Visual attention toward emotional stimuli: Anxiety symptoms correspond to distinct gaze patterns.

Decades of research have established a link between emotional disorders and attentional biases for emotional stimuli, but the relationship between symptom severity and visual attention is still not fully understood. Depression has been associated with increased attention towards dysphoric stimuli and decreased attention on positive stimuli ("negativity bias"), and some studies have also shown this trend in anxiety disorders. We examined eye fixation variables in 47 participants with emotional disorders completing an emotion recognition task. Results showed that depression severity was not associated with increased fixations on dysphoric stimuli, however, higher levels of generalized anxiety predicted increased fixations in the mouth region of sad and happy faces. Higher levels of social interaction anxiety predicted reduced fixations in the eye region of happy faces. While we did not replicate the negativity bias that has been shown in prior studies, our sample was highly comorbid, indicating the need to consider comorbidity, disorder severity, and the task itself when conducting research on visual attention in clinical samples. Additionally, more attention should be paid to the mouth region of emotional faces, as it may provide more specific information regarding the visual processing of emotions

Beyond Risk: Individual Mental Health Trajectories from Large-Scale Social Media Data

Depression and anxiety are now the 1st and 10th leading causes of disability worldwide. However, their variegated presentation and symptoms complicate efforts to develop a better understanding of the complex factors that shape the dynamics of their development within individuals. The development of personalized detection, diagnostics, and treatment options has been hindered by the lack of within-subject longitudinal observations at high temporal resolution and for large samples of individuals across the spectrum of internalizing disorders. Here, we discuss our efforts in the burgeoning field of precision mental health which leverages large-scale data of the behavioral, cognitive, emotional and social traces that billions of individuals leave behind when they interact with social media platforms. Our results point towards the possibility of modeling individualized mental health trajectories at population scale to identify high-precision targets for the detection, intervention, and mitigation of internalizing disorders

Social Media Insights Into US Mental Health During the COVID-19 Pandemic:Longitudinal Analysis of Twitter Data

Background: The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world's mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population's mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being. Objective: This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic? Methods: We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. Results: LDA topics generated in the early months of the data set corresponded to major COVID-19-specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March. Conclusions: Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts

Declining well-being during the COVID-19 pandemic reveals US social inequities

Background The COVID-19 pandemic led to mental health fallout in the US; yet research about mental health and COVID-19 primarily rely on samples that may overlook variance in regional mental health. Indeed, between-city comparisons of mental health decline in the US may provide further insight into how the pandemic is disproportionately affecting at-risk groups. Purpose This study leverages social media and COVID-19-city infection data to measure the longitudinal (January 22- July 31, 2020) mental health effects of the COVID-19 pandemic in 20 metropolitan areas. Methods We used longitudinal VADER sentiment analysis of Twitter timelines (January-July 2020) for cohorts in 20 metropolitan areas to examine mood changes over time. We then conducted simple and multivariate Ordinary Least Squares (OLS) regressions to examine the relationship between COVID-19 infection city data, population, population density, and city demographics on sentiment across those 20 cities. Results Longitudinal sentiment tracking showed mood declines over time. The univariate OLS regression highlighted a negative linear relationship between COVID-19 city data and online sentiment (β = -.017). Residing in predominantly white cities had a protective effect against COVID-19 driven negative mood (β = .0629, p < .001). Discussion Our results reveal that metropolitan areas with larger communities of color experienced a greater subjective well-being decline than predominantly white cities, which we attribute to clinical and socioeconomic correlates that place communities of color at greater risk of COVID-19. Conclusion The COVID-19 pandemic is a driver of declining US mood in 20 metropolitan cities. Other factors, including social unrest and local demographics, may compound and exacerbate mental health outlook in racially diverse cities.Applied Probabilit

Negative affect variability differs between anxiety and depression on social media.

ObjectiveNegative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications.MethodParticipants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder (A cohort, n = 896) or a depressive disorder (D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content.ResultsFindings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p LimitationsOur sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect.ConclusionsIndividuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale