We summarize our first set of results in Table 1. For all our measures, we found statistical significance (as per t tests) as well as significant effect sizes (Cohen d>0.4 for all measures [71]) in social media expressions in the treatment data as compared to that in the control data. Assuming that most other confounders were minimized due to the geo-temporal similarity of the data sets, our findings indicated that the COVID-19 outbreak led to an increase in people’s symptomatic and support expressions of mental health. We elaborate on the results in the following sections.

Figure 1 shows the changes in symptomatic mental health expressions for the same period in the treatment (2020) and control (2019) years. We found that the treatment and control data sets showed significant differences in people’s symptomatic expressions (Table 1), among which anxiety showed the most significant increase (21.32%), followed by suicidal ideation (19.73%), depression (10.18%), and stress (3.76%). Figure 2 shows the evolution of support expressions change in the treatment and control data sets. The differences were significant (Table 1), and we found that ES increased by 4.77% and IS increased by 4.78%.

In both the plots of Figures 1 and 2, we found a general trend of negative slope (average slope=–0.03) within the treatment year, which was closer to zero slope (average slope=3.19*10⁻⁴) in the control data set. This may suggest that within the treatment year, people’s mental health expressions gradually leveled out over time, despite the growing rate of COVID-19 active cases. The plots indicated that psychological expressions almost converge at the tails. This could likely be due to people’s habituation with the situation and surroundings with the passage of time [70,72], as has been observed for other crisis events [48,73]; however, this needs to be explored further. Within the control data set, we observed a sudden peak on April 28, 2019, which could be attributed to a shooting incident at a synagogue in San Diego [74]. The observations reflected that the COVID-19 pandemic has increased people’s mental health expressions on social media, aligning with other contemporary literature and media reports [8,53].

Our study suggests that social media posts during the COVID-19 pandemic contain a significantly higher frequency of symptomatic mental health and support expressions than a comparable data set from the same period in the previous year. The effect sizes and statistical differences observed in our analyses provide evidence that COVID-19 may have led to mental health concerns compared to other normative times. We also found that they topically relate to the ongoing crisis situation and include concerns such as treatment, precautionary measures, loss of jobs, school closings, stockpiling of basic livelihood necessities, feeling lonely, boredom, and tired of the restrictions and constraints put on by the ongoing pandemic. Our findings suggest that although the COVID-19 pandemic has amplified mental health risks and concerns, it may have heightened a sense of belonging and solidarity among individuals—bringing them together, raising collective awareness, and encouraging them to provide support to one another. We also found expressions of solidarity with health care and high-risk workers, suggesting that people have been considerate about these workers and have expressed desire to set up opportunities for donating to those who have lost jobs during the crisis; this also aligns with recent media reports and World Health Organization guidelines of tackling the pandemic [77,78]. Media reports have also indicated how benevolent neighbors have been, tending to their older adult neighbors by delivering their groceries and other basic necessities [79].

However, mental health experts say that, although the crisis is amplifying risk factors for suicide, the COVID-19 outbreak’s effect on individuals’ mental and emotional well-being is complex [80]. Suicide is multifaceted, and although economic loss is a risk factor, so are depression, isolation, and fear of the future. At the same time, the crisis is possibly creating a sense of belonging for individuals at risk for suicide, as stress and anxiety are normalized, and people come together to better support one another during a crisis [81,82]. Our data showed a significant impact of COVID-19 on suicidal ideation, which calls for enhanced importance of population-scale mental health care, such as using approaches like universal screening (ie, Zero Suicide Model) [83]. As Florida [84] noted in a recent article, “The long-term toll on mental health of social isolation, remote work, and economic insecurity could have impacts akin to post-traumatic stress disorder; yet, the new focus on mental health may reduce stigma and increase the availability of support services.” Indeed, the world beyond the crisis may be one in which mental health is more honestly recognized and supported.

Interestingly, we noted that our findings indicate a gradual leveling out of these expressions, both symptomatic and supportive, which may reflect a developing new normal. In February 2020, it seemed unthinkable that the white-collar workforce of many countries would soon be working solely from home; it seemed unthinkable that air travel would plummet by 96% and that all major sporting events would be called off. Indeed, epidemiologists surmise that many if not most of the changes surrounding the rhythms of our daily life are likely to fade over time, just as they did after the 1918 influenza epidemic [84]. In other words, the pandemic could make us revisit and possibly reform many of our lifestyle choices and civic roles, and the persistent discussion of the new normal may help bring order to our current turbulence. Others have argued that perhaps the crisis is a prelude to a new paradigm, as recently noted by the World Economic Forum [84,85]: “Feeling unsettled, destabilized and alone can help us empathize with individuals who have faced systematic exclusions long-ignored by society even before the rise of COVID-19 – thus stimulating urgent action to improve their condition.” We should, therefore, “revel in the discomfort of the current moment to generate a ‘new paradigm,’ not a ‘new normal.’” The leveling out trend in our data gives empirical ground to these conjectures.

Nevertheless, if robust antiviral treatments are developed and rolled out relatively quickly or if a vaccine becomes available soon enough, presumably, the changes will be short-lived, and the new normal may be temporary. However, if the pandemic comes back in larger waves over the next few seasons, as was the case with historical epidemics, the economic, political, and social crises that have arisen as a consequence will lead to deeper ramifications in turn, leading to longer lasting or permanent changes. Future research will need to explore the persistence of the new normal and the emergence of a possible new paradigm as the pandemic evolves, and therein the mental health impacts further along in the crisis. A study like ours on the ongoing pandemic is a step toward leveraging large-scale online data to understand people’s response to the crisis and thinking about means to address the major concerns. Our study bears implications in digital technology driven mental health interventions to provide tailored support to people’s concerns during the crisis; a recent work by Rudd and Beidas [86] pointed out four point guidelines to build innovative and expansive solutions toward improving public mental health. The variety of concerns and help-seeking factors reflected in our study can also help several stakeholders, ranging across mental health facilitators and policy makers, toward early preparedness and interventions for mental health support. Similarly, our methods can offer the potential to build public health surveillance technologies that surface early warning signs of the effects of the various events related to the pandemic and other crises. The potential of social media to assist in the response to the pandemic is clear but also dependent on the accuracy of underlying methods. The reach of social media allows for broad access that transcends national borders or cultural differences. Using this access to meet the increasing need for help seeking, online and social media data is in a prime position to offer people personalized guidance toward accurate information, health care resources, and even basic lifestyle interventions. Underlying this potential is, thus, the ability of social media data to classify the state and needs of each individual and use that information to tailor a customized response. Precedents for such a system are abound as seen in several prior studies [27-31,37,87-89].

COVID-19 is not the first pandemic—catastrophic pandemics have been occurring at regular intervals throughout human history, with the 1918 influenza epidemic being the last one before the current pandemic [84,85,90]. The backdrop of the 1918 pandemic was that it happened just before the advent of modern psychiatry as a science and a clinical specialty—a time when psychoanalysis was gaining recognition as an established treatment within the medical community [91,92]. Consequently, psychiatry has had little opportunity to consider such historically important phenomena through its clinical, scientific lens until now. Although outbreaks of the Zika and Ebola viruses, Middle East respiratory syndrome, and severe acute respiratory syndrome managed to draw global attention, stirring up anxiety and uncertainty in societies, scholars have noted that participation of mental health experts in pandemic preparedness has remained negligible [93]. Consequently, our ability to understand mental health responses as well as the mental health burden in pandemic outbreaks have been limited [94]. For instance, a routinely practiced method of infection control, quarantine and social distancing, have received surprisingly little attention in psychiatric literature so far. Baumeister and Leary [95] contended that humans need frequent contacts, and crisis events further stimulate a need for affiliation and intimacy. Therefore, prolonged isolation and separation from families and their community can have profound effects on individuals even if they are not directly affected by the disease [4]. In the current pandemic, the additional layer of extensive social media use and exposure to often sensationalized online news while in physical isolation may add new complexities to implementing emotional epidemiology in managing concerns, fears, and misconceptions [96,97], as these tools have been argued to bear negative effects on psychological well-being [97,98].

By adopting social media as a lens to unpack these previously less understood dimensions of a pandemic’s mental health effects, our study is one step toward closing some of the previously noted gaps. The published literature posits that the distress and anxiety among individuals in this COVID-19 pandemic may increase the incidence of mental disorders [53,54,99]; data thus far from the United States point to a population increase in psychological distress of 10% compared to 2018 data [8], a trend that is in line with our results. These rates may be higher in those regions heavily exposed to COVID-19 or among individuals working during the pandemic, with a recent review reporting over 20% prevalence of anxiety, which is also consistent with our findings [8].

Prior work found that mental health discourse on Twitter ranges across stigmatizing, inspirational, resource, medical, and social dimensions of expressions [100], and our study revealed similar topical diversity in our data set. Further, we detected through social media many of the stresses associated with the pandemic (eg, prolonged isolation, exposure to pandemic-related death, loss of income and career, increased workload, and lack of pertinent and accurate information). These results align with epidemiological findings that COVID-19 has led to elevated mental health symptoms for individuals. Nelson et al [101] surveyed 2000 individuals from the United States, Canada, and Europe, and found elevated symptoms of anxiety and depression compared to historical norms and observed factors similar to the concerns we detected regarding symptomatic expressions and those related to seeking support. They also reinforced the summary data released by the Crisis Text Line (a major crisis helpline in the United States) listing major concerns of crisis support sought during this period [102], with 80% of conversations mentioning “virus,” 34% mentioning “anxiety,” 34% feeling solidarity with friends and family, etc. Along similar lines, there have been numerous reports about the increasing number of mental health crisis helpline calls during this period [103,104], providing further support and external validation that our social media findings reflect many of the same elements of distress expressed offline during this crisis.

Next, our temporal analyses pointed to a steady decline in people’s expressed psychosocial concerns during the 2-month study period (Figures 1 and 2), which conforms with similar findings in Google search queries as stay-at-home orders and other COVID-19–related policy changes were implemented in the United States [105]. We note contemporary social computing research studying various aspects of the social media discourse related to COVID-19 [63,106-108]. By providing complementary evidence to observations by Mackey et al [106] and Stokes et al [107] on expressed (mental health) concerns during the crisis, our study further underscores their findings using a comparable (control) data set, reinforcing and providing empirical credibility to the impression that the COVID-19 pandemic has indeed caused or contributed directly to the mental health concerns that we describe.

We note some limitations in our study, many of which present excellent directions for future research. We recognize the lack of transparency related to the Twitter Streaming API. Recent research has also questioned the credibility of the “1% Twitter stream” aspect, noting that actual sampling data was smaller than what it ideally should have been [41]. Given these data limitations, we decided against conducting several descriptive and fine-grained analyses (such as comparing regions), and refrained from making claims based on comparing absolute numbers of those impacted by various mental health concerns. For example, we cannot define based on our data whether there were increased or decreased Twitter postings during our COVID-19 study period compared to the same months in 2019. Again, we chose to filter English-only Twitter posts given both algorithmic limitations of our methodologies and lower prevalence of non-English data (particularly in the US context). However, future work can extend our methodologies to conduct analyses in other languages to draw richer insights.

Despite the strengths of Twitter as a data source that provides us unobtrusive access to large-scale, unstructured, and naturalistic data of people’s candid self-disclosure and that it has been a valuable source to study disaster and crisis response [109], we acknowledge that this data inherently has many biases such as self-selection and representation [110]. We can only study those who self-select to express on Twitter. Pew Internet Center surveys reported that social media platforms are underrepresentative of minorities, although Twitter is an exception, which overrepresents minorities such as Blacks, Hispanics, and women [20]. There is already a digital divide in terms of social media use where the population is skewed toward young adults and white-collar workers. Further, technology and social media could be a luxury to marginalized and underprivileged populations, and any sort of technology-driven support and assistance will disproportionately affect different individuals based on technology use [88]. Similarly, a single platform cannot provide a complete picture; different platforms (eg, Facebook, Reddit, Twitter, and instant messaging services) have unique design strengths and weaknesses both in terms of their affordances as well as who uses them. Therefore, as highlighted in a recent article by Chunara and Cook [23], public health surveillance (including that for COVID-19) can account for several factors such as the “population at risk” in epidemiology and demographic disparities in the use and behavioral expressions on social media.

We understand that our study is observational and, as any other observational study, does not measure “true causality.” Watts [111] noted the impossibility to test all explanations and confounders simultaneously. However, by including and comparing against control data, we minimized geo-temporal and seasonal confounds, thereby enabling us to provide stronger evidence and insights than purely correlational analyses regarding the effects of the COVID-19 pandemic on people’s mental health. We also note that support expressions in our study can not only include support-seeking but also support-providing expressions. This has also enabled us to observe how solidarity and sense of belonging proliferated during the COVID-19 crisis. Future work can build separate high precision classifiers for each kind of expression to disentangle the prevalence of seeking and providing expressions during the crisis.

Further, although we did have data beyond May 24, 2020, we decided to exclude those to keep our focus on the effects on social media expressions due to COVID-19 and minimize those that followed the death of George Floyd on May 25, 2020, in the light of the Black Lives Matter protests throughout the United States [112]. We also are aware that, with the continuing nature of the pandemic, our conclusions are restricted to the mental health and support-seeking concerns expressed during a finite study period. Events since the end of the study period underscore the dynamic nature of these events, as different parts of the United States are heavily affected, while others are recovering, and some remain relatively spared. It will be important to extend this work temporally; increase the size of future samples; and, whenever possible, add geospatial specificity to future analyses. The latter will be especially important for potential supportive interventions locally if one has the resources and the ability to assemble recurring, near-real-time local “snapshots” as a basis for community-focused preventive interventions. Further, our analyses can be extended to retrospectively examine the aftereffects of particular global and local events, such as policy changes, related to the pandemic.

Our study, like those of others studying other major events, further reinforces the potential utility of accessing and analyzing social media data in near-real-time to take the temperature of communities. This will require a more focused and robust collection of locally targeted information to build samples that are sufficiently large to produce reliably representative data sets to be useful for public health interventions. Further work is now needed to track mental health–related expressions and those reflecting needs for support throughout the pandemic, which has seen dynamic changes associated with disease spread to areas that had been less affected during the early months of the outbreak. This geo-specific research may further enhance our understanding of the causal connections between COVID-19 spread and waves of expressed distress. Having the ability to present locally pertinent, contemporaneous analyses offers the opportunity for local public health and mental health providers as well as political leaders to develop and deploy targeted support services in a timely fashion.