The pushshift (version 4.1) Python (version 3.9.0) package was used to extract all public posts made between January 1, 2020 and January 31, 2021 from the r/LGBTeens subreddit (n=38,389 posts). We chose this online community because of its popularity as a community for LGBTQ+ youth and its specific focus on teens. Because we aimed to assess how users’ textual expressions manifested amid global events, not in response to others’ posts, comments were excluded from the data set. Although the anonymity of Reddit prevented us from accessing demographic information about the r/LGBTeens community, Reddit users live predominantly in the United States (49.3%) [23]. Notably, given the integral role of the United States in promoting LGBTQ+ rights in foreign policy, even LGBTQ+ youth outside the United States are impacted by the erosion of US LGBTQ+ advocacy [22].

To understand whether r/LGBTeens emotional patterns were specific to LGBTQ+ teenagers, we compared the trajectory of emotional tone in r/LGBTeens posts with those in 2 other subreddit microcommunities. After a review of relevant subreddits, we determined that r/Teenagers was the largest subreddit community, with n=1,364,980 posts, tailored toward a wide population of teens. To investigate LGBTeens post sentiment relative to widespread interpersonal relationship turmoil during the COVID-19 crisis [3], we compared the emotional tones of r/LGBTeens and r/Teenagers posts to that in the average of r/Relationships posts over time; r/Relationships was the largest subreddit dedicated to posts about interpersonal relationships (n=193,282).

This study only used information that could be accessed freely by the public. This study did not include any personally identifiable information. The institutional review board recognized that analysis of publicly available data does not constitute research on human participants. Thus, ethical review approval was not required for this study.

To track negative emotions over time, we analyzed aggregate post sentiment using the Linguistic Inquiry and Word Count program (LIWC) [24]. LIWC is a computerized coding tool that analyzes psychological processes (eg, positive and negative emotions) in texts by calculating the percentage of words in prevalidated lexicons relative to all words in a text. For example, we might find that 22 of 230 (9.56%) words in a post were words related to anxiety (eg, “scared” or “stressed”), and the program would assign that particular post an anxiety score of 9.56.

We focused on levels of anger, sadness, and anxiety present in posts because these psychological processes are symptoms of COVID-19–induced mental health challenges. For example, COVID-19 health threats and uncertainty may trigger feelings of anxiety [25-27]. Similarly, the COVID-19 pandemic has presented persistent stressful stimuli that some individuals may react to with anger [28-30]. Current research has demonstrated a positive correlation between the perceived threat of COVID-19 and moods of anger and hostility [31].

Likewise, the loss of loved ones, feelings of isolation, and routine disruptions associated with the rapidly changing COVID-19 pandemic may trigger feelings of sadness. For example, a recent study [32] showed a link between increased perceived loneliness and depression symptoms. Similarly, a longitudinal study [33], conducted with children in the United Kingdom, demonstrated a substantial increase in childhood depression symptoms compared to childhood depression symptoms before lockdowns began. As anonymous support forums serve as space for individuals to build community resilience in times of crisis [34], we analyzed the overall emotional tone of posts (positive vs negative) using LIWC to understand the potential positive influences of events (eg, the US presidential election of Joe Biden, a supporter of LGBTQ+ rights) on LGBTQ+ youth mental health [35]. Furthermore, we holistically investigated the relative emotional valence of texts over time. To investigate if patterns of emotion were specific to members of the LGBTQ+ youth community, or general to the population, we compared the trajectory of LIWC emotional tone scores of r/LGBTeens posts to those of 2 other subreddit spaces.

We explored differences in the trajectory of emotions displayed in posts by visualizing trends. In addition, we recorded salient events during the crisis to examine how they may have affected the changing patterns of COVID-related user responses and associated emotions.

We marked 10 major events in the course of the COVID-19 crisis. Events were selected if they considerably disrupted LBGTQ+ youth lifestyles (eg, widespread school closures) [36,37] or if they affected the rights of the wider LBGTQ+ community [38-41]. According to the Centers for Disease Control and Prevention, the first US coronavirus cases emerged on January 21, 2020 [36]. In mid-February, many school campuses were closed temporarily to assess the severity of the virus [37]. Permanent campus closure then followed on March 16, 2020 [37]. By May 7, 2020, many schools announced that they were extending web-based learning for the remainder of 2020 [37]. School closures and shifts toward web-based learning represented a turbulent time for many adolescents during the pandemic. In addition, Black Lives Matter protests in the US peaked in June 2020, which garnered support from LGBTQ+ Pride celebrations around the world [38-40]. Black Lives Matter and Pride were followed by widespread political unrest for LGBTQ+ populations, including a change by the Trump administration that eliminated nondiscrimination protections for LGBTQ+ health care on August 8, 2020, as well as the Supreme Court nomination of Amy Coney Barrett [41]. We marked both events, as well as the election of President Biden, an advocate of LGBTQ+ populations when compared to President Trump [35,41]. Finally, we marked the January 6 Capitol riots, which threatened advocates of LGBTQ+ rights as well as President Biden’s inauguration [35].

We extrapolated meaningful conversation topics related to trends in anxiety, anger, sadness, as well as, overall emotional tone. We conducted 2-tailed independent sample t tests to determine whether the mean levels of anxiety, sadness, and anger were significantly different before and after March 21, 2020, when COVID-19 social distancing orders were enacted. To check for homogeneity of variance, we ran Levene tests for each of the 3 variables. Although results revealed the variance of sadness (F_1,39352=0.11, P=.74) and anger (t₃₉₃₅₂=−0.57, P=.57) met the assumption, the variance of anxiety (F_1,39352=37.99, P<.001) violated the assumption. We ran a 2-tailed Mann-Whitney 2-sample rank-sum test to supplement the results.

The results of the 2-tailed independent t tests suggested differences in the mean of anger (t₃₉₃₅₂=−0.57, P=.57) and sadness (t₃₉₃₅₂=−0.34, P=.74) were not meaningfully different before and during the widespread social distancing orders. However, results revealed the mean of anxiety was significantly higher during social distancing measures (t_17672.02=−7.94, P<.001). The distribution of anxiety before widespread social distancing orders was meaningfully different from the distribution after (U=125657033, z=−6.20, P<.001). To supplement the conclusion that anxiety levels decreased as a function of lifestyle stressors related to the COVID-19 crisis, we plotted the anxiety levels of posts with anxiety-related topics from January 1, 2015 through January 31, 2021. We found an unprecedented upsurge between January 1, 2020 and January 1, 2021 (Multimedia Appendix 1). Next, we employed latent Dirichlet allocation (LDA) to examine topics provoking this shift in anxiety.

LDA topic modeling is a bag-of-words machine learning algorithm that extrapolates meaningful topics from a large body of texts, in this case, subreddit posts [42]. Notably, we excluded posts with 0 LIWC anxiety from the LDA model. In line with the LDA procedures and guidelines outlined by prior researchers, we preprocessed the textual data and selected the optimal number of topics, and then human coders labeled those topics [43].

To generate a bag of words for the LDA model, we preprocessed the texts by tokenizing the text, removing stop words, lemmatizing the text, and generating a document term matrix. Tokenization separates sentences into bags of unordered words by removing all punctuation and making words lowercase. Given our relatively small sample size (n=7882 texts), lemmatization was necessary to reduce model noise. Lemmatizing texts removes prefixes and suffixes by transforming all words to their base lemma (eg, we converted “vaccinated” and “vaccinating” to “vaccine”). Only nouns were retained through the process of lemmatization because other parts of speech were not meaningful to our topics.

We set β to learn the asymmetric prior from the data [44]. Because multiple topics may have appeared in a single subreddit post, we set α=.9 to reflect the nuanced distribution of topics per text. To identify the optimal number (k) of topics for the model (Multimedia Appendix 2), we used the perplexity metric (the normalized log-likelihood of the model finding a previously unseen term), in which lower perplexity values suggest greater model accuracy. To keep the model parsimonious, we compared the perplexity values of models with k=1 to 30 topics and selected the 11 topic model because it yielded the lowest k value relative to the lowest perplexity value (−7.68).

Topics defined by the model require human labeling. LDA generates a list of the most relevant 30 terms, along with each term’s β value (ie, their relative contribution to that topic) (Multimedia Appendix 3). The algorithm also identifies posts that belong primarily to a single topic. Two coders individually inspected the top 10 most relevant posts and the 30 terms with the highest β values, and then used these terms to label each topic. Human coders deemed 1 topic incoherent (Multimedia Appendix 4).

