023). We performed another
$\chi^{2}$ test to identify significant differences in the total number of
messages falling into each of the six risk types, as self-annotated by the
youth. Similar to the approach with standardized residuals described above, we
employed this analysis to gain insights into risk type disparities within the
dataset.

Table 2. Counts and percentage of risks based on risk type and severity for LGBTQ+ and heterosexual participants (N = 173) Risk Type |  LGBTQ+ (N = 86) |  Heterosexual (N = 87)
---|---|---
Low | Medium | High | Low | Medium | High
Sexual messages |  n = 286 |  n = 171 |  n = 141 |  n = 339 |  n = 151 |  n = 69
N = 1157 | 35.18% | 43.85% | 53.21% | 34.35% | 35.03% | 50.36%
Harassment |  n = 276 |  n = 101 |  n = 64 |  n = 327 |  n = 159 |  n = 36
N = 963 | 33.94% | 25.89% | 24.15% | 33.13% | 36.89% | 26.27%
Spam and others |  n = 158 |  n = 39 |  n = 9 |  n = 185 |  n = 52 |  n = 13
N = 456 | 19.43% | 10.00% | 3.40% | 18.74% | 12.06% | 9.49%
Hate Speech and violence |  n = 48 |  n = 47 |  n = 28 |  n = 68 |  n = 41 |  n = 12
N = 244 | 5.90% | 12.05% | 10.57% | 6.89% | 9.51% | 8.76%
Illicit actions |  n =30 |  n = 21 |  n = 13 |  n = 55 |  n = 21 |  n = 7
N = 147 | 3.69% | 5.38% | 4.90% | 5.57% | 4.87% | 5.10%
Self-injury |  n = 15 |  n = 11 |  n = 10 |  n = 13 |  n = 7 |  n = 0
N = 56 | 1.84% | 2.82% | 3.77% | 1.31% | 1.62% | 0.00%
Total |  N = 813 (100%) |  N = 390 (100%) |  N = 265 (100%) |  N = 987 (100%) |  N = 431 (100%) |  N = 137 (100%)

####  3.6.2. Exploring Associations Between Mental Health and Negative Online
Experiences (RQ2).

To operationalize the frequency and levels of risk for each participant, we
utilized a weighted model, with higher severity risks having a higher weight.
Our methodology involved multiplying the number of messages within each risk
type by the corresponding risk weight. This resulted in a comprehensive risk
score for each risk type and user, providing a nuanced representation of the
interplay between message volume and risk severity. Next, we averaged items
associated with the three mental health constructs and calculated the mental
health scores for each participant. After calculating their Self-Harm and
Injury, Depression, and Mental Well-Being scores for both LGBTQ+ and
heterosexual youth, we examined the distribution and variance of the two
groups for each construct. As the two populations were normally distributed,
and the variances for the constructs were not equal for our independent
populations, we performed Welch’s two-sample _t_ -test to compare the between-
group differences in their mental health scores (Lu and Yuan, 2010).

Next, we investigated the relationship between the risky messages received via
DMs and the mental health of these youth via multiple linear regressions, and
we also tested whether this relationship is modulated by the self-reported
sexual identity of these youth. We started by fitting our data into a linear
regression model with each youth’s risk type scores and sexual identity as
predictor variables and mental health constructs as our outcome variables. Six
risk-type scores (e.g., sexual message, harassment, spam and others, hate
speech and violence, illicit actions, and self-injury) were entered as
predictor variables. Three mental health scores (Depression, Self-Harm and
Injury, Mental Well-Being) were the outcome variables. Furthermore, the sexual
identity of these youth was used as an interaction term in each regression
model. Here, messages categorized as self-injury are not synonymous with the
mental health construct known as Self-Harm and Injury.

Table 3. Codebook for Risk-Flagged Conversations of LGBTQ+ Youth (RQ3)

Themes |  Subthemes (Codes) |  Illustrative Quotations
---|---|---
Most risky conversations had sexual undertones |  Exchanged sexual message content
(Sexual content from strangers; from peers; Sent to others; Added to porn groupchats by strangers) |  OP*: hurry up bitch
OP: u wasting my time
P*: [User ID] sent an attachment
OP: oh girl, u are a good submissive. I fucking love u

Received sexual solicitations
(Sexual solicitations from strangers; From peers; With monetary offers; Received persistent messages; Led to harassment) |  OP: Can I get my duck sucked
P: I don’t know you do I? And not by me

Got harassed for sexual identity
(Harassed for LGBTQ+ identity by peers; by strangers) |  OP: Faggots like you are in this world will take us in hell
P: leave me alone please

Received spam targeted to sexual identity
(Advertisements; Scams targeted to LGBTQ+) |  OP: Hey, i’m from [Organization name]! I hope you’re having a great January. We are looking for brand ambassadors and reps. Please send us a message at our main account [UserID] ASAP! Have a wonderful day

LGBTQ+ youth encountered general harassment and scams |  Got harassed
(Harassed by strangers; By peers) |  OP: Instagram User sent an attachment
OP: can we be friends
OP: Instagram User sent an attachment
OP: bitch talk with me

Received spam/scams
(Received general advertisements; scams; ads for illegal substances) |  OP: you won’t believe this, I just got sent a $1,000 GiftCardVisa yesterday and all I had to do was participate a little and they sent me it. I used it to pick up a new gaming computer Ive been eyeing LOL. I thought u would want one since your my follower. Hurry thought there is just a few left!! Go

LGBTQ+ youth received support from peers regarding negative experiences |  Shared negative experiences related to sexual identity
(Received support for negative experiences with trans-phobic people; Fear of coming out; Urge to self-harm) |  P: I am tired of my dad. He gives me pills to cure my homosexuality. Its as if a disease my parents want me to get well. At this point I don’t think I should be living anymore. OP: I am sorry your going through these. It suckz when family is liek them. Can you move out

Shared mental health concerns
(Received support for sad feelings; Depressive thoughts) |  P: I ve bn having break up after break up. this time her problem was my bisexuality. I feel like cuting, I tried so many times
OP: Honestly, she doesn’t deserve you and your such a great guy and any girl
would be lucky to have you

Received other support and advice
(Received advice for general negative experiences; Drug abuse) |  OP: Oh so u out for smoking blunt again?
P: a little
OP: u know u shouldn’t be smoking

*P: Participant; *OP: Other Person

####  3.6.3. Qualitative Analysis of Instagram Private Message Conversations
(RQ3).

To further explore the types of risks that LGBTQ+ youth faced in private
messaging contexts, we performed a thematic analysis (Braun and Clarke, 2012)
of the private message conversations risk-flagged by LGBTQ+ youth. Each
conversation represented a messaging thread where the participant and other
person/people messaged back and forth intermittently. Therefore, each
conversation could have either a single message or a series of messages that
were exchanged over a long period of time. There were a total 1,468 unsafe
messages (of 808 conversations) that were flagged by our LGBTQ+ youth
participants. However, we analyzed all messages (N = 216,332) of these 808
conversations. All messages were in textual format. When participants or their
conversation partners shared media, our data showed the message as ”Instagram
User sent an attachment.” or ”[Name] sent an attachment” instead of showing
the actual file. In such cases, we used contextual cues from the larger
conversation to interpret the risk.

To complete the qualitative analysis, the second author initially familiarized
themselves with the data by reading through the conversations and creating
initial codes, which were then discussed among all co-authors. Subsequently,
the second and last author worked together to iteratively establish consensus
and incorporate codes as they emerged. They jointly collaborated to
conceptually group the codes into cohesive subthemes and overarching themes.
Our thematic analysis, as presented in subsubsection 3.6.2, aimed to identify
key characteristics of the risk-flagged conversations LGBTQ+ youth had with
others. For each theme, the total count and percentages of codes can be
greater than the total number of conversations as we double-coded the
conversations for different risk types. For example, there were instances when
sexual message content, sexual solicitations, and bullying were all present in
the same conversations.

##  4\. Results

In the following sections, we highlight distinctions in the total number of
online risks and their severity between LGBTQ+ and heterosexual youth by
presenting the outcomes of chi-square tests. Subsequently, we delve into the
results of linear regression models, investigating the correlation between
online risk experiences and mental health outcomes for both groups. Lastly, we
provide qualitative insights to enhance the overall understanding of online
risks in private conversations among LGBTQ+ youth.

###  4.1. LGBTQ+ Youth at Higher Risk for Sexual and Self-Injury Messages
(RQ1)

####  4.1.1. Flagged Messages by Risk Level (H1 & H2).

We first tested the degree to which LGBTQ+ youth flagged riskier messages
compared to heterosexual youth. Overall, LGBTQ+ youth flagged (N = 1468)
messages, whereas heterosexual youth flagged (N = 1555) as risky. Our chi-
square test indicated significant variation in the total number of online
risks experienced by LGBTQ+ youth versus heterosexual youth, $\chi^{2}$ (2, N
= 3023) = 57.167, p ¡ .001. However, this effect was in the opposite direction
than hypothesized. Hence, the result did not support our first hypothesis (H1)
that LGBTQ+ youth would report more online risks than heterosexual youth. In
contrast, heterosexual youth reported significantly more online risks than
LGBTQ+ youth.

For H2, Figure 1 (a) reveals that LGBTQ+ youth experienced a significantly
higher frequency of high severity online risks. Moreover, a significant
difference exists between heterosexual and LGBTQ+ youth regarding the number
of low-level risk conversations, with heterosexual youth reporting more low-
level risk conversations. However, there was no significant difference in
medium-level risks based on sexual identity. Thus, these results support our
hypothesis (H2).

(a)

(b)

Figure 1. Results (Standardized residuals) from chi-square test showing the
between-group analysis of online risk messages encountered by LGBTQ+ and
heterosexual youth based on (a) risk level and (b) risk types (N = 3023). (∗)
indicates significant association. Note that green indicates a positive
association, while red indicates a negative one.

####  4.1.2. Flagged Messages by Risk Type.

We conducted an additional chi-square test to identify differences between
LGBTQ+ and heterosexual youth groups based on risk types. The chi-square test
revealed significant differences between the two groups concerning risk types,
$\chi^{2}$ (5, N = 3023) = 16.927, p = .004. Standardized residuals indicated
a significant difference between LGBTQ+ and heterosexual individuals,
particularly when examining messages containing sexual content, harassment,
and self-injury. As shown in Figure 1 (b), LGBTQ+ youth experienced a
significantly higher frequency of sexual messages and messages containing
self-injury language compared to their heterosexual counterparts. In contrast,
heterosexual youth reported a significantly higher number of harassment
messages than LGBTQ+ youth. In the next section, we examine the association
between online risks and mental health for youth.

###  4.2. Associations Between Online Risks and Mental Health (RQ2)

####  4.2.1. Investigating the Differences in Mental Health Outcomes based on
Sexual Identity (H3).

Next, we investigated whether there were significant differences in mental
health challenges reported by LGBTQ+ versus heterosexual youth. Our Welch’s
Two-Sample t-test compared between-group differences based on mental health
scores and revealed a statistically significant difference in self-reported
Depression, Self-Harm and Injury, and Mental Well-Being scores p ¡ .001, as
illustrated in Table 4. LGBTQ+ youth reported higher levels of depressive
thoughts, Self-Harm, and Injury behavior than heterosexual youth. LGBTQ+ youth
also scored significantly lower in Mental Well-Being than heterosexual youth.
Therefore, the results confirmed our hypothesis (H3) that LGBTQ+ individuals
report worsened mental health outcomes compared to heterosexual youth. These
results serve to confirm past studies reporting similar results (Dawson et
al., 2023; healthcare.utah.edu, 2021).

Table 4. T-test results comparing LGBTQ+ and heterosexual on mental health constructs Measure | Cronbach’s $\boldsymbol{\alpha}$ | Sexual Identity | Mean | SD | t | df | Std. CI | p
---|---|---|---|---|---|---|---|---
Depression | 0.918 | LGBTQ+ | 3.598 | 0.881 | -5.788 | 170.94 | -1.036 - -0.509 | ¡.001
|  | Heterosexual | 2.825 | 0.876 |  |  |  |
Self-Harm and Injury | 0.878 | LGBTQ+ | 1.858 | 0.773 | -4.908 | 142.47 | -0.676 - -0.287 | ¡.001
|  | Heterosexual | 1.375 | 0.484 |  |  |  |
Mental Well-Being | 0.897 | LGBTQ+ | 4.279 | 1.102 | 4.248 | 170.82 | 0.377 - 1.032 | ¡.001
|  | Heterosexual | 4.984 | 1.079 |  |  |  |
Table 5. Unstandardized estimates for linear regression models examining the
relationship between online risky messages (e.g., sexual messages, harassment,
and self-injury) and mental health constructs (e.g., Self-Harm and Injury,
Depression, and Mental Well-Being)

| Self-Harm and Injury (M1) | Depression (M2) | Mental Well-Being (M3)
---|---|---|---
Predictors | Estimates | Std. CI | p | Estimates | Std. CI | p | Estimates | Std. CI | p
(Intercept) | 1.284 | 1.100 - 1.467 | ¡0.00*** | 2.639 | 2.382 - 2.895 | ¡0.00*** | 5.175 | 4.858 - 5.492 | ¡0.00***
Sexual Messages | 0.010 | 0.000 - 0.020 | 0.048∗ | 0.014 | 0.000 - 0.028 | 0.048∗ | -0.020 | -0.037 - -0.002 | 0.026∗
Harassment | -0.000 | -0.008 - 0.007 | 0.865 | 0.002 | -0.009 - 0.013 | 0.717 | -0.001 | -0.015 - 0.012 | 0.803
Self-Injury | -0.011 | -0.167 - 0.144 | 0.885 | 0.082 | -0.136 - 0.301 | 0.457 | 0.064 | -0.205 - 0.334 | 0.636
Sexual Identity (LGBTQ+) | 0.328 | 0.052 - 0.605 | 0.020∗ | 0.891 | 0.504 - 1.278 | ¡ 0.001*** | -0.994 | -1.472 - -0.517 | ¡ 0.001***
Sexual Messages x Sexual Identity (LGBTQ+) | -0.006 | -0.019 - 0.007 | 0.379 | -0.012 | -0.031 - 0.006 | 0.200 | 0.028 | 0.004. - 0.051 | 0.018∗
Harassment x Sexual Identity (LGBTQ+) | 0.020 | 0.003 - 0.037 | 0.018∗ | -0.003 | -0.020 - 0.027 | 0.749 | 0.001 | -0.028 - 0.031 | 0.919
Self-injury x Sexual Identity (LGBTQ+) | 0.058 | -0.108 - 0.225 | 0.489 | -0.088 | -0.322 - 0.145 | 0.458 | -0.063 | -0.352 - 0.225 | 0.663
Observations | 173 | 173 | 173
$R^{2}$ / Adjusted $R^{2}$ | 0.195 / 0.161 | 0.190 / 0.156 | 0.135 / 0.098
Note. *p ¡ .05; **p¡.01; ***p¡.001 |

####  4.2.2. Examining the Associations between Online Risk and Mental Health
Outcomes (H4).

The results of the second chi-square test for RQ1 (Figure 1 (b)), which was
conducted to examine the variation in risk types, guided our subsequent
analyses for H4. Specifically, we focused our analysis on sexual messages,
harassment, and self-injury-related online interactions due to the significant
differences found between LGBTQ+ and heterosexual youth in relation to these
distinct categories of risky online messages received. Therefore, we
investigated to what extent the online risk experiences encountered by youth
in private online spaces were associated with mental health outcomes (i.e.,
Self-Harm and Injury, Depression, and Mental Well-Being), and how this
relationship was moderated by participants’ sexual identity.

_Self-Harm and Injury:_ Our first multiple regression model (M1) identified a
significant and positive association between messages containing sexual
content and youths’ Self-Harm and Injury scores (see Table 5). Results
indicated that youths more frequently engaged in self-harming behaviors when
they also received more sexual messages through private messages, regardless
of sexual identity (t = 1.991, df = 165, p = .048, 95% confidence interval:
[0.000, 0.020]). In contrast, harassing messages and those having to do with
self-injury did not predict Self-Harm and Injury mental health scores (p ¿
.05). Additionally, we conducted a detailed examination of individual Self-
Harm and Injury behaviors by implementing a multivariate regression model (M4)
to discern the specific self-harm behaviors that significantly impact youth’s
mental health. The outcomes of our M4 model align with those of the M1 model,
reinforcing earlier findings. Notably, we observed a positive and
statistically significant main effect between messages containing sexual
content and particular self-harm behaviors, such as carving and pulling hair,
aligning with M1. Additionally, the model (M4) reveals a significant
interaction effect between messages containing harassment and behaviors such
as cutting, carving, and rubbing skin against a rough surface, specifically
for LGBTQ+ youth. However, no significant effects were identified for other
risky messages and their association with Self-Harm and Injury behaviors (p ¿
.05). Appendix A provides comprehensive details of this model.

_Depression:_ In our second regression model (M2), a significant correlation
emerged between messages featuring sexual content and the self-reported
Depression scores of young individuals (see Table 5). The positive direction
suggested that regardless of their sexual identity, young individuals reported
more frequent experiences of depressive thoughts when they received a higher
volume of sexual messages through private messaging (t = 1.992, df = 165, p =
.048, 95% confidence interval: [0.000, 0.028]). Conversely, messages that
included harassment or language likely to induce self-injury did not forecast
self-reported Depression scores in young individuals (p ¿ .05).

_Mental Well-Being:_ In our third regression model (M3), we observed a
significant negative association between messages containing sexual content
and the Mental Well-Being scores of young individuals (see Table 5). The
findings revealed that irrespective of their sexual identity, young
individuals reported lower scores, indicating a decline in Mental Well-Being,
when they received an increased number of sexual messages via private
messaging (t = -2.244, df = 165, p = .026, 95% confidence interval: [-0.037,
-0.002]). On the other hand, messages that contained harassment or language
that might prompt self-injury did not predict the Mental Well-Being scores of
young individuals (p ¿ .05). According to established standards in social
science research an $R^{2}$ value of 10% or above is considered acceptable
(Ozili, 2023; Falk and Miller, 1992). Our models, across all three mental
health measures (Mental Well-being marginally close to the acceptable
threshold), meet this threshold, signifying that they provide meaningful
insights into the relationships between the predictors and mental health
outcomes.

In summary, our results (M1, M2) reveal that a substantial correlation exists
between youth encountering a significant number of messages containing sexual
content and reporting more pronounced mental health challenges (e.g., higher
Depression scores, increased Self-Harm and Injury), and lower Mental Well-
Being scores (M3). This association stands out, as other types of risky
encounters, such as messages containing harassment and self-injury, did not
demonstrate statistical significance with any of the mental health constructs
for the youth as a whole. Consequently, our multiple linear regression models
provide insightful findings, partially confirming our hypothesis (H4),
emphasizing that youth with more high-risk online experiences tend to report
more adverse mental health outcomes compared to those with lower levels of
online risk. Next, we report on the moderating effects of our models.

####  4.2.3. Examining the Moderating Effect of Online Risk and Sexual
Identity on Mental Health (H5).

We examined the moderating effect of sexual identity on the relationship
between risky online messages and youth’s mental health outcomes. In doing so,
our multiple linear regression model (M1) identified a significant interaction
effect between harassing messages and Self-Harm and Injury such that the non-
significant main effect held for heterosexual youth; yet, the correlation
between harassing messages and Self-Harm and Injury for LGBTQ+ youth became
significant and positive. Figure 2 (a) shows the significant interaction
effect between harassment messages and Self-Harm and Injury behavior of youth.
Results indicated that harassment within direct messages represents a unique
risk factor for Self-Harm among LGBTQ+ youth (t = 2.386, df = 165, p = .018,
95% confidence interval: [0.003, 0.037]). In other words, harassment is
associated with an increase in Self-Harm for young people who identify as
LGBTQ+, whereas heterosexual youth are less affected. Hence, this finding
supports our hypothesis (H5), which is the relationship between online risks
(e.g., harassment) and adverse mental health outcome (e.g., Self-Harm and
Injury behavior in this instance) will be stronger for LGBTQ+ youth. In
contrast, the non-significant interaction effect between sexual messages and
sexual identity indicates that sexual messages are positively associated with
Self-Harm and Injury behaviors for LGBTQ+ and heterosexual youth alike.
Similarly, there was no significant interaction effect found between messages
containing self-injury language and sexual identity in relation to Self-Harm
and Injury behaviors (p ¿ .05).

For our model (M2) with Depression score as the outcome variable, the
interaction effect between risky messages (e.g., sexual messages, harassment,
and self-injury-containing messages) and Depression scores was not
statistically significant (p ¿ .05). The lack of an interaction effect between
sexual messages and sexual identity suggests that sexual messages can be
concerning for both LGBTQ+ and heterosexual youths when it comes to their
depressive thoughts.

On the other hand, our model (M3) identified a significant relationship
between messages containing sexual content toward LGBTQ+ youth and Mental
Well-Being scores. Figure 2 (b) shows the significant interaction effect
between sexual messages (X-axis) and the Mental Well-Being score (Y-axis) of
youths. The results indicated that sexual messages are uniquely associated
with positive Mental Well-Being for LGBTQ+ youth only (t = 2.386, df = 165, p
= .018, 95% confidence interval: [0.004, 0.051]). In other words, sexual
messages are associated with a decrease in Mental Well-Being scores for
heterosexual young people (blue straight line), while for LGBTQ+ youth (orange
dotted line) the association is in the opposite direction. Consequently, this
finding contradicts our hypothesis (H5), which stated that the relationship
between online risks (e.g., sexual messages) and negative mental health
outcomes (e.g., Mental Well-Being scores) would be more pronounced for LGBTQ+
youth compared to heterosexual youth. However, there was no significant
interaction effect when examining the relationship between messages containing
harassment or self-injury inducing language and sexual identity in relation to
youth’s self-reported Mental Well-Being scores (p ¿ .05).

In summary, our investigation into how sexual identity moderates the
relationship between negative mental health outcomes and online risk
experiences yields novel insights. Our findings illuminate the distinctive
role of harassment as a risk factor for Self-Harm and Injury, while also
uncovering an unexpected positive association between sexual messages and the
Mental Well-Being of LGBTQ+ youth. Therefore, our results contribute to a
nuanced understanding and partially support our hypothesis (H5) that the link
between online risks and adverse mental health outcomes will be more
pronounced for LGBTQ+ youth. Table 6 provides a summary of the hypothesis
testing results from our statistical model.

(a)

(b)

Figure 2. Moderating Effect of self-reported sexual identity on the association between online risk experiences and mental health outcomes: (a) Shows a positive relationship between harassment (X-axis) and Self-Harm/Injury (Y-axis); (b) Demonstrates a positive relationship between sexual messages (X-axis) and Mental Well-being (Y-axis) for LGBTQ+ youth. Orange and blue lines depict the moderating effect for LGBTQ+ and heterosexual youth, respectively. Table 6. Hypotheses testing results Hypotheses | Supported
---|---
H1: LGBTQ+ youth will report more online risks than heterosexual youth | No
H2: LGBTQ+ youth will report more high risk online experiences than heterosexual youth | Yes
H3: LGBTQ+ youth will report worse mental health outcomes than heterosexual youth | Yes
H4: Youth with more high risk online experiences will report worse mental health outcomes | Partially
H5: LGBTQ+ youth will exhibit a strong relationship between online risks and negative mental health outcomes | Partially

###  4.3. Nature of Risks Experienced by LGBTQ+ Youth in Private Online Spaces
(RQ3)

To further unpack the statistically significant effects found in RQ1 and RQ2,
this section qualitatively examines risk-flagged Instagram conversations
donated by the LGBTQ+ youth to shed light on the nature of online risks.
LGBTQ+ youth participants flagged a total of 1,468 private messages of 808
conversations as ”risky”, and these conversations serve as the unit of
analysis for the results presented below. Table 3.6.2 presents the themes and
subthemes for RQ3, as well as their corresponding codes and illustrative
quotations. We used illustrative quotations to describe each of the themes
that emerged from our thematic analysis. In the illustrative conversations, we
replaced all referenced names with the letter ”X” in the messages and removed
the Unicode characters (emojis). Overall, we found that the risky experiences
reported by LGBTQ+ youth were often related to their sexual identity.

####  4.3.1. Most Risky Conversations had Sexual Undertones.

Overall, we found that most of the conversations LGBTQ+ youth flagged as
making them feel unsafe or uncomfortable contained sexual content, either
related to their gender or sexual identity and/or made direct sexual
solicitations. For instance, more than one-third of the conversations
contained messages with sexual content, and one-fourth of these conversations
took place with strangers. Sexual messages often contained explicit adult
content (e.g., photos and videos), porn website URLs, and sexual texts. For
example, LGBTQ+ youth were often added to porn group chats by strangers, where
they also received inappropriate content and/or porn site URLs. Interestingly,
we often found LGTBQ+ youth were interested in participating in such
conversations as a way to explore their sexual identities but wanted to do so
slowly. For example, a 21-year-old homosexual woman had the following
conversation with a stranger:

> Other person: You are so incredibly beautiful. Its an absolute pleasure to
> meet you. What exactly is it you are looking for? I’m looking for a special
> friend to share, maybe explore things together. We can be friends and
> forward funny videos. You feel you might be interested in something casual
> like this?
> Participant: Uh sure, Haha, Omg! I think I probably seen you on a dating app
> lol
> Other person: Instagram User sent an attachment [flagged as sexual content]
> Participant: whoa, thats just downright fast

While the LGBTQ+ youth mostly received sexual message content from strangers,
we found some conversations where participants received sexual messages from
their peers and the youth themselves also sent sexual messages to others. When
asked to describe the situation, they explained that they sent these sexual
photos either to experiment or to earn money, but often later they regretted
it. Below is an example conversation that a 21-year-old bisexual woman
flagged:

> Other Person: Do u have haire on pussy
> Participant: A lil
> Other Person: Can I see
> Participant: X sent an attachment [flagged as sexual content]
> Other Person: and boobs

Almost a quarter of the conversations consisted of text messages with sexual
solicitations. These conversations frequently included adult content;
therefore, they were double-coded for sexual content and solicitations.
Similar to the sexual message content, LGBTQ+ youth mostly received these
sexual solicitations from strangers. In some cases, participants were offered
different kinds of incentives, e.g., monetary and/or other benefits, in
exchange for a sexual relationship with these strangers. For instance, a
19-year-old bisexual woman received the following messages:

> Other Person: Hello beautiful, can you be my sugar baby? I’m ready to help
> you and you gonna get paid weekly. Let me know when you’re ready. Just chat
> with me everyday and you get your paid for that [flagged as harassment]
> Participant: can u buy my amazon wish list
> Other Person: Money has never been my problem,How much is your amazon
> wishlist
> Participant: like $300ish

Similar to the sexual content, LGBTQ+ youth did not just receive these sexual
solicitations from strangers, but some of the sexual solicitations came from
their peers as well. While in majority of the conversations we saw youth to
participate or interact with the other person, there were a few other
conversations where we found our participants received persistent unwanted
messages that they flagged as ”harassment”. These messages were received from
strangers and peers both. Although youth repeatedly requested to stop texting,
they kept sending these messages. Interestingly, we noticed there were a small
portion of conversations where they started with sexual content and/or sexual
solicitations, but later they moved toward harassment. This is because
participants often refused to advance with the sexual solicitations, which
caused the other person to become aggressive, sometimes even threatening the
youth.

Along with the above sexual solicitations, we found conversations where
participants were harassed because of their sexual identity. Participants
often labeled these messages as ”harassment” or ”unwanted messages”. They
mostly received these messages from their peers. Some of these conversations
also had sexual slurs mentioned within instances of sexual harassment. For
example, a 20-year-old transgendered man flagged the following conversation
that they had with someone they knew:

> Other Person: Call me. [Video call started.] [Video call ended.]
> Participant: Wtf what’s wrong with you
> Other Person: I didn’t mean to do that
> Participant: Oh really? [Video call started.] [Video call ended.]
> Other Person: Answer the phone fag [flagged as harassment]
> Participant: Stop texting me please

LGBTQ+ youth also received similar messages from strangers where they were
harassed for their sexual identities. Interestingly, most of these
conversations took place in large group chats created for makeups, games,
travels, electronics troubleshooting, etc, where people may not know each
other. Youth were often harassed during casual conversations related to
technical problems with their games and/or computers. For instance, a 21-year-
old gender-fluid woman had the following conversation in a group chat for
gamers:

> Participant: My fucking laptop is just burning up
> Other Person: thats because you’re a faggot and your gonna burn in hell
> [flagged as harassment]
> Participant: well that was impolite because you dont know anythin abt me

LGBTQ+ youth also often received different advertisements to promote sexual
products and services that were specifically targeted to their sexual
identities. Participants mostly flagged these messages as spam or harassment.
In these cases, it seemed apparent that these advertisements and/or scams were
aware of their sexual identities, possibly disclosed through their profile
descriptions or public posts. LGBTQ+ youth also received scam messages that
often contained a monetary offer for signing up on a porn website, sending
body photos, or working as a sex worker. Participants mostly did not reply to
these messages; therefore, we did not find any conversations where they
stepped into these monetary traps. In fact, many of the youth seemed savvy to
avoid or exploit these solicitations to their own advantage. Below is an
example conversation that a 21-year-old transgender (bisexual) woman had with
a stranger:

> Other Person:  Hi, My ex trans baby left me. Will you be my sugar baby
> [flagged as spam]
> Participant:  pay me first ? bc i’ve been in these situations b4
> Other Person:  I’m a legit sugar daddy
> Participant:  send me $100 den

As shown above, LGBTQ+ youth often received sexual content and solicitations
from strangers, but they often took these interactions in stride until they
became threatening. However, when they were harassed because of their sexual
identities, they did not appreciate such messages. In the next section, we
present other general types of online risks that LGBTQ+ youth often reported.

####  4.3.2. LGBTQ+ Youth Encountered Harassment and Scams.

LGBTQ+ youth also received other types of risky messages that were not sexual
in nature. For example, we found one-third of the conversations had some form
of harassment, e.g., namecalling, threats, abusive words. Participants
received these messages not just from strangers but from their peers also. We
noticed that youths were often bullied (with threats and abusive words) by
peers because of arguments over some physical incidents that happened at the
their schools or workplaces. Also, they were often body-shamed by their peers.
For instance, a 15 years old non-binary (bisexual) woman had the following
conversation with a peer:

> Other Person: hey, your kind fat. you should lose weight. I remember you
> lost weight… what happened [flagged as harassment]
> Participant: um it was hard for me. I’ve been suffering… Stop sending me
> hate messages

Besides the harassing messages, participants also received different
advertisements and scam messages that were not specifically targeted to their
sexual identity. LGBTQ+ youth flagged these unsolicited messages as unsafe
because they often promoted different products or websites and/or intended to
cause financial harm. Some of these spam messages contained links to the
websites of illegal products, e.g., tobacco or recreational drugs.
Participants rarely responded to these messages, and therefore, these
conversations were mostly one-way. Because these risk experiences seemed
relatively typical to the risks generally experienced by youth online (Razi et
al., 2022b, 2021a; Hartikainen et al., 2021; Agha et al., 2020), we chose not
to examine these conversations in more depth.

####  4.3.3. LGBTQ+ Youth Received Support from Peers Regarding Negative
Experiences.

Although our conversation dataset focused on risky messages flagged by the
LGBTQ+ youth, we often found messages in these conversations where the LGBTQ+
youth shared negative experiences relating to their sexual identities with
peers. Most of these conversations had messages that they flagged as
harassment or bullying. This is because, in the same messaging thread, the
youth had arguments with their peers over an issue that led to harassment, and
therefore, they flagged the messages as unsafe. However, the youth also shared
their negative emotions with the same peers, receiving compassion and moral
support. Participants were also seen to share experiences that they had with
homophobic people around them, mostly in their families. In some of these
conversations, they expressed that they are afraid of coming out because of
their LGBTQ+ intolerant families. Participants also often discussed their urge
to self-harm as they became depressed about their daily struggle for their
gender identities. Interestingly, youth often mentioned about the negative
experiences they faced when they publicly shared their photos on different
social media platforms. Below is an example conversation that a 19-year-old
gender-fluid man had with a peer:

> Participant: i wish things were going ok i have been having thoughts of
> wanting to cut and i freaking hate it so much i just want to scream and cry
> for hours
> Other Person: I’m sorry to hear it, self-harm urges are awful
> Participant: I hate that i am still afraid to posting wearing a dress or
> skirt it is making me dysphoric as fuck. People are good for judging me for
> showing my pics on Instagram and This is the 5th time I was treated badly
> online And im so fucking tired of it.

Participants did not just share their struggles related to gender identity;
they also often discussed their mental health issues with peers. LGBTQ+ youth
expressed their sad feelings about different negative incidents that occurred
with their friends and family. Besides these, participants also shared their
depressive thoughts, e.g., self-harm, suicide, with their peers. In all these
messages, we saw their peers being compassionate and providing advice to cope.
For instance, a 15-year-old homosexual woman and their peer had the following
conversations:

> Participant:  And maybe I’d been lucky to die
> Other Person: Umm X, don’t die on me
> Participant:  You know almost everytime I smile or something of that sort is
> fake, because I’ve been broken for so long I’ve become the person I thought
> I was when I was 9-12. I’ve become the girl who literally lost it from
> grief, and wants to just end it all
> Other Person: Wait X plz no crying…imma bout to be sad now

Alongside the depressive feelings, we also often found the LGBTQ+ youth
sharing other feelings about the negative experiences that they had with their
families, friends, and others. We observed that these feelings were not sad in
particular, but more related to fear, anger, disgust, and/or anxiety.
Participants also occasionally exchanged advice on their drug usage. A few
conversations contained messages where one person used illegal tobacco or
drugs and the other person gave advice or showed concern for their risky
behavior. In summary, LGBTQ+ youth did not just receive risky messages from
others; they also received support and advice from their peers about their
negative experiences with their family and friends. More importantly, they
often sought support for the struggles that they dealt with in their families
regarding their sexual identity.

##  5\. Discussion

In this section, we briefly summarize our key findings, unpack the
implications of these results, and provide recommendations for empowering and
protecting LGBTQ+ youth in social media spaces.

###  5.1. LGBTQ+ Young Experience More Severe Online Risks on Social Media
(RQ1)

While LGBTQ+ youth in our study flagged fewer Instagram DMs as risky (48.56%)
compared to heterosexual participants (H1), they encountered significantly
more high-risk situations (H2), involving increased instances of sexual
messages and self-injury. Conversely, heterosexual youth reported more low-
risk conversations, primarily centered around online harassment. Despite
conflicting with previous research that positioned LGBTQ+ youth as highly
vulnerable to cyberbullying (Aboujaoude et al., 2015; Zych et al., 2015,
2016), our qualitative findings elucidate this apparent contradiction. Many
instances of online harassment experienced by LGBTQ+ participants were
sexually motivated, leading to categorization as sexual rather than general
harassment. This insight reveals the unique challenges faced by LGBTQ+ youth,
who often endure online harassment targeting their sexual and gender
identities, arguably more detrimental than bullying experienced by
heterosexual counterparts. Consequently, our study indicates that LGBTQ+ youth
reported significantly more risky conversations involving self-injury and
mental health struggles compared to their heterosexual counterparts.

These significant findings underscore the need for urgent action in
implementing targeted risk prevention and mitigation programs addressing
sexually motivated online harassment and supporting the mental health of
LGBTQ+ youth both online and offline. Existing national programs like
”Netsmartz” in the United States (for Missing and Children, 2023) and
”ThinkUKnow” in the United Kingdom (thinkuknow, 2023) offer commendable
educational resources but primarily target broader audiences. To effectively
safeguard vulnerable communities, such as LGBTQ+ youth, there’s a growing
demand for specialized education programs tailored to their unique risks and
challenges. While organizations like The Trevor Project provide vital crisis
intervention and suicide prevention services for LGBTQ+ youth (Project, 1998),
recent studies highlight limitations, including resource availability and
operational challenges (Levtov, 2023). Establishing such platforms alone is
insufficient; continuous monitoring and timely support are crucial for their
effectiveness. Our research emphasizes the need for more focused studies on
vulnerable youth, including LGBTQ+ (Gatehouse et al., 2018; Saha et al.,
2019), neurodiverse (Page et al., 2022; Barros Pena et al., 2023), and foster
youth (Badillo-Urquiola et al., 2019; Fowler et al., 2022), concerning their
online safety. Recent discourse on youth empowerment and resilience in online
spaces (Agha et al., 2023; Akter et al., 2022b; Wisniewski et al., 2015; Akter
et al., 2023a, b; Kumar, 2022; Akter et al., 2022a) may not be inclusive of
the needs and struggles of more vulnerable youth populations, necessitating a
more comprehensive approach.

###  5.2. Social Media’s Dual Impact on LGBTQ+ Youth Mental Health (RQ2)

Our H3 results confirmed existing research indicating that depression
disproportionately affects LGBTQ+ minority youths compared to their
heterosexual counterparts (Quarshie et al., 2020). Furthermore, LGBTQ+ youth
exhibit higher rates of self-harm and diminished mental well-being (Almeida et
al., 2009; Charmaraman et al., 2021) when compared to heterosexual peers
(Russell and Fish, 2016, 2019) (refer to Table 4 showing the differences in
mental health constructs). In addition, our H4 and H5 results introduced novel
insights by linking online risk experiences to mental health outcomes for both
LGBTQ+ and heterosexual youth, addressing a crucial gap in the literature.
Through our models M1-M3 (refer to Table 5), we observed that sexual messages
are significantly and positively associated with increased levels of self-
harm, injury, and depression, while negatively impacting mental well-being for
all youth, regardless of sexual identity. This supports our H4 that more high-
risk online experiences correlate with worse mental health outcomes. However,
examining interaction effects (H5) revealed nuances. Online harassment was
significantly and positively linked to self-harm and injury (Figure 2) for
LGBTQ+ youth, not evident in the main effects, while an opposite effect
emerged for sexual messages: more messages were associated with reduced mental
well-being for heterosexual youth but positively correlated with mental well-
being for LGBTQ+ youth.

