Tracking Fluctuations in Psychological States Using Social Media Language: A Case Study of Weekly Emotion

Personality psychologists are increasingly documenting dynamic, within‐person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality‐relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text‐based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human‐annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self‐reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model‐predicted emotion and self‐reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology’s evolution into a dynamic science. © 2020 European Association of Personality PsychologyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163564/3/per2261-sup-0001-Open_Practices_Disclosure_Form.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163564/2/per2261.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163564/1/per2261_am.pd

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Big data methods, social media, and the psychology of entrepreneurial regions: capturing cross-county personality traits and their impact on entrepreneurship in the USA

There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated via social media) to help understand economic outcomes. But can artificial intelligence models based on publicly available Big Data identify geographical differences in entrepreneurial personality or culture? We use a machine learning model based on 1.5 billion tweets by 5.25 million users to estimate the Big Five personality traits and an entrepreneurial personality profile for 1,772 U.S. counties. The Twitter-based personality estimates show substantial relationships to county-level entrepreneurship activity, accounting for 20% (entrepreneurial personality profile) and 32% (Big Five traits) of the variance in local entrepreneurship, even when controlling for other factors that affect entrepreneurship. Whereas more research is clearly needed, our findings have initial implications for research and practice concerned with entrepreneurial regions and eco-systems, and regional economic outcomes interacting with local culture. The results suggest, for example, that social media datasets and artificial intelligence methods have the potential to deliver comparable information on the personality and culture of regions than studies based on millions of questionnaire-based personality tests

Lifestyle and wellbeing: Exploring behavioral and demographic covariates in a large US sample

Using data from a nationally representative sample of 46,179 US adults from the Gallup-Healthways Wellbeing Index, we investigate covariates of four subjective mental wellbeing dimensions spanning evaluative (life satisfaction), positive affective (happiness), negative affective (worry), and eudaimonic wellbeing. Negative covariates were generally more strongly correlated with the four dimensions than positive covariates, with depression, poor health, and loneliness being the greatest negative correlates and excellent health and older age being the greatest positive correlates. We reproduce previous evidence for a “midlife crisis” around age 50 across the four wellbeing dimensions. Notably, although salutogenic behaviors (diet, exercise, socializing) correlated with greater wellbeing, there were diminishing benefits beyond thresholds of about four hours a day spent socializing, four days per week of consuming fruits and vegetables, and four days per week of exercising. Findings suggest that wellbeing is easier lost than gained, underscore the influence that relatively malleable lifestyle factors have on wellbeing, and stress the importance of multidimensional measurement for public policy

Genetic counselling and testing in pulmonary arterial hypertension: a consensus statement on behalf of the International Consortium for Genetic Studies in PAH

Pulmonary arterial hypertension (PAH) is a rare disease that can be caused by (likely) pathogenic germline genomic variants. In addition to the most prevalent disease gene, BMPR2 (bone morphogenetic protein receptor 2), several genes, some belonging to distinct functional classes, are also now known to predispose to the development of PAH. As a consequence, specialist and non-specialist clinicians and healthcare professionals are increasingly faced with a range of questions regarding the need for, approaches to and benefits/risks of genetic testing for PAH patients and/or related family members. We provide a consensus-based approach to recommendations for genetic counselling and assessment of current best practice for disease gene testing. We provide a framework and the type of information to be provided to patients and relatives through the process of genetic counselling, and describe the presently known disease causal genes to be analysed. Benefits of including molecular genetic testing within the management protocol of patients with PAH include the identification of individuals misclassified by other diagnostic approaches, the optimisation of phenotypic characterisation for aggregation of outcome data, including in clinical trials, and importantly through cascade screening, the detection of healthy causal variant carriers, to whom regular assessment should be offered

Of Roots and Fruits: A Comparison of Psychedelic and Nonpsychedelic Mystical Experiences

Experiences of profound existential or spiritual significance can be triggered reliably through psychopharmacological means using psychedelic substances. However, little is known about the benefits of religious, spiritual, or mystical experiences (RSMEs) prompted by psychedelic substances, as compared with those that occur through other means. In this study, 739 self-selected participants reported the psychological impact of their RSMEs and indicated whether they were induced by a psychedelic substance. Experiences induced by psychedelic substances were rated as more intensely mystical ( d = .75, p &lt; .001), resulted in a reduced fear of death ( d = .21, p &lt; .01), increased sense of purpose ( d = .18, p &lt; .05), and increased spirituality ( d = .28, p &lt; .001) as compared with nonpsychedelically triggered RSMEs. These results remained significant in an expanded model controlling for gender, education, socioeconomic status, and religious affiliation. These findings lend support to the growing consensus that RSMEs induced with psychedelic substances are genuinely mystical and generally positive in outcome. </jats:p

Early treatment with ambrisentan of mildly elevated mean pulmonary arterial pressure associated with systemic sclerosis: a randomized, controlled, double-blind, parallel group study (EDITA study)

OBJECTIVE: The objective of this randomized, placebo-controlled, double-blind, parallel group, trial was to assess the effect of ambrisentan on mean pulmonary arterial pressure (mPAP) in patients with systemic sclerosis (SSc) and mildly elevated pulmonary hypertension (PH). METHODS: Thirty-eight SSc patients with mildly elevated mPAP at rest between 21 and 24 mmHg and/or > 30 mmHg during low-dose exercise were randomly assigned to treatment with either ambrisentan 5-10 mg/day or placebo. Right heart catheterization and further clinical parameters were assessed at baseline and after 6 months. The primary endpoint was the difference of mPAP change at rest between groups. RESULTS: After 6 months, the two groups did not differ in the primary endpoint (ambrisentan mPAP - 1 ± 6.4 mmHg vs. placebo - 0.73 ± 3.59 mmHg at rest, p = 0.884). However, three patients from the placebo group but none of the ambrisentan group progressed to SSc-associated pulmonary arterial hypertension. Furthermore, ambrisentan treatment showed significant improvements in the secondary endpoints cardiac index (CI) and pulmonary vascular resistance (PVR) at rest (CI 0.36 ± 0.66 l/min/m2 vs. - 0.31 ± 0.71 l/min/m2, p = 0.010; PVR - 0.70 ± 0.78 WU vs. 0.01 ± 0.71 WU, p = 0.012) and during exercise (CI 0.7 ± 0.81 l/min/m2 vs. - 0.45 ± 1.36 l/min/m2, p = 0.015; PVR - 0.84 ± 0.48 WU vs. - 0.0032 ± 0.34 WU, p < 0.0001). CONCLUSION: This is the first randomized, double-blind, placebo-controlled study testing the effect of ambrisentan in patients with mildly elevated mPAP and/or exercise PH. The primary endpoint change in mPAP did only tendentially improve in the ambrisentan group, but the significant improvement of other hemodynamic parameters points to a possible benefit of ambrisentan and will be helpful to design future trials. TRIAL REGISTRATION: www.ClinicalTrials.gov, unique identifier NCT: NCT02290613 , registered 14th of November 2014

Do 2H and 18O in leaf water reflect environmental drivers differently?

We compiled hydrogen and oxygen stable isotope compositions (δ H and δ O) of leaf water from multiple biomes to examine variations with environmental drivers. Leaf water δ H was more closely correlated with δ H of xylem water or atmospheric vapour, whereas leaf water δ O was more closely correlated with air relative humidity. This resulted from the larger proportional range for δ H of meteoric waters relative to the extent of leaf water evaporative enrichment compared with δ O. We next expressed leaf water as isotopic enrichment above xylem water (Δ H and Δ O) to remove the impact of xylem water isotopic variation. For Δ H, leaf water still correlated with atmospheric vapour, whereas Δ O showed no such correlation. This was explained by covariance between air relative humidity and the Δ O of atmospheric vapour. This is consistent with a previously observed diurnal correlation between air relative humidity and the deuterium excess of atmospheric vapour across a range of ecosystems. We conclude that H and O in leaf water do indeed reflect the balance of environmental drivers differently; our results have implications for understanding isotopic effects associated with water cycling in terrestrial ecosystems and for inferring environmental change from isotopic biomarkers that act as proxies for leaf water