A third human coder validated the labels by reviewing the top 10 most relevant posts for each topic and confirming the human coder–assigned labels are present in those posts. The third human coder confirmed that the incoherent topic was indeed incoherent (Multimedia Appendix 5).

We quantified the proportion of angry sentiment observed in r/LGBTeens posts relative to each post’s total number of words. Of the 39,389 posts in the data set, 17.67% (6961) were classified as containing anger. The mean percentage of words denoting anger relative to the total number of words in an anger-flagged post was 4.72% (SD 8.81%). There was an upsurge of anger following widespread school closures in the United States, and a second upsurge when many schools announced closures would last through the end of 2020 (Figure 1). Anger increased significantly again after the rise of the Black Lives Matter protests. More specifically, anger levels almost doubled between the Black Lives Matter protests on June 7, 2020 and when Biden won the US presidential election on November 7, 2020 [45,46]. The upsurge of anger may have been a result of frustration surrounding the Trump administration’s stance on LGBTQ+ and racial injustice [47,48], which resulted in policies such as the elimination of nondiscrimination protections for LGBTQ+ health care on August 8, 2020 [41].

We also quantified the proportion of sad sentiment observed in r/LGBTeens posts relative to the total number of words in each post (Figure 2). Sadness appeared in 14.4% (5687/39,389) of the posts in the data set (mean 3.82, SD 8.47). There was an upsurge of posts with high sad sentiment when the Trump administration enacted a change that eliminated nondiscrimination protections for LGBTQ+ health care on August 8, 2020 [41]. We found another increase in sadness when Amy Coney Barrett was appointed to the US Supreme Court [41]. However, following the election of President Biden—an advocate of LGBTQ+ rights when compared to President Trump [35,41]—there was a decrease in sadness until the Capitol riots on January 6, when sadness levels sharply increased. Sadness levels decreased again after President Biden’s first week in office, perhaps because of positive feelings about President Biden’s pro-LGBTQ+ policies [35].

Of the 39,389 posts in the data set, 20.01% (7881) were classified as containing anxiety (anxiety level: mean 4.97, SD 10.43). Although all 3 negative emotions analyzed in r/LGBTeens posts increased over time, anxiety trended upward the most sharply. The histograms reveal a sharp spike in anxiety, sadness, and anger in the first week of May 2020, which may reflect emotional distress resulting from US schools closing for the remainder of the school year (Figure 3). Another upsurge in anxiety occurred in late October, potentially a result of concern surrounding the future of LGBTQ+ constitutional rights following the Supreme Court appointment of Amy Coney Barrett [41]. This spike in anxiety flattened after President Biden won the US presidential election but spiked again on January 6, 2021, which was the day of the US Capitol riot [49].

To supplement the conclusion that the mental health of r/LGBTeens community members had been adversely impacted by the COVID-19 crisis and concurrent social and political unrest, we measured the overall emotional tone of each post from the expanded timeline of January 1, 2015 through January 31, 2021. Emotional tone is measured by LIWC as the valence of texts (ie, whether a text is positively valenced or negatively valenced) [24].

Of all posts encompassed in the 5-year period (n=123,440), the average post valence was negative (mean 44.33, SD 35.11, SEM 0.10, minimum 1.00, maximum 99.00, skewness 0.53, kurtosis −1.25). We found that posts became more negatively valenced throughout 2020 (Figure 4).

We compared the emotional tone of r/LGBTeens posts from January 1, 2020 to January 31, 2021 (n=38,389 posts) to the emotional tone of r/Teenagers (n=1,364,980) and r/Relationships (n=193,282) posts from the same time period (Figure 5). The mean emotional sentiment of r/LGBTeens posts was 42.34 (SD 34.68, SEM 0.18, minimum 1.00, maximum 99.00). Overall post valence in the r/Teenagers subreddit (n=1,364,980 posts) was negative (mean 37.4, SD 32.95, SEM 0.03, minimum 1.00, maximum 99.00, skewness 0.94, kurtosis −0.52). Overall post valence in the r/Relationships subreddit (n=193,282) was negative (mean 28.38, SD 15.57, SEM 0.04, minimum 1.00, maximum 99.00, skewness 2.27, kurtosis 6.84).