Careful consideration of these findings is essential due to the conflicting
mental health outcomes associated with receiving sexual messages. Both LGBTQ+
and heterosexual youth showed higher rates of Depression and Self-Harm/Injury
in association with sexual messages, yet for LGBTQ+ youth, an increase in
sexual messages was also linked to improved Mental Well-Being. Our exploration
of qualitative insights in response to RQ3 offers a potential explanation for
this paradox. While LGBTQ+ youth faced sexual messages harassing their
identities, they also received messages facilitating exploration without
judgment. Future studies should distinguish between harmful and helpful sexual
messages sent to and by LGBTQ+ youth. This holds crucial implications for
Human-Centered Machine Learning (HCML) researchers developing algorithms to
detect and mitigate online sexual risks (Razi et al., 2020b, 2021b, 2023). If
these algorithms inadvertently restrict or censor beneficial sexual
conversations of LGBTQ+ youth, the technologies designed to protect them may
disproportionately harm them. Our research underscores the need to provide
LGBTQ+ youth a safe space for exploring their sexual and gender identities.
Future HCML research should strive to differentiate between sexually motivated
harassment and violence online and healthy sexual exploration during
adolescent developmental growth.

###  5.3. Social Media Amplifies Risks But Provides Needed Support for LGBTQ+
Youth (RQ3)

Our qualitative findings played a crucial role in elucidating unexpected and
occasionally conflicting results from our statistical analyses, offering
deeper insights into private online interactions that made LGBTQ+ youth feel
uncomfortable or unsafe. One key theme revealed in risk-flagged messages was
the prevalence of sexual undertones, even in harassing messages, intensifying
the interconnectedness between online risk and the sexual identities of LGBTQ+
youth. This underscores the need for spaces where LGBTQ+ youth can interact
online without their sexual identities being the focal point, a benefit often
naturally afforded to heterosexual youth. Our results emphasize the urgent
societal need for increased awareness and education about the LGBTQ+ community
to foster acceptance and inclusion. Additionally, our research highlights the
normalcy of some level of risk-seeking behavior in youth development
(Baumrind, 1987), as evidenced by LGBTQ+ youth’s willingness to engage with
flagged sexual messages, sometimes benefiting from these interactions.
However, a concern arises as these conversations often occur with strangers,
posing potential risks, including sexual predation. To address this, it is
crucial to provide safe outlets for LGBTQ+ youth (Buckley, 2022), allowing
them to question, explore, and discuss their sexual identities online.

Our research also revealed that LGBTQ+ youth flagged messages as risky even
when sharing struggles with peers to seek support, highlighting the double-
edged nature of social media. This aligns with existing literature indicating
that LGBTQ+ individuals often utilize social media as a support group to share
unique experiences both online and offline (Craig and McInroy, 2014; Lucero,
2017). Notably, a proposed Kids Online Safety Act (KOSA) (Blumenthal, 2022)
aims to enhance minors’ online safety but raises privacy concerns due to
increased surveillance and potential content filtering. Legislative efforts to
ban certain platforms could inadvertently harm marginalized groups, such as
LGBTQ+ teens who rely on social media to connect to peers (Kelly, 2023; Brown,
2023), to question, explore, and understand their sexual identities (Feiner,
2022). Restricting Internet use (Reed, 2023) for LGBTQ+ youth might diminish
their support networks, necessitating practical solutions that reduce harmful
risk exposure while providing opportunities for online support.

###  5.4. Implications for Practice and Design

We provide several actionable recommendations towards education and design in
promoting the online safety and digital well-being of LGBTQ+ youth.

####  5.4.1. Establishing Stronger Community Guidelines and Norms to Protect
LGBTQ+ Youth Online.

Recent research highlights significant safety risks for LGBTQ+ users on social
media platforms (Silva, 2021), with claims that these platforms prioritize
profit over LGBTQ+ safety and lives (Yurcaba, 2022). Moreover, researchers
have raised doubts about the effectiveness of existing community guidelines
(Connellan, 2022; Yurcaba, 2022), emphasizing their inadequacy in protecting
the LGBTQ+ community. Our study advocates for stronger community-based
guidelines, specifically focusing on safeguarding sexual minority social media
users, especially LGBTQ+ youth. These guidelines provide a crucial foundation
for curtailing harmful content targeting sexual minority youths, setting clear
boundaries for acceptable behavior, and discouraging harassment and
discrimination. They convey a powerful message affirming the right to a safe
online environment for all users, irrespective of sexual identity.
Additionally, guidelines offer a structured mechanism for reporting
unacceptable behavior, empowering victims to seek assistance. Violators can
face consequences, including warnings, suspensions, or bans from the platform
(Reddit, 2023b). In summary, when combined with effective enforcement, these
guidelines contribute to positive community norms by setting clear
expectations, reinforcing social norms, fostering trust, and promoting
responsible behavior (Scharlach et al., 2023; Reddit, 2023a).

####  5.4.2. Providing Resources for Cyberabuse Prevention and Support.

To this end, providing help resources and establishing peer support networks
for LGBTQ+ youth who are victims of cyberabuse is crucial. These resources
empower youth to combat the effects of online abuse and seek help when needed.
Equipping them with tools to address cyberabuse is paramount in giving them
control over their online experiences. Previous research delved into the
effectiveness of diverse support systems, encompassing family, curriculum,
peer networks, school policies, Gay-Straight Alliances, etc. and confirmed
that these elements are positively linked to the enhancement of positive
socioemotional, behavioral, and educational outcomes for LGBTQ+ youth (Leung
et al., 2022). Additionally, we must establish mechanisms for bystander
intervention to create a safer online environment. Encouraging friends, family
members, and online community members to step in and advocate for LGBTQ+ youth
ensures that they do not bear the burden of responsibility alone. In sum, our
research serves as a clarion for immediate action, emphasizing the necessity
of creating safer and more supportive digital spaces for all youth, with
particular attention to the unique needs and challenges faced by LGBTQ+
individuals.

####  5.4.3. Creating Safe Online Spaces for LGBTQ+ Youth.

Our research underscores the need for safe online spaces explicitly designed
for LGBTQ+ youth. These spaces should serve as shelter, where they can explore
their sexual identities and access accurate sexual health information without
fear of judgment. The absence of such secure environments often turn LGBTQ+
youth to seek information and support from fringe or unregulated online
communities (Badham, 2021; the New Drug, 2022). Unfortunately, these spaces
may lack adequate moderation, exposing youth to harmful ideologies and
potential safety risks. Furthermore, our findings highlight the significant
mental health challenges faced by LGBTQ+ youth, who reported a higher
prevalence of such challenges compared to heterosexual youth. This highlights
the urgency of ensuring easy access to professional support resources in
online settings to mitigate these adverse health outcomes. Such initiatives
can act as protective measures, reducing the need for LGBTQ+ youth to engage
with potentially harmful strangers online. In essence, our research serves as
a call to action to create safer and more supportive digital environments for
all youth, with a particular focus on the unique needs and challenges faced by
LGBTQ+ individuals (Project, 1998; Center, 2023; Hotline, 2023).

