Abstract
Objective:
To determine whether rates of online racial discrimination changed over the course of 2020 and their longitudinal effects on Black youths’ mental health.
Method:
This longitudinal study collected 18,454 daily assessments from a nationally representative sample of 602 Black and White adolescents in the United States (58% Black, 42% White; mean age = 15.09 years, SD = 1.56 years) across 58 days during the heightened racial tensions between March and November 2020.
Results:
Black youths experienced increases in online racial discrimination, and these increases were not fully explained by time spent online or by general cybervictimization experiences. Online racial discrimination predicted poorer same-day and next-day mental health among Black youths but not among White youths. Black youths’ mental health did not predict their online racial discrimination experiences.
Conclusion:
Online racial discrimination has implications for shaping mental health disparities that disadvantage Black youths relative to their White peers. Programs can be implemented to decrease online hate crimes, and health providers (eg, pediatricians, psychiatrists) should develop procedures that mitigate the negative mental health effects following online racial discrimination experiences.
Keywords: online racism, Black adolescent development, mental health, ecological momentary assessment
In 2020, the killings of Breonna Taylor, George Floyd, and other Black Americans at the hands of White civilians and law enforcement officers sparked an uprising against racial injustice that was met with fierce opposition from White nationalists and domestic terror groups in the United States. Not only did these groups become more prominent, but they also became more active in online spaces through Zoom-bombing (ie, unwanted intrusion during a video-conference call)1 and online message boards (eg, 8kun), where race-related hate crimes were coordinated under the guise of anonymity.2 Unfortunately, youths navigated online spaces to connect with peers during social distancing mandates and school closures brought about by the COVID-19 pandemic, potentially increasing youths’ exposure to online racism. To date, scientists have fallen short in documenting the nature of racism during this period.3 To understand the context and consequences of online racism, the present study is one of the first to examine how rates of online racial discrimination changed throughout this period and how such discrimination predicted mental health longitudinally among a nationally representative sample of Black adolescents.
Because of historic systemic inequities, Black youths navigate stress associated with racial discrimination.4,5 Racial discrimination is the behavioral component of racism and is the differential treatment based on race or on inadequately justified factors other than race that creates disparities in power, resources, and opportunities between racial groups.6,7 Interpersonal racial discrimination may have consequences for minoritized groups’ psychological adjustment because it directly activates stress processes and erects barriers between individuals and resources.8,9 Discrimination can also result in greater hopelessness and increased vigilance that indirectly activates stress processes.8–10 Indeed, racial discrimination has been found to be a robust predictor of maladaptive academic, physical, and psychological outcomes.11,12
Although there is a rich body of literature addressing racial discrimination in offline settings, less is known about the frequency of online racial discrimination during the COVID-19 pandemic. As COVID-19—related public health restrictions contributed to an increase in adolescents’ Internet use,13 online spaces may have been primary settings for discrimination during the 2020 racial unrest. Online racial discrimination is a specific form of racial discrimination that occurs on Internet-based social media or direct messaging platforms.14,15 It includes disparaging remarks, symbols, images, or behaviors that inflict harm through the use of computers, cell phones, and other electronic devices.15 As perpetrators of discrimination become more cognizant of negative stereotypes about Black youths, discrimination is more likely to unfold over time. For instance, Black citizens have reported racial discrimination following stereotypes about their racial group members as carriers of COVID-19.16,17 Thus, online racial discrimination may expectedly increase over time, but no study has empirically captured such change during this particular sociohistorical period.
A larger issue in the literature is the lack of longitudinal research addressing the consequences of online racial discrimination on mental health, as most extant studies have been cross-sectional.14,18 Online spaces have become prominent developmental contexts for youths during the COVID-19 pandemic; yet, only a single study has documented the lasting effects of online racial discrimination on adolescents’ mental health.18 A pre-pandemic study followed a sample of Black and Latino adolescents over a 3-year period and found that direct online racial discrimination was unrelated to self-esteem, depressive symptoms, and anxiety.18 These null findings are likely attributable to the study’s timespan, as the negative effects of online racism may have deteriorated over a 3-year period. Considering that adverse events predict more immediate than distal outcomes,19 we examined whether online racial discrimination predicted same-day and next-day mental health symptoms among Black youths.
The present study had 2 primary research goals: (1) to examine how the frequency of online racial discrimination changed prior to and following the 2020 racial unrest; and (2) to determine whether online racial discrimination predicted mental health longitudinally among a nationally representative sample of Black youths. We predicted that, on average, online racial discrimination would increase throughout the study period. Furthermore, due to stigma, stress, and hypervigilance,8,9 we hypothesized that Black youths who experienced online racial discrimination would report decrements in their same-day and next-day mental health. In addition to our core research goals, we examined whether youths’ poor mental health occurred prior to and contributed to their online racial discrimination perceptions, and whether the associated mental health consequences of online racial discrimination emerged among same-aged White youths.
METHOD
Participants
Our study participants include a nationally representative sample of 602 self-identified Black and White adolescents (58% Black; 39% male; 71% qualified for free lunch; mean age =15.09 years, SD = 1.56 years, age range = 12–18 years). Given our interest in examining online racial discrimination experiences among Black adolescents, we used the White sample for descriptive purposes and exploratory comparisons with the Black sample. Thus, our analytic sample included 351 Black adolescents (40% male; 85% qualified for free lunch; mean age = 14.78 years, SD = 1.52 years).
Procedure
The study worked with a survey company to recruit a nationally representative sample of adolescents via random sampling. Of those contacted for participation (n = 1,150), 602 adolescents and their primary caregivers participated. All consented adolescents and their primary caregivers provided demographic information and completed baseline measures. Adolescents took approximately 5 to 10 minutes to complete a 52-item daily survey between 5 pm and 12 am using their Internet-capable devices across 58 days over 4 waves in 2020: wave 1 (ie, 14 days; March 2–15), wave 2 (ie, 14 days; April 8–21), wave 3 (ie, 15 days; May 18–June 1), and wave 4 (ie, 15 days; October 19–November 2). Figure 1 presents key dates of 2020 and the study design. Adolescents received $40 for their participation at each wave. During wave 1, the primary aim was to understand how daily environmental and psychosocial stressors contributed to adolescents’ well-being and overall school adjustment. When school closures occurred nationwide following the wave 1 assessment, the principal investigators pivoted the mission of the study to capture COVID-19—related stressors and their links with students’ daily well-being and school adjustment in the context of pandemic-related school closures and stay-at-home orders. To increase the sample size and to buffer against attrition over waves, we recruited new participants at each wave. All materials and procedures were reviewed and approved by the authors’ university institutional review board.
FIGURE 1.
Key Dates of 2020 in the United States and Study Design
Measures
Daily Online Racial Discrimination.
Each day, adolescents reported whether they experienced online racial discrimination, which was a single item from the Online Victimization Scale (OVS).15 The OVS is a validated measure of adolescents’ experiences with online general, sexual, and racial victimization (Supplement 1, available online). Drawing from the OVS and considering our research questions, we focused on the Individual Online Racial Discrimination subscale as the primary independent variable. For the subscale, adolescents used a 2-point Likert scale to report on their daily and direct encounters of victimization based on their racial identification (0 = no, 1 = yes; ie, “Over the past 24 hours, did anyone say or post mean or rude things about you because of your race or ethnic group online?”).
Daily Mental Health Symptoms
Mental health was assessed each day using adolescents’ self-reported depressive symptoms, anxiety, stress, and exhaustion/tiredness. Depressive symptoms and anxiety were assessed using the Profile of Mood States (POMS) Questionnaire20 with a 5-point Likert scale (1 = not at all, 5 = extremely). Each measure demonstrated acceptable internal consistency (depressive symptoms: 2-item; eg, “Today, how often did you feel depressed or sad,” r = 0.95, RChange = 0.98; anxiety: 2-item; eg, “Today, how often did you feel anxious,” r = 0.93, RChange = 0.97). After our reliability and validity assessments of these shortened versions of the POMS subscales (Supplement 2, available online), a mean score was created for both depressive and anxiety indices within each day and coded such that higher values indicated worse mental health symptoms. Stress was a single item from the Daily Stress Scale21 (ie, “Overall, how stressful was your day?” 1 = not at all, 4 = very stressful). Adolescents also reported the degree to which they felt tired (1-item; ie, “Overall, how tired did you feel today?” 1 = not at all, 5 = very much).22
Covariates.
We accounted for potential third variable confounds that could bias the link between online racial discrimination and youths’ mental health. Between-person covariates included youths’ sex (0 = girl, 1 = boy), age (range = 12–18), parent-reported eligibility for free/reduced-priced lunch (0 = participant eligible for free/reduced-price lunch, 1 = participant ineligible for free/reduced-priced lunch), and cohort (ie, wave in which youths were recruited). To specify that the variations of the mental health symptoms were attributed to online racial discrimination, within- and between-person covariates included time spent on social media (1-item; “How much time over the last 24 hours did you spend using social media.”; 1 = less than 1 hour, 11 = 10 or more hours) and whether youths experienced general cybervictimization (1-item; “Over the past 24 hours, were you cyberbullied?” 0 = no, 1 = yes; see SI for a validity assessment of our OVS subscales). To account for possible time and fatigue effects of study participation and sleep behaviors affected by the COVID-19 pandemic, within-person covariates included time (ie, day of the study; range = 0–57), whether the survey was on the weekend (0 = weekday, 1 = weekend), last night’s sleep quality (1 item; ie, “How well did you sleep last night?” 1 = very bad, 5 = very good),23 and last night’s sleep quantity (1 item; range = 0–24 hours23) (Table S1, available online, provides a description of the covariates for our sample of Black and White adolescents).
Missing Data
As is common in all research contexts, our longitudinal design included missing data. Among the sample of 602 Black and White adolescents, 265 adolescents (44%) participated in wave 1, 391 (65%) in wave 2, 387 (64%) in wave 3, and 341 (57%) in wave 4. These participation rates were shaped by rates of adolescents who opted in and out of the study in later waves. Specifically, 265 adolescents (44%) were recruited in wave 1, 216 (36%) in wave 2, 94 (16%) in wave 3, and 27 (4%) in wave 4 (see the SI for comparisons on key constructs by wave of recruitment). Since they were first recruited into the study, 307 participants (55%) did not miss any waves, 135 (22%) missed 1 wave, 57 (9%) missed 2 waves; and 103 (17%) missed 3 waves (see SI for retention rates by wave of recruitment). A Little missing completely at random (MCAR) test suggested that the data were missing completely at random [χ2 (16) = 24.77, p = .07]. Considering that the Little MCAR test was trending significance, we explored potential missing data patterns at the wave and daily levels. After controlling for wave of recruitment, partial correlations indicated that younger adolescents were more likely to participate in all waves than their older-aged peers (r = −0.11, p < .05), but no relations emerged between participation and adolescents’ race (r = 0.03, p = .53), sex (r = −0.07, p = .16), and eligibility for free/reduced-priced lunch (r = −0.01, p = .89). After controlling for covariates, partial correlations indicated that study participation was unrelated to our key outcomes, such as depressive symptoms (r = −0.08, p = .11), anxiety (r = 0.01, p = .80), stress (r = −0.07, p = .13), tiredness (r = −0.06, p = .23), and online racial discrimination (r = 0.00, p = .98).
We also assessed participation rates at the daily level. Since they were first recruited into the study, adolescents on average missed 2 daily diaries, and this low rate of missingness is reflected in the daily level participation within each wave (Supplement 3, available online). After accounting for wave level participation, partial correlations indicated that daily level participation was unrelated to adolescents’ race (r = 0.03, p = .53), sex (r = −0.07, p = .10), or eligibility for free/reduced-priced lunch (r = 0.04, p = .27), but that younger adolescents completed more daily assessments than their older-aged peers (r = −0.14, p < .001). After accounting for these demographic differences, partial correlations indicated that daily level participation was unrelated to depressive symptoms (r = 0.00, p = .67), anxiety (r = −0.01, p = .29), stress (r = −0.01, p = .73), tiredness (r = −0.01, p = .36), and online racial discrimination (r = −0.01, p = .15). Considering that our data were characterized as missing at random,24 we used full information maximum likelihood to retain all 602 adolescents. In Supplement 3 and Table S2 (available online), we re-estimated our models using multiple imputation as a sensitivity analysis, and our results stayed the same.
Analytic Plan
All analyses were conducted in Mplus version 8.3,25 using TYPE=TWO LEVEL to account for the nested structure in which 58 daily assessments were nested within 351 adolescents. In doing so, we estimated multi-level models with fixed effects and a random intercept for each dependent variable and assigned time to level 1 and adolescents to level 2, while positioning the random intercepts to reflect adolescent-level intercepts of the dependent variables. The intraclass correlations presented in Table S3 (available online) justified our multi-level modeling approach. These multi-level models enabled us to adopt a quasi-experimental framework and to treat each adolescent like his/her own control group.26 Specifically, after controlling for between-person differences in online racial discrimination experiences, we examined within-person differences associated with online racial discrimination and, specifically, whether adolescents who experienced online racial discrimination at any day also experienced poor mental health symptoms relative to days when they did not experience online racial discrimination. We first examined whether the frequency of online racial discrimination changed for Black youths over the course of the 58-day study period and whether these changes operated similarly to changes in time spent online and general cybervictimization.
Next, we estimated 2 multi-level models that examined the relations between online racial discrimination and youths’ mental health at level 1. In 2 of these multi-level models, we examined whether adolescents who reported online racial discrimination also reported same-day (ie, model 1) and next-day (ie, model 2) changes relative to their own average on mental health symptoms. In both models, the 4 mental health outcomes (ie, depressive symptoms, anxiety, stress, and tiredness) were coded as continuous dependent variables, freely estimated at levels 1 and 2, and regressed on all covariates at level 1 (eg, day, weekend, last night’s sleep quality and quantity, time spent online, and online racial discrimination) and level 2 (eg, sex, age, free-lunch eligibility, and cohort). Level 1 predictors were group-mean centered, and level 2 predictors were grand-mean centered.26,27 These predictors were either continuous (ie, level 1: day, last night’s sleep quality and quantity, and time spent online; level 2: age and time spent online) or dichotomous (ie, level 1: weekend, online racial discrimination, and general cybervictimization; level 2: sex, free-lunch eligibility, cohort, online racial discrimination, and general cybervictimization).
To support our inferences, we conducted several sensitivity analyses. For instance, we excluded prior-day mental health outcomes in our analytic models, because methodologists suggest that including lagged dependent variables introduces error in multi-level models.28 Nonetheless, we tested whether our results held when we controlled for prior-day mental health outcomes in the models. In addition, to establish temporal precedence among our key constructs and to test alternative hypotheses, we examined whether poor mental health predicted next-day’s online racial discrimination perceptions and whether the pattern of findings replicated in a sample of White adolescents.
RESULTS
Descriptive Statistics
Table 1 presents descriptive statistics for online racial discrimination, alternative online experiences, and mental health symptoms. Across all 4 waves, 158 (45%) Black youths reported at least 1 instance of online racial discrimination. On average, Black youths experienced 2 incidents of online racial discrimination throughout the study period. The average count of online racial discrimination was stable between waves 1 and 2 [Δχ2(1) = 0.18, p = .67], increased between waves 2 and 3 [Δχ2(1) = 19.76, p < .001], and stabilized thereafter [Δχ2(1) = 0.50, p = .48]. Although youths’ average time spent online increased between waves 1 and 2 [Δχ2(1) = 21.66, p < .001], it was stable between waves 2 and 3 [Δχ2(1) = 0.88, p = .35] and between waves 3 and 4 [Δχ2(1) = 2.29, p = .13]. The average count of general cybervictimization experiences was stable throughout the pandemic [waves 1 and 2: Δχ2(1) = 0.64, p = .42; waves 2 and 3: Δχ2(1) = 0.06, p = .80; waves 3 and 4: Δχ2(1) = 0.11, p = .74]. In addition, the percentage of participants experiencing online racial discrimination increased from 8% at wave 1 to 22% at wave 4; this finding suggests that the increase in online racial discrimination was not solely attributable to individual youths reporting re-occurring instances of online racial discrimination across time.
Table 1.
Descriptive Statistics for Key Measures Among 351 Black Adolescents and 10,639 Daily Diaries
| Wave 1 | Wave 2 | Wave 3 | Wave 4 | ||
|---|---|---|---|---|---|
| Continuous measures | Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | Range |
| Depressive symptoms | 1.63 (0.74) | 1.58 (0.80) | 1.52 (0.73) | 1.47 (0.65) | 1–5 |
| Anxiety symptoms | 1.76 (0.76) | 1.57 (0.70) | 1.56 (0.67) | 1.62 (0.69) | 1–5 |
| Stress | 1.88 (0.58) | 1.55 (0.58) | 1.52 (0.56) | 1.69 (0.65) | 1–4 |
| Tiredness | 2.42 (0.74) | 2.10 (0.74) | 2.03 (0.69) | 2.11 (0.83) | 1–5 |
| Time spent online, h | 3.88 (2.64) | 5.21 (2.63) | 5.05 (2.61) | 4.74 (2.54) | 0–24 |
| Categorical measures | Sum or % | Sum or % | Sum or % | Sum or % | Total |
| Counts of online racial discrimination incidents | 42.00 | 85.00 | 224.00 | 318.00 | 669.00 |
| % of Participants reporting online racial discrimination | 8.00 | 13.40 | 21.10 | 21.70 | 45.00 |
| Count of cybervictimization experiences | 16.00 | 42.00 | 35.00 | 29.00 | 107.00 |
| % of Participants reporting general cybervictimization | 2.30 | 6.80 | 4.60 | 4.60 | 16.00 |
Table 2 presents zero-order bivariate correlations among key constructs that varied within-person and between-persons. Without controlling for any covariates, youths who experienced online racial discrimination also reported poor mental health symptoms (ie, depressive symptoms, anxiety, stress, and tiredness). Notably, the weak relation between online racial discrimination and cybervictimization suggests that they are distinguishable constructs.
TABLE 2.
Zero-Order Bivariate Correlations Among Key Study Variable in the Sample of 351 Black Adolescents and 10,639 Daily Diaries
| Within-person variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Online racial discrimination | 1 | ||||||||||
| 2 | Depressive symptoms | 0.15** | 1 | |||||||||
| 3 | Anxiety symptoms | 0.16** | 0.59** | 1 | ||||||||
| 4 | Stress | 0.12** | 0.43** | 0.45** | 1 | |||||||
| 5 | Tiredness | 0.08** | 0.31** | 0.30** | 0.38** | 1 | ||||||
| 6 | Time spent online | 0.10** | 0.11** | 0.08** | 0.09** | 0.06** | 1 | |||||
| 7 | Cybervictimization | 0.17** | 0.15** | 0.18** | 0.12** | 0.06** | 0.09** | 1 | ||||
| 8 | Day | 0.12** | −0.08** | −0.04** | −0.05** | −0.08** | 0.03* | −0.01 | 1 | |||
| 9 | Weekend | 0.01 | −0.04** | −0.05** | −0.08** | −0.06** | 0.01 | 0.01 | 0.005** | 1 | ||
| 10 | Last night’s sleep quality | −0.06** | −0.32** | −0.26** | −0.30** | −0.44** | −0.06** | −0.04** | 0.10** | 0.03** | 1 | |
| 11 | Last night’s sleep quantity | 0.00 | −0.10** | −0.06** | −0.05** | −0.12** | −0.04** | 0.02 | 0.47** | 0.01 | 0.16** | 1 |
|
| ||||||||||||
| Between-person variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||
| 1 | Male sex | 1 | ||||||||||
| 2 | Age | −0.12* | 1 | |||||||||
| 3 | Eligible for free lunch | −0.08 | 0.02 | 1 | ||||||||
| 4 | Cohort 2 (vs Cohort 1) | 0.09 | −0.02 | 0.00 | 1 | |||||||
| 5 | Cohort 3 (vs Cohort 1) | −0.02 | −0.05 | −0.03 | −0.38** | 1 | ||||||
| 6 | Cohort 4 (vs Cohort 1) | 0.06 | −0.13* | −0.29** | −0.25** | −0.25** | 1 | |||||
| 7 | Online racial discrimination | −0.11* | 0.10 | 0.02 | 0.03 | 0.04 | −0.05 | 1 | ||||
| 8 | Time spent online | −0.17** | 0.15** | −0.03 | 0.03 | 0.08 | −0.03 | 0.17** | 1 | |||
| 9 | Cybervictimization | −0.10 | 0.01 | −0.02 | 0.13* | −0.04 | 0.01 | 0.33** | 0.06 | 1 | ||
Note:
p < .05;
p < .01;
p < .001.
Online Racial Discrimination and Mental Health
The top half of Table 3 presents unstandardized coefficients for our multi-level models examining the same-day effects of online racial discrimination on youths’ mental health after we controlled for within-and between-person covariates. In the within-person fixed effects of the model, we found that Black adolescents who experienced online racial discrimination at any day also reported increased same-day depressive symptoms, anxiety, and stress relative to days when they did not experience online racial discrimination. Online racial discrimination was unrelated to same-day within-person changes in tiredness.
TABLE 3.
Multi-level Models Predicting Same-Day and Next-Day Mental Health Symptoms Across 58 Days Among 351 Black Adolescents and 10,639 Daily Diaries
| Same-day outcomes |
Depressive symptoms |
Anxiety |
Stress |
Tiredness |
||||
|---|---|---|---|---|---|---|---|---|
| Fixed effects | Estimate (SE) | 95% CI | Estimate(SE) | 95% CI | Estimate (SE) | 95% CI | Estimate (SE) | 95% CI |
| Within-person | ||||||||
| Day | 0.00 (0.00) | [−0.01, .00] | .00 (.00) | [−0.01, .01] | .00 (.00) | [0.00, .00] | −0.01 (.01) | [−0.01, .00] |
| Weekend | −0.07 (0.01)*** | [−0.10, −0.04] | −0.08 (.01)*** | [−0.11, −0.05] | −0.14 (.02)*** | [−0.17, −0.11] | −0.11 (.02)*** | [−0.15, −0.07] |
| Last night’s sleep quality | −0.11 (0.02)*** | [−0.14, −0.08] | −0.06 (0.01)*** | [−0.09, −0.03] | −0.10 (0.01)*** | [−0.13, −0.08] | −0.34 (0.02)*** | [−0.38, −0.29] |
| Last night’s sleep quantity | 0.00 (0.00) | [−0.01, .01] | .00 (0.00) | [−0.01, .01] | .00 (0.00) | [−0.01, .01] | −0.01 (0.00)** | [−0.01, −0.01] |
| Online racial discrimination | 0.18 (0.05)*** | [0.08, .29] | .17 (0.05)** | [0.07, .26] | .14 (0.04)** | [0.05, .22] | .07 (0.04) | [−0.01, .16] |
| Time spent online | 0.01 (0.01) | [−0.01, .03] | .00 (0.01) | [−0.02, .01] | −0.01 (0.01) | [−0.03, .01] | .01 (0.01) | [−0.01, .03] |
| Cybervictimization | 0.20 (0.13) | [−0.06, .45] | .34 (0.09)*** | [0.16, .52] | .18 (0.08)* | [0.02, .33] | .04 (0.14) | [−0.24, .32] |
| Between-person | ||||||||
| Male sex | −0.08 (0.07) | [−0.21, .05] | −0.02 (0.06) | [−0.14, .11] | .00 (0.05) | [−0.10, .11] | −0.05 (0.07) | [−0.18, .08] |
| Age | 0.10 (0.02)*** | [0.05, .15] | .07 (0.02)*** | [0.03, .11] | .05 (0.02)** | [0.02, .09] | .07 (0.02)*** | [0.03, .11] |
| Eligible for free lunch | 0.06 (0.08) | [−0.10, .21] | .07 (0.07) | [−0.07, .21] | .06 (0.06) | [−0.05, .18] | .15 (0.08) | [−0.01, .29] |
| Cohort 2 (vs cohort 1) | −0.14 (0.08) | [−0.29, .01] | −0.20 (0.07)** | [−0.34, −0.06] | −0.35 (0.06)*** | [−0.47, −0.23] | −0.25 (0.08)** | [−0.40, −0.10] |
| Cohort 3 (vs cohort 1) | 0.06 (0.13) | [−0.19, .30] | −0.09 (0.10) | [−0.29, .11] | −0.18 (0.09)* | [−0.35, −0.01] | −0.15 (0.10) | [−0.35, .05] |
| Cohort 4 (vs cohort 1) | 0.02 (0.15) | [−0.28, .31] | −0.10 (0.14) | [−0.37, .18] | −0.18 (0.13) | [−0.43, .08] | .12 (0.20) | [−0.28, .51] |
| Online racial discrimination | 0.22 (0.07)** | [0.08, .37] | .17 (0.07)** | [0.04, .30] | .11 (0.05)* | [0.01, .22] | .14 (0.07)* | [0.01, .28] |
| Time spent online | 0.02 (0.02) | [−0.01, .06] | .03 (0.02) | [−0.01, .06] | .04 (0.01)** | [0.01, .07] | .02 (0.02) | [−0.01, .06] |
| Cybervictimization | 0.19 (0.10) | [−0.01, .38] | .31 (0.11)** | [0.10, .51] | .15 (0.08) | [−0.01, .31] | .04 (0.09) | [−0.14, .21] |
| Random effects | ||||||||
| Within-person | ||||||||
| Residual-intercept | 0.42 (0.03)*** | [0.36, .47] | .39 (0.02)*** | [0.35, .43] | .43 (0.02)*** | [0.39, .48] | .70 (0.03)*** | [0.64, .76] |