A 2-tailed independent samples t test was conducted to examine whether the difference in mean emotional sentiment between the r/LGBTeens and r/Relationships posts were statistically significant. Prior to the main analyses, the assumption of homogeneity of variance was checked; the results of a Levene test for r/Relationships and r/LGBTeens emotional tone was significant based on α=.05 (F_1,1403967=37821.73, P<.001), indicating the assumption was violated. The result of the 2-tailed independent samples t-test suggested the mean emotional sentiment of r/LGBTeens and r/Relationships posts were significantly different based on α=.05 (t_42204.64=77.99, P<.001). Since the assumption of assumption of homogeneity of variance was violated, a Mann-Whitney U Test, which does not have any distributional assumptions, was conducted to supplement the t test results. The result of the 2-tailed Mann-Whitney U test examining differences between r/LGBTeens and r/Relationships posts was significant based on α=.05 (U=4301049386.5, z=−Inf, P<.001), suggesting that the distribution of emotional tone in r/LGBTeens posts was significantly different than that in r/Relationships.

Additionally, a 2-tailed independent samples t test was conducted to compare the mean emotional sentiment of r/LGBTeens and r/Teenagers posts. The results of the Levene test comparing r/teenagers and r/LGBTeens posts also indicated the variances of tone for r/LGBTeens and r/Relationships posts were unlikely to be equal based on α=.05 (F_1,232265=37821.73, P<.001). The results of the 2-tailed independent sample t test suggested the mean emotional sentiment of r/LGBTeens and r/teenagers posts were also significantly different, based on α=.05 (t_41019.96=27.86, P<.001). The homogeneity of variance violation necessitated a Mann-Whitney U Test to supplement the t test results; results were significant based on α=.05 (U=28602717011.5, z=−27.23, P<.001), suggesting their distributions were significantly different.

Findings revealed that the emotional sentiment of r/LGBTeens posts (mean 42.34, SD 34.68) was significantly greater than those of r/Teenagers posts (mean 37.4, SD 32.95) and r/Relationships posts (mean 28.38, SD 15.57).

We found a decrease in emotional tone in late January in both r/Teenagers and r/LGBTeens posts when many schools closed temporarily (Figure 5). This downturn was followed by a second decrease in r/LGBTeens and r/Teenagers tone when public schools announced that closures would last through 2020. Then, there was a sharp rise in emotional tone during the Black Lives Matter protests and simultaneous Pride celebrations for r/LGBTeens posts but not for r/Teenagers posts. After Pride celebrations and Black Lives Matter protests in June, the trend for r/Teenagers posts seemed to stabilize. However, we saw a tone decrease in r/LGBTeens posts, representing more negatively valenced posts, which persisted until a second prominent increase in early November 2020. This November upsurge may be explained by the election of President Biden, who has a pro-LGBTQ+ agenda in contrast to his predecessor [35,37,47].

Furthermore, we ran a point biserial correlation analysis to assess whether negative emotion changed as US social distancing orders were relaxed and vaccine distribution increased in January 1, 2021 [50]. LIWC positive emotion calculates the frequency of positive emotion lexicon words (eg, “happy” or “thankful”) relative to the total number of words in a post. To supplement our results, we likewise assessed whether positive emotion changed as US social distancing orders were gradually lifted. While there were no significant differences in anger and sadness, results showed a significant increase in anxiety after social distance orders were reduced in January of 2021 (r=0.01, P=.04, 95% CI 0 to 0.02). Similarly, we found a decrease in positive emotion after social distancing orders began to be lifted (r=−0.01, P=.04, 95% CI −0.02 to 0). This decrease in positive emotion and increase in anxiety may be because—regardless of the vaccine becoming available on December 10, 2020 and reduced social distancing orders—youth were not immediately impacted (eg, the vaccine was not approved for youth ages 12 to 16 years until May of 2021 [51]). We would expect that this trend might flatten out in Fall 2021—when students begin to return to normalcy (Multimedia Appendix 6).

Because the distribution of anxiety before widespread social distancing orders was meaningfully different from the distribution after lockdown (U=125657033, z=−6.20, P<.001), we used a topic model to extrapolate the specific conversation topics co-occurring with LGBTQ+ teen anxiety. Findings revealed the following conversation topics among the LGBTQ+ youth during the pandemic accompany sharp increases in anxiety: attraction to a friend, coming out, coming out to family, discrimination, education, exploring sexuality, gender pronouns, love/relationship advice, starting a new relationship, and struggling with mental health. There was no meaningful change in the anxiety-provoking topics discussed over time (Multimedia Appendix 7); however, the frequency of such discussions increased (Figure 6).