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Background: This study leveraged machine learning to evaluate the contribution of information from multiple developmental stages to prospective prediction of depression and anxiety in mid-adolescence. Methods: A community sample (N = 374; 53.5% male) of children and their families completed tri-annual assessments across ages 3–15. The feature set included several important risk factors spanning psychopathology, temperament/personality, family environment, life stress, interpersonal relationships, neurocognitive, hormonal, and neural functioning, and parental psychopathology and personality. We used canonical correlation analysis (CCA) to reduce the large feature set to a lower dimensional space while preserving the longitudinal structure of the data. Ablation analysis was conducted to evaluate the relative contributions to prediction of information gathered at different developmental periods and relative to previous disorder status (i.e. age 12 depression or anxiety) and demographics (sex, race, ethnicity). Results: CCA components from individual waves predicted age 15 disorder status better than chance across ages 3, 6, 9, and 12 for anxiety and 9 and 12 for depression. Only the components from age 12 for depression, and ages 9 and 12 for anxiety, improved prediction over prior disorder status and demographics. Conclusions: These findings suggest that screening for risk of adolescent depression can be successful as early as age 9, while screening for risk of adolescent anxiety can be successful as early as age 3. Assessing additional risk factors at age 12 for depression, and going back to age 9 for anxiety, can improve screening for risk at age 15 beyond knowing standard demographics and disorder history. [ABSTRACT FROM AUTHOR]

Recent research on artificial intelligence has demonstrated that natural language can be used to provide valid indicators of psychopathology. The present study examined artificial intelligence-based language predictors (ALPs) of seven trauma-related mental and physical health outcomes in responders to the World Trade Center disaster. The responders (N = 174, M age = 55.4 years) provided daily voicemail updates over 14 days. Algorithms developed using machine learning in large social media discovery samples were applied to the voicemail transcriptions to derive ALP scores for several risk factors (depressivity, anxiousness, anger proneness, stress, and personality). Responders also completed self-report assessments of these risk factors at baseline and trauma-related mental and physical health outcomes at two-year follow-up (including symptoms of depression, posttraumatic stress disorder, sleep disturbance, respiratory problems, and GERD). Voicemail ALPs were significantly associated with a majority of the trauma-related outcomes at two-year follow-up, over and above corresponding baseline self-reports. ALPs showed significant convergence with corresponding self-report scales, but also considerable uniqueness from each other and from self-report scales. The study has a relatively short follow-up period relative to trauma occurrence and a limited sample size. This study shows evidence that ALPs may provide a novel, objective, and clinically useful approach to forecasting, and may in the future help to identify individuals at risk for negative health outcomes. [ABSTRACT FROM AUTHOR]

Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way. [ABSTRACT FROM AUTHOR]

Forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (ED) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of Facebook statuses and electronic medical records. An ensemble machine learning model forecasted ED visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits. [ABSTRACT FROM AUTHOR]

The words that people use have been found to reflect stable psychological traits, but less is known about the extent to which everyday fluctuations in spoken language reflect transient psychological states. We explored within-person associations between spoken words and self-reported state emotion among 185 participants who wore the Electronically Activated Recorder (EAR; an unobtrusive audio recording device) and completed experience sampling reports of their positive and negative emotions 4 times per day for 7 days (1,579 observations). We examined language using the Linguistic Inquiry and Word Count program (LIWC; theoretically created dictionaries) and open-vocabulary themes (clusters of data-driven semantically-related words). Although some studies give the impression that LIWC's positive and negative emotion dictionaries can be used as indicators of emotion experience, we found that when computed on spoken language, LIWC emotion scores were not significantly associated with self-reports of state emotion experience. Exploration of other categories of language variables suggests a number of hypotheses about substantive everyday correlates of momentary positive and negative emotion that can be tested in future studies. These findings (a) suggest that LIWC positive and negative emotion dictionaries may not capture self-reported subjective emotion experience when applied to everyday speech, (b) emphasize the importance of establishing the validity of language-based measures within one's target domain, (c) demonstrate the potential for developing new hypotheses about personality processes from the open-ended words that are used in everyday speech, and (d) extend perspectives on intraindividual variability to the domain of spoken language. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and it is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. Judges rated the temporal orientation of 4,302 social media messages. We trained a classifier based on these ratings, which could accurately predict the temporal orientation of new messages in a separate validation set (accuracy/mean sensitivity = .72; mean specificity = .77). We used the classifier to automatically classify 1.3 million messages written by 5,372 participants (50% female; ages 13-48). Finally, we tested whether individual differences in past, present, and future orientation differentially related to gender, age, Big Five personality, satisfaction with life, and depressive symptoms. Temporal orientations exhibit several expected correlations with age, gender, and Big Five personality. More future-oriented people were older, more likely to be female, more conscientious, less impulsive, less depressed, and more satisfied with life; present orientation showed the opposite pattern. Language-based assessments can complement and extend existing measures of temporal orientation, providing an alternative approach and additional insights into language and personality relationships. [ABSTRACT FROM AUTHOR]

Language use is a psychologically rich, stable individual difference with well-established correlations to personality. We describe a method for assessing personality using an open-vocabulary analysis of language from social media. We compiled the written language from 66,732 Facebook users and their questionnaire-based self-reported Big Five personality traits, and then we built a predictive model of personality based on their language. We used this model to predict the 5 personality factors in a separate sample of 4,824 Facebook users, examining (a) convergence with self-reports of personality at the domain- and facet-level; (b) discriminant validity between predictions of distinct traits; (c) agreement with informant reports of personality; (d) patterns of correlations with external criteria (e.g., number of friends, political attitudes, impulsiveness); and (e) test-retest reliability over 6-month intervals. Results indicated that language-based assessments can constitute valid personality measures: they agreed with self-reports and informant reports of personality, added incremental validity over informant reports, adequately discriminated between traits, exhibited patterns of correlations with external criteria similar to those found with self-reported personality, and were stable over 6-month intervals. Analysis of predictive language can provide rich portraits of the mental life associated with traits. This approach can complement and extend traditional methods, providing researchers with an additional measure that can quickly and cheaply assess large groups of participants with minimal burden. [ABSTRACT FROM AUTHOR]

We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase ‘sick of’ and the word ‘depressed’), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive ‘my’ when mentioning their ‘wife’ or ‘girlfriend’ more often than females use ‘my’ with ‘husband’ or 'boyfriend’). To date, this represents the largest study, by an order of magnitude, of language and personality. [ABSTRACT FROM AUTHOR]

Music is a universal phenomenon that has existed in every known culture around the world. It plays a prominent role in society by shaping sociocultural interactions between groups and individuals, and by influencing their emotional and intellectual life. Here, we provide evidence for a new theory on musical preferences. Across three studies we show that people prefer the music of artists who have publicly observable personalities ("personas") similar to their own personality traits (the "self-congruity effect of music"). Study 1 (N = 6,279) and Study 2 (N = 75,296) show that the public personality of artists correlates with the personality of their listeners. Study 3 (N = 4,995) builds on this by showing that the fit between the personality of the listener and the artist predicts musical preferences incremental to the fit for gender, age, and even the audio features of music. Our findings are largely consistent across two methodological approaches to operationalizing an artist's public personality: (a) the public personality as reported by the artist's fans, and (b) the public personality as predicted by machine learning on the basis of the artist's lyrics. We discuss the importance of the self-congruity effect of music in the context of group-level process theories and adaptionist accounts of music. [ABSTRACT FROM AUTHOR]

• Artificial intelligence has been undergoing a purported "paradigm shift" initiated by new machine learning models, large language models. • We review recent empirical evaluations of AI-based language assessments and present a case for the technology of large language models, that are used for chatGPT and BERT, to be poised for changing standardized psychological assessment. • While potential applications for automated therapy are beginning to be studied on the heels of chatGPT's success, here we present evidence that suggests, with thorough validation of targeted deployment scenarios, that AI's newest technology can move mental health assessment away from rating scales and to instead use how people naturally communicate, in language. In this narrative review, we survey recent empirical evaluations of AI-based language assessments and present a case for the technology of large language models to be poised for changing standardized psychological assessment. Artificial intelligence has been undergoing a purported "paradigm shift" initiated by new machine learning models, large language models (e.g., BERT, LAMMA, and that behind ChatGPT). These models have led to unprecedented accuracy over most computerized language processing tasks, from web searches to automatic machine translation and question answering, while their dialogue-based forms, like ChatGPT have captured the interest of over a million users. The success of the large language model is mostly attributed to its capability to numerically represent words in their context, long a weakness of previous attempts to automate psychological assessment from language. While potential applications for automated therapy are beginning to be studied on the heels of chatGPT's success, here we present evidence that suggests, with thorough validation of targeted deployment scenarios, that AI's newest technology can move mental health assessment away from rating scales and to instead use how people naturally communicate, in language. [ABSTRACT FROM AUTHOR]

The COVID pandemic placed a spotlight on alcohol use and the hardships of working within the food and beverage industry, with millions left jobless. Following previous studies that have found elevated rates of alcohol problems among bartenders and servers, here we studied the alcohol use of bartenders and servers who were employed during COVID. From February 12-June 16, 2021, in the midst of the U.S. COVID national emergency declaration, survey data from 1,010 employed bartender and servers were analyzed to quantify rates of excessive or hazardous drinking along with regression predictors of alcohol use as assessed by the 10-item Alcohol Use Disorders Identification Test (AUDIT). Findings indicate that more than 2 out of 5 (44%) people surveyed reported moderate or high rates of alcohol problem severity (i.e., AUDIT scores of 8 or higher)–a rate 4 to 6 times that of the heavy alcohol use rate reported pre- or mid-pandemic by adults within and outside the industry. Person-level factors (gender, substance use, mood) along with the drinking habits of one's core social group were significantly associated with alcohol use. Bartenders and servers reported surprisingly high rates of alcohol problem severity and experienced risk factors for hazardous drinking at multiple ecological levels. Being a highly vulnerable and understudied population, more studies on bartenders and servers are needed to assess and manage the true toll of alcohol consumption for industry employees. [ABSTRACT FROM AUTHOR]

Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates.Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Share care Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up tor=0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used. [ABSTRACT FROM AUTHOR]

The words that people use have been found to reflect stable psychological traits, but less is known about the extent to which everyday fluctuations in spoken language reflect transient psychological states. We explored within-person associations between spoken words and self-reported state emotion among 185 participants who wore the Electronically Activated Recorder (EAR; an unobtrusive audio recording device) and completed experience sampling reports of their positive and negative emotions 4 times per day for 7 days (1,579 observations). We examined language using the Linguistic Inquiry and Word Count program (LIWC; theoretically created dictionaries) and open-vocabulary themes (clusters of data-driven semantically-related words). Although some studies give the impression that LIWC’s positive and negative emotion dictionaries can be used as indicators of emotion experience, we found that when computed on spoken language, LIWC emotion scores were not significantly associated with self-reports of state emotion experience. Exploration of other categories of language variables suggests a number of hypotheses about substantive everyday correlates of momentary positive and negative emotion that can be tested in future studies. These findings (a) suggest that LIWC positive and negative emotion dictionaries may not capture self-reported subjective emotion experience when applied to everyday speech, (b) emphasize the importance of establishing the validity of language-based measures within one's target domain, (c) demonstrate the potential for developing new hypotheses about personality processes front the open-ended words that are used in everyday speech, and (d) extend perspectives on intraindividual variability to the domain of spoken language. [ABSTRACT FROM AUTHOR]

Opioid poisoning mortality is a substantial public health crisis in the United States, with opioids involved in approximately 75% of the nearly 1 million drug related deaths since 1999. Research suggests that the epidemic is driven by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and isolation. Hindering this research is a lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolutions. To address this issue, we use a multi-modal data set consisting of natural language from Twitter, psychometric self-reports of depression and well-being, and traditional area-based measures of socio-demographics and health-related risk factors. Unlike previous work using social media data, we do not rely on opioid or substance related keywords to track community poisonings. Instead, we leverage a large, open vocabulary of thousands of words in order to fully characterize communities suffering from opioid poisoning, using a sample of 1.5 billion tweets from 6 million U.S. county mapped Twitter users. Results show that Twitter language predicted opioid poisoning mortality better than factors relating to socio-demographics, access to healthcare, physical pain, and psychological well-being. Additionally, risk factors revealed by the Twitter language analysis included negative emotions, discussions of long work hours, and boredom, whereas protective factors included resilience, travel/leisure, and positive emotions, dovetailing with results from the psychometric self-report data. The results show that natural language from public social media can be used as a surveillance tool for both predicting community opioid poisonings and understanding the dynamic social and psychological nature of the epidemic. [ABSTRACT FROM AUTHOR]