| Between-person | ||||||||
| Intercept | 1.92 (0.12)*** | [1.68, 2.16] | 1.81 (0.11)*** | [1.58, 2.04] | 2.09 (0.09)*** | [1.90, 2.28] | 3.45 (0.13)*** | [3.19, 3.70] |
| Model fit indices: | χ2 (25) = 122.62, p < .001, RMSEA = 0.01, CFI = 0.98, SRMRwithin = 0.01, SRMRbetween = 0.05 | |||||||
|
| ||||||||
| Next-day outcomes |
Depressive symptoms |
Anxiety |
|
Stress |
|
Tiredness |
|
|
| Fixed effects | Estimate (SE) | 95% CI | Estimate (SE) | 95% CI | Estimate (SE) | 95% CI | Estimate (SE) | 95% CI |
| Within-person | ||||||||
| Day | 0.00 (0.00) | [0.00, 0.00] | 0.00 (0.00) | [0.00, 0.00] | 0.00 (0.00) | [0.00, 0.00] | −0.01 (0.01) | [−0.01, 0.01] |
| Weekend | −0.07 (0.01)*** | [−0.09, −0.04] | −0.08 (0.01)*** | [−0.10, −0.05] | −0.14 (0.02)*** | [−0.17, −0.11] | −0.11 (0.02)*** | [−0.15, −0.07] |
| Last night’s sleep quality | −0.11 (0.02)*** | [−0.14, −0.08] | −0.05 (0.01)*** | [−0.08, −0.03] | −0.09 (0.01)*** | [−0.12, −0.06] | −0.33 (0.02)*** | [−0.38, −0.29] |
| Last night’s sleep quantity | 0.00 (0.00) | [−0.01, 0.01] | 0.00 (0.00) | [−0.01, 0.01] | 0.00 (0.00) | [−0.01, 0.01] | −0.01 (0.00)** | [−0.01, −0.01] |
| Online racial discrimination | 0.16 (0.05)** | [0.06, 0.26] | 0.14 (0.05)** | [0.04, 0.24] | 0.16 (0.04)*** | [0.07, 0.24] | 0.01 (0.04) | [−0.07, 0.09] |
| Time spent online | 0.01 (0.01) | [−0.01, 0.02] | 0.00 (0.01) | [−0.01,0.02] | 0.00 (0.01) | [−0.01, 0.02] | 0.00 (0.01) | [−0.02, 0.02] |
| Cybervictimization | 0.19 (0.09)* | [0.01, 0.38] | 0.20 (0.08)* | [0.04, 0.36] | 0.16 (0.09) | [−0.01, 0.33] | 0.25 (0.13) | [−0.02, 0.51] |
| Between-person | ||||||||
| Male sex | −0.08 (0.07) | [−0.22, 0.06] | −0.02 (0.07) | [−0.14, 0.11] | 0.00 (0.06) | [−0.11, 0.11] | −0.04 (0.08) | [−0.19, 0.11] |
| Age | 0.11 (0.02)*** | [0.06, 0.16] | 0.08 (0.02)*** | [0.03, 0.12] | 0.06 (0.02)** | [0.03, 0.10] | 0.11 (0.02)*** | [0.06, 0.16] |
| Eligible for free lunch | .07 (0.08) | [−0.08, 0.23] | 0.08 (0.07) | [−0.06, 0.22] | 0.08 (0.06) | [−0.04, 0.20] | 0.20 (0.09)* | [0.02, 0.38] |
| Cohort 2 (vs Cohort 1) | −0.17 (0.08)* | [−0.32, −0.01] | −0.22 (0.08)** | [−0.36, −0.07] | −0.37 (0.06)*** | [−0.50, −0.24] | −0.34 (0.08)*** | [−0.50, −0.18] |
| Cohort 3 (vs Cohort 1) | 0.04 (0.13) | [−0.22, 0.30] | −0.09 (0.10) | [−0.30, 0.11] | −0.19 (0.09)* | [−0.37, −0.01] | −0.19 (0.12) | [−0.43, 0.05] |
| Cohort 4 (vs Cohort 1) | 0.00 (0.16) | [−0.31, 0.31] | −0.10 (0.15) | [−0.39, 0.18] | −0.16 (0.13) | [−0.42, 0.11] | 0.06 (0.22) | [−0.37, 0.50] |
| Online racial discrimination | 0.25 (0.07)** | [0.10, 0.40] | 0.19 (0.07)** | [0.06, 0.32] | 0.13 (0.06)* | [0.02, 0.24] | 0.21 (0.08)** | [0.06, 0.36] |
| Time spent online | 0.03 (0.01)* | [0.01, 0.06] | 0.03 (0.01)* | [0.01, 0.06] | 0.04 (0.01)** | [0.02, 0.07] | 0.04 (0.02)* | [0.01, 0.08] |
| Cybervictimization | 0.17 (0.10) | [−0.03, 0.37] | 0.31 (0.11)** | [0.10, 0.52] | 0.14 (0.08) | [−0.03, 0.31] | −0.01 (0.10) | [−0.21, 0.20] |
| Random effects | ||||||||
| Within-person | ||||||||
| Residual-intercept | 0.42 (0.03)*** | [0.36, 0.47] | 0.39 (0.02)*** | [0.35, 0.43] | 0.43 (0.02)*** | [0.39, 0.48] | 0.70 (0.03)*** | [0.64, 0.76] |
| Between-person | ||||||||
| Intercept | 1.47 (0.10)*** | [1.26, 1.67] | 1.57 (0.10)*** | [1.38, 1.76] | 1.72 (0.08)*** | [1.56, 1.89] | 2.12 (0.11)*** | [1.91, 2.33] |
| Model fit indices: | χ2 (25) = 82.93, p < .001, RMSEA = 0.01 CFI = 0.99, SRMRwithin = 0.01, SRMRbetween = 0.05 | |||||||
Note: CFI = ; RMSEA = root mean square error of approximation; SE = standard error; SRMRbetween = ; SRMRwithin = .
p < .05;
p < .01;
p < .001.