####  5.4.4. Developing Automated Risk Detection Differentiating between
Sexual Harassment and Exploration.

In contrast to our previous point, we also advocate for existing social media
spaces to be safer for LGBTQ+ youth, so that they do not need to segregate
themselves. Our findings shed light on the heightened vulnerability of LGBTQ+
youth to unsolicited sexual messages from strangers. Consequently, there is a
pressing need for the development and implementation of automated risk
detection systems capable of identifying cyberabuse directed at the LGBTQ+
community and enforcing appropriate penalties. Navigating this challenging
landscape reveals algorithmic bias as a significant threat to the well-being
of LGBTQ+ individuals (DeGeest, 2022). Past studies have shown that automated
content moderation often restricts LGBTQ+ content, under the guise of
”preserving decency” and ”protecting the youth” (Hay, 2021; Kayser-Bril, 2020;
DeGeest, 2022). The challenges associated with bias detection involve anomaly
identification and assessing error rate equality (Lee et al., 2019; Zafar et
al., 2017; Spielkamp, 2017; Corbett-Davies et al., 2016). HCML emerges as a
pivotal solution, emphasizing fair language models and comprehensive training
in gender-neutral pronouns (Chancellor, 2023; Tomasev et al., 2021). HCML
strategies include expanding nondiscrimination laws, regulatory sandboxes,
safe harbors, and self-regulatory practices with bias impact statements and
inclusive design. Efforts also focus on algorithmic literacy and collaborative
mitigation through formal feedback mechanisms (Lee et al., 2019). However, a
significant challenge lies in ensuring that these penalties do not
inadvertently infringe upon or harm the LGBTQ+ population. Striking a delicate
balance is essential to mitigate potentially harmful interactions while
preserving the rights of LGBTQ+ individuals to freely express their sexual
identity. These automated systems should demonstrate sophistication in
distinguishing between different types of content. For instance, they should
distinguish between hate speech directed towards these communities and content
that addresses transgender or homosexual issues.

####  5.4.5. Raising Awareness and Inclusivity.

Our study underscores the need to foster greater acceptance and understanding
of the LGBTQ+ community. Achieving this goal requires a concerted effort to
expand online awareness and educational programs. A diverse array of
strategies may be employed to cultivate more inclusive and empathetic
environments for LGBTQ+ youth. One strategy may involve educational campaigns
for disseminating accurate and up-to-date information regarding LGBTQ+ issues.
Previous research highlights how pervasive myths can undermine and dehumanize
LGBTQ+ individuals, often unfairly labeling these young individuals as
“confused” or “misguided” (Abreu et al., 2017). Therefore, we suggest that
these campaigns adopt a conservative approach that incorporates storytelling
and representation supported by facts from evidence-based research. Such
personal narratives, combined with objective research, have the potential to
put faces and voices to humanize LGBTQ+ experiences, making it easier for
others to empathize and relate (Suzuki et al., 2018; Bauer, 2003). A social
media campaign explaining ‘deadnaming’ and how it is harmful to transgender
people would be a specific example (Resnick, 2023). This approach has the
potential to address common misconceptions, ultimately enlightening the public
on the complexities of LGBTQ+ identities.

###  5.5. Limitations and Future Research

We would like to highlight some limitations of our work, which inform future
research directions. First, since youth participants were asked to flag at
least two conversations that made them feel unsafe or uncomfortable, it is
possible that they did not flag all conversations that met this criteria.
Further, because we allowed youth to self-annotate their risk flagged messages
by risk level and type, we do not account for individual differences in their
perceptions of risk. At the same time, we adopt a victim-centered lens that
believes participants’ lived experiences, rather than questioning these
experiences. Second, to have enough power to detect significant differences
between heterosexual youth and LGBTQ+ youth, we had to group LGBTQ+ youth
allies (e.g., lesbian, gay, bisexual, transgender, queer or questioning,
intersex, and asexual). As different LGBTQ+ youth likely have unique
experiences concerning their sexual and gender identities, as well as their
online risk experiences, studying specific subgroups of the LGBTQ+ community
might yield important insights not uncovered in our study. We suggest that
future studies oversample from LGBTQ+ communities to achieve large enough
sample sizes to detect medium to large size effects between subgroups within
the LGBTQ+ community. While a strength of our research is examining the
private online communications and risk experiences of LGBTQ+ and heterosexual
youth, this means that our results should not be generalized to their public
social media communications. Further, private communications via Instagram may
also differ from those of other social media platforms. Therefore, we
encourage future research to take into account different private versus public
contexts, as well as diversify to study youths’ experiences on other popular
social media platforms. Ultimately, the landscape of adolescent mental health
is likely influenced by a multitude of factors beyond private conversations.
Future research should explore additional factors that play a role in shaping
the mental well-being of LGBTQ+ youth, extending beyond the scope of their
online risk experiences.

##  6\. Conclusion

The present study offers empirical evidence regarding the online risk
experiences encountered by LGBTQ+ youth in private social media spaces.
Furthermore, we examined how these interactions may impact the overall mental
health of young adults. We conducted a mixed-method study to understand the
severity and types of risks within online private messages experienced by
LGBTQ+ youth. In doing so, we differentiate various types of risky and
uncomfortable experiences of LGBTQ+ youth and show that LGBTQ+ youth face more
severe risks in private spaces than their heterosexual peers. In addition, our
findings highlight the relationship between sexual messages and Self-Harm
behavior among both LGBTQ+ and heterosexual youth. In contrast, young people
identifying as LGBTQ+ are more likely to Self-Harm due to harassment, whereas
heterosexual youth are less affected. At the same time, an increased number of
sexual messages corresponded to positive mental Well-Being among LGBTQ+ youth.
Thematic analysis of DMs show that LGBTQ+ youth engage in risky conversations
with strangers and frequently receive sexual messages and solicitations with
inappropriate content. Our findings highlight the critical need for social
media tools and support mechanisms to combat risks that take place within
private online spaces and are particularly likely to negatively effect LGBTQ+
youth.

###### Acknowledgements.

This research is supported in part by the U.S. National Science Foundation
under grants #IIP-2329976, #IIS-2333207 and by the William T. Grant Foundation
grant #187941. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the authors and do not necessarily
reflect the views of the research sponsors. We would also like to thank all
the participants who donated their data and contributed towards our research.

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