Friends and spouses tend to be similar in a broad range of characteristics, such as age, educational level, race, religion, attitudes, and general intelligence. Surprisingly, little evidence has been found for similarity in personality--one of the most fundamental psychological constructs. We argue that the lack of evidence for personality similarity stems from the tendency of individuals to make personality judgments relative to a salient comparison group, rather than in absolute terms (i.e., the reference-group effect), when responding to the self-report and peer-report questionnaires commonly used in personality research. We employed two behavior-based personality measures to circumvent the referencegroup effect. The results based on large samples provide evidence for personality similarity between romantic partners in = 1,101; rs = .20-.47) and between friends in = 46,483; rs = .12--.31). We discuss the practical and methodological implications of the findings. [ABSTRACT FROM AUTHOR]

Background: Oral histories from 9/11 responders to the World Trade Center (WTC) attacks provide rich narratives about distress and resilience. Artificial Intelligence (AI) models promise to detect psychopathology in natural language, but they have been evaluated primarily in non-clinical settings using social media. This study sought to test the ability of AI-based language assessments to predict PTSD symptom trajectories among responders. Methods: Participants were 124 responders whose health was monitored at the Stony Brook WTC Health and Wellness Program who completed oral history interviews about their initial WTC experiences. PTSD symptom severity was measured longitudinally using the PTSD Checklist (PCL) for up to 7 years post-interview. AI-based indicators were computed for depression, anxiety, neuroticism, and extraversion along with dictionary-based measures of linguistic and interpersonal style. Linear regression and multilevel models estimated associations of AI indicators with concurrent and subsequent PTSD symptom severity (significance adjusted by false discovery rate). Results: Cross-sectionally, greater depressive language (β = 0.32; p = 0.049) and first-person singular usage (β = 0.31; p = 0.049) were associated with increased symptom severity. Longitudinally, anxious language predicted future worsening in PCL scores (β = 0.30; p = 0.049), whereas first-person plural usage (β = −0.36; p = 0.014) and longer words usage (β = −0.35; p = 0.014) predicted improvement. Conclusions: This is the first study to demonstrate the value of AI in understanding PTSD in a vulnerable population. Future studies should extend this application to other trauma exposures and to other demographic groups, especially under-represented minorities. [ABSTRACT FROM AUTHOR]

Using a large social media dataset and open-vocabulary methods from computational linguistics, we explored differences in language use across gender, affiliation, and assertiveness. In Study 1, we analyzed topics (groups of semantically similar words) across 10 million messages from over 52,000 Facebook users. Most language differed little across gender. However, topics most associated with self-identified female participants included friends, family, and social life, whereas topics most associated with self-identified male participants included swearing, anger, discussion of objects instead of people, and the use of argumentative language. In Study 2, we plotted male- and female-linked language topics along two interpersonal dimensions prevalent in gender research: affiliation and assertiveness. In a sample of over 15,000 Facebook users, we found substantial gender differences in the use of affiliative language and slight differences in assertive language. Language used more by self-identified females was interpersonally warmer, more compassionate, polite, and—contrary to previous findings—slightly more assertive in their language use, whereas language used more by self-identified males was colder, more hostile, and impersonal. Computational linguistic analysis combined with methods to automatically label topics offer means for testing psychological theories unobtrusively at large scale. [ABSTRACT FROM AUTHOR]

Background: Early indicators of who will remain in - or leave - treatment for substance use disorder (SUD) can drive targeted interventions to support long-term recovery.Objectives: To conduct a comprehensive study of linguistic markers of SUD treatment outcomes, the current study integrated features produced by machine learning models known to have social-psychology relevance.Methods: We extracted and analyzed linguistic features from participants' Facebook posts (N = 206, 39.32% female; 55,415 postings) over the two years before they entered a SUD treatment program. Exploratory features produced by both Linguistic Inquiry and Word Count (LIWC) and Latent Dirichlet Allocation (LDA) topic modeling and the features from theoretical domains of religiosity, affect, and temporal orientation via established AI-based linguistic models were utilized.Results: Patients who stayed in the SUD treatment for over 90 days used more words associated with religion, positive emotions, family, affiliations, and the present, and used more first-person singular pronouns (Cohen's d values: [-0.39, -0.57]). Patients who discontinued their treatment before 90 days discussed more diverse topics, focused on the past, and used more articles (Cohen's d values: [0.44, 0.57]). All ps < .05 with Benjamini-Hochberg False Discovery Rate correction.Conclusions: We confirmed the literature on protective and risk social-psychological factors linking to SUD treatment in language analysis, showing that Facebook language before treatment entry could be used to identify the markers of SUD treatment outcomes. This reflects the importance of taking these linguistic features and markers into consideration when designing and recommending SUD treatment plans. [ABSTRACT FROM AUTHOR]

Objective: We explore the personality of counties as assessed through linguistic patterns on social media. Such studies were previously limited by the cost and feasibility of large‐scale surveys; however, language‐based computational models applied to large social media datasets now allow for large‐scale personality assessment. Method: We applied a language‐based assessment of the five factor model of personality to 6,064,267 U.S. Twitter users. We aggregated the Twitter‐based personality scores to 2,041 counties and compared to political, economic, social, and health outcomes measured through surveys and by government agencies. Results: There was significant personality variation across counties. Openness to experience was higher on the coasts, conscientiousness was uniformly spread, extraversion was higher in southern states, agreeableness was higher in western states, and emotional stability was highest in the south. Across 13 outcomes, language‐based personality estimates replicated patterns that have been observed in individual‐level and geographic studies. This includes higher Republican vote share in less agreeable counties and increased life satisfaction in more conscientious counties. Conclusions: Results suggest that regions vary in their personality and that these differences can be studied through computational linguistic analysis of social media. Furthermore, these methods may be used to explore other psychological constructs across geographies. [ABSTRACT FROM AUTHOR]

Objective: We present a new open language analysis approach that identifies and visually summarizes the dominant naturally occurring words and phrases that most distinguished each Big Five personality trait. Method: Using millions of posts from 69,792 Facebook users, we examined the correlation of personality traits with online word usage. Our analysis method consists of feature extraction, correlational analysis, and visualization. Results: The distinguishing words and phrases were face valid and provide insight into processes that underlie the Big Five traits. Conclusion: Open-ended data driven exploration of large datasets combined with established psychological theory and measures offers new tools to further understand the human psyche [ABSTRACT FROM AUTHOR]

We introduce a new method, differential language analysis (DLA), for studying human development in which computational linguistics are used to analyze the big data available through online social media in light of psychological theory. Our open vocabulary DLA approach finds words, phrases, and topics that distinguish groups of people based on 1 or more characteristics. Using a data set of over 70,000 Facebook users, we identify how word and topic use vary as a function of age and compile cohort specific words and phrases into visual summaries that are face valid and intuitively meaningful. We demonstrate how this methodology can be used to test developmental hypotheses, using the aging positivity effect (Carstensen & Mikels, 2005) as an example. While in this study we focused primarily on common trends across age-related cohorts, the same methodology can be used to explore heterogeneity within developmental stages or to explore other characteristics that differentiate groups of people. Our comprehensive list of words and topics is available on our web site for deeper exploration by the research community. [ABSTRACT FROM AUTHOR]

We show that using a recent break-through in artificial intelligence –transformers–, psychological assessments from text-responses can approach theoretical upper limits in accuracy, converging with standard psychological rating scales. Text-responses use people's primary form of communication –natural language– and have been suggested as a more ecologically-valid response format than closed-ended rating scales that dominate social science. However, previous language analysis techniques left a gap between how accurately they converged with standard rating scales and how well ratings scales converge with themselves – a theoretical upper-limit in accuracy. Most recently, AI-based language analysis has gone through a transformation as nearly all of its applications, from Web search to personalized assistants (e.g., Alexa and Siri), have shown unprecedented improvement by using transformers. We evaluate transformers for estimating psychological well-being from questionnaire text- and descriptive word-responses, and find accuracies converging with rating scales that approach the theoretical upper limits (Pearson r = 0.85, p < 0.001, N = 608; in line with most metrics of rating scale reliability). These findings suggest an avenue for modernizing the ubiquitous questionnaire and ultimately opening doors to a greater understanding of the human condition. [ABSTRACT FROM AUTHOR]

In March 2020, residents of the Bronx, New York experienced one of the first significant community COVID-19 outbreaks in the United States. Focusing on intensive longitudinal data from 78 Bronx-based older adults, we used a multi-method approach to (1) examine 2019 to early pandemic (February-June 2020) changes in momentary psychological well-being of Einstein Aging Study (EAS) participants and (2) to contextualize these changes with community distress scores collected from public Twitter posts posted in Bronx County. We found increases in mean loneliness from 2019 to 2020; and participants that were higher in neuroticism had greater increases in thought unpleasantness and feeling depressed. Twitter-based Bronx community scores of anxiety, depressivity, and negatively-valenced affect showed elevated levels in 2020 weeks relative to 2019. Integration of EAS participant data and community data showed week-to-week fluctuations across 2019 and 2020. Results highlight how community-level data can characterize a rapidly changing environment to supplement individual-level data at no additional burden to individual participants. [ABSTRACT FROM AUTHOR]

Background: Much of the population fails to meet recommended physical activity (PA) levels, but there remains considerable individual variation. By understanding drivers of different trajectories, interventions can be better targeted and more effective. One such driver may be a person's physical activity identity (PAI)-the extent to which a person perceives PA as central to who they are.Methods: Using survey information and a unique body of essays written at age 11 from the National Child Development Study (N = 10 500), essays mentioning PA were automatically identified using the machine learning technique support vector classification and PA trajectories were estimated using latent class analysis. Analyses tested the extent to which childhood PAI correlated with activity levels from age 23 through 55 and with trajectories across adulthood.Results: 42.2% of males and 33.5% of females mentioned PA in their essays, describing active and/or passive engagement. Active PAI in childhood was correlated with higher levels of activity for men but not women, and was correlated with consistently active PA trajectories for both genders. Passive PAI was not related to PA for either gender.Conclusions: This study offers a novel approach for analysing large qualitative datasets to assess identity and behaviours. Findings suggest that at as young as 11 years old, the way a young person conceptualizes activity as part of their identity has a lasting association with behaviour. Still, an active identity may require a supportive sociocultural context to manifest in subsequent behaviour. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to determine whether words used in medical school admissions essays can predict physician empathy. Methods: A computational form of linguistic analysis was used for the content analysis of medical school admissions essays. Words in medical school admissions essays were computationally grouped into 20 'topics' which were then correlated with scores on the Jefferson Scale of Empathy. The study sample included 1,805 matriculants (between 2008- 2015) at a single medical college in the North East of the United States who wrote an admissions essay and completed the Jefferson Scale of Empathy at matriculation. Results: After correcting for multiple comparisons and controlling for gender, the Jefferson Scale of Empathy scores significantly correlated with a linguistic topic (r = .074, p< .05). This topic was comprised of specific words used in essays such as "understanding," "compassion," "empathy," "feeling," and "trust." These words are related to themes emphasized in both theoretical writing and empirical studies on physician empathy. Conclusions: This study demonstrates that physician empathy can be predicted from medical school admission essays. The implications of this methodological capability, i.e. to quantitatively associate linguistic features or words with psychometric outcomes, bears on the future of medical education research and admissions. In particular, these findings suggest that those responsible for medical school admissions could identify more empathetic applicants based on the language of their application essays. [ABSTRACT FROM AUTHOR]

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients’ consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients’ Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions. [ABSTRACT FROM AUTHOR]

Information about a person’s income can be useful in several business-related contexts, such as personalized advertising or salary negotiations. However