The bottom half of Table 3 presents unstandardized coefficients for our multi-level models examining the next-day effects of online racial discrimination on youths’ mental health after we controlled for within-and between-person covariates. In the within-person effects of the model, Black adolescents who experienced online racial discrimination at any day also reported increased next-day depressive symptoms, anxiety, and stress relative to days when they did not experience online racial discrimination. Again, online racial discrimination was unrelated to next-day within-person changes in tiredness.
Both models produced acceptable fit indices (Table 3), and the sizes of the within-person fixed effects were small (β-range = 0.06–0.08). Notably, in both models, online racial discrimination was associated with increased same-day and next-day depressive symptoms, anxiety, and stress, but online racial discrimination was not related to same-day and next-day tiredness. However, improved sleep quality the night before was associated with lower levels of depressive symptoms, anxiety, and stress. Thus, sleep disruption is likely an important outcome in addition to depressive symptoms, anxiety, and stress, even in the absence of tiredness as an outcome.
Sensitivity Analyses
Because there are concerns about the inclusion of lagged dependent variables,28 we examined whether our online racial discrimination continued to predict same-day and next-day mental health after we controlled for prior-day mental health to account for the autocorrelation of daily diary observations; ultimately, the pattern of results stayed the same (Table S4, available online).
We conducted additional analyses to determine whether adolescents’ poor mental health symptoms contributed to their racial discrimination experiences or made them more prone to attribute unfair treatment to racial discrimination. After controlling for our covariates, neither mental health outcome at the within-person or between-person level predicted online racial discrimination longitudinally (Table S5, available online).
In addition, we examined the degree to which the present findings extended to White adolescents. Only 61 White adolescents reported at least 1 online racial discrimination instance, and the prevalence of participants reporting online racial discrimination among White adolescents was low and stable across waves (ie, 8% in wave 1, 4% in wave 2, 7% in wave 3, and 11% in wave 4). We also found that online racial discrimination did not predict White youths’ mental health at either the within-person or between-person levels (Table S6, available online), and these results reliably differed from those found among Black youths [Δχ2(6) = 46.49, p < .001].
DISCUSSION
The present study examined whether the rate of online racial discrimination changed over the course of the racial unrest in the United States from March to November 2020. Using a national sample, we found that Black adolescents reported increases in online racial discrimination during this time. In addition, Black youths who experienced online racial discrimination reported poorer same-day and next-day mental health.
One in 2 Black youths experienced at least 1 instance of online racism during the study period. This rate is observably higher than in a prior documented study, which found that 38% of Black American adults reported at least 1 offline discrimination experience between March and June 2020.16 Our rate was expectedly higher, as we covered a wider time period and focused on rates of racial discrimination among youths in online settings. Although adolescents may have been more likely to experience online, as opposed to offline, discrimination because of public health measures associated with COVID-19 school closures and social distancing mandates, these youths may have also experienced racial discrimination in offline contexts as well. This reality unfortunately means that youths’ total exposure to racial discrimination could be even higher and may be underestimated in this study.
In addition, Black youths reported increases in online racial discrimination over time. Notably, these increases were not solely explained by increased time spent online or by general cybervictimization experiences. The reader will recall that Breonna Taylor’s and George Floyd’s deaths sparked worldwide protests over the killings of innocent and unarmed Black Americans in the hands of law enforcement personnel. Because these protests were met with opposition, youths who were attuned to such events may have also experienced such opposition as discriminatory and may have also fallen victim to unwanted online harassment.
Black youths who experienced at least 1 instance of online racial discrimination also reported poorer same-day and next-day mental health. Consistent with the literature,4,5 online discrimination that targeted youths’ racial identities likely activated threat responses and reminded Black youths about their lack of power within a racially stratified society.5 In addition, online racial discrimination may have been coupled with offline discrimination,29 such as direct and vicarious offline harassment across contexts that adolescents face throughout any given day, including school figures, peer groups, and extracurricular activities.30,31 In turn, this amalgamation of negative online and offline encounters across youths’ life courses may have led Black youths to experience heightened stress and hopelessness.10,32 Whereas past research documented nonsignificant longitudinal effects of direct online racial discrimination across a 3-year period,18 we found immediate same-day and next-day decrements in mental health following online racist events. This attention to timing suggests that there may be a critical period following racial discrimination experiences when youths’ mental health is particularly vulnerable. Critics may contend that youths’ mental health could contribute to a sensitivity to discriminatory experiences; however, our evidence did not support such a claim. Rather, our evidence indicated that online spaces may be a particularly dangerous setting for adolescents, especially considering the salient processes of racial identity development during this period.33 To prepare racially minoritized youths and their families to cope with these adverse experiences, psychiatrists and clinicians should recognize online spaces as developmental contexts with immediate consequences for youths’ mental health.34,35
No discernible effect of online racial discrimination emerged for White adolescents. This finding is not surprising, as White youths belong to a racial group that has more power, wealth, and privilege; hence their racial identity is less susceptible to threats than that of Black youths.5 Supporting this possibility, the frequency of online racial discrimination experiences was lower for White youths than for Black youths. In addition, the absence of threat to their racial identity may explain why White youths are less attuned to race and see diversity as less self-relevant, which could have resulted in White youths’ lesser vulnerability to online racial discrimination. Indeed, studies have found that White youths are less affected by race-related experiences than are their Black peers.31,36
The present study was not free of limitations. For instance, we solely examined online racial discrimination; therefore, we cannot rule out the possibility that offline discrimination also increased during this period. Second, our study was a series of daily diaries across 8 months during the COVID-19 pandemic; for this reason, we have a restricted snapshot of online racial discrimination in a particularly nuanced social context. Third, Black youths were not the only ones experiencing racism during this period, as other racial minority youths (eg, Asian Americans) have experienced racism as well.16 Future studies should investigate whether the impact of online discrimination on mental health identified in this study can be generalized to other racial minority groups. Fourth, because we recruited new participants at each wave, not all participants had the opportunity to participate in all possible waves. For this reason, some adolescents had fewer opportunities to report on online racism, and the uncertainty associated with missing data may have dampened significant relationships. Thus, the present study’s observed rate of online racial discrimination and its associated consequences are likely conservative estimates. Fifth, because our data relied primarily on self-report measures, our study is susceptible to standard concerns regarding social desirability bias. Yet, our pattern of findings aligns with studies that have used non-survey measures of discrimination and adolescents’ adjustment outcomes.37–39 Also, the intensive longitudinal nature of the present study provided us with a 58-day snapshot of youths’ experiences, which is a relatively short time period. Scholars should consider a more longterm longitudinal study to understand whether and how our findings hold over the course of adolescence. Finally, to reduce respondent burden and attrition given the intensity of the daily-diary study, some important constructs, including vicarious online racial discrimination, were not measured in the present study. Considering the health impacts associated with vicarious racism,40 scholars should examine this type of racial discrimination in future studies.
Clinical Implications
Our findings have immediate implications for clinical practice. We found that racial discrimination was associated with increases in same-day and next-day mental health among a nonclinical sample of Black adolescents. Although the magnitude of these effect sizes was relatively small, the present study’s effect sizes were similar to those found in longitudinal studies with wider time frames (eg, yearly intervals).33,41 However, our effect sizes were on average larger than those found in prior studies using daily-diary study designs.42,43 It is worth noting, however, that these prior daily-diary studies focused on offline racial discrimination only, whereas the present study examined racial discrimination in online settings. As such, youths’ chronic exposure to online settings may exacerbate the impact of racial discrimination on youths’ mental health. Importantly, these daily effects may accrue and, over time, may contribute to clinically significant levels of impairment. Because the litany of mental health issues associated with racial discrimination includes suicidality,44 clinicians should be especially attuned to the ways in which their racially minoritized adolescent patients process instances of discrimination over time, especially in the case of repeated incidents.
Given the prevalence of discrimination experienced by racial minority youths in today’s society, clinicians should receive training on culturally sensitive assessments and effective communication skills to use when patients’ racial trauma arises in clinical settings.45,46 These professionals may also benefit from systematic training in racial literacy and resources to help youths cope with racially traumatic events within communities.45,47 In addition, practitioners may want to give special attention to understanding how processes of ethnic—racial socialization have operated within families. When parents feel underprepared to discuss racism and racist events, family conversations about these topics may contribute to greater externalizing and internalizing symptomatology among youths.48 Importantly, conversations about racial discrimination would be incomplete without discussing practical approaches to cope with race-related stressors in daily life. Therefore, both clinicians and parents should consult the ethnic—racial socialization literature about how, when, and what to discuss with youths during critically important conversations about race, racism, and discrimination.
Online racial discrimination has been documented as the most frequent discrimination experience among Black youths,49 yet it has been sparingly addressed by health providers’ antiracism aefforts.34,35 During our assessment periods in 2020, we found that the frequency of online racial discrimination increased throughout the United States. In addition, online racial discrimination was linked to poorer same-day and next-day mental health among Black youths. Because of the prevalence of online racism and its associated consequences for developing youths, social media companies have a responsibility to address hate speech in online spaces. Considering that these same hate crimes are legal offenses in offline spaces, it is time to consider whether the same legal ramifications should extend to online hate crimes. In addition to these policy actions on behalf of social media platforms, health providers can play an active role in helping adolescents cope with online hate speech. Moreover, training on racial trauma is needed among mental health experts,46 and members of online communities need accessible tools that encourage the reporting of online racism.50 To address the mental health repercussions following online racial discrimination, pediatricians and clinicians should acknowledge and discuss practices that help Black youths cope with such widespread discrimination,46 because in America, it is not a question of whether these youths will encounter discrimination, but when.
Supplementary Material
Acknowledgments
We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list.
This research was supported by Grants 1315943 and 1561382 from the National Science Foundation to Dr. Wang, Grant 201600067 from the Spencer Foundation to Dr. Wang, and Grant 202100287 from the Spencer Foundation to Dr. Del Toro.
Footnotes
Disclosure: Drs. Del Toro and Wang have reported no biomedical financial interests or potential conflicts of interest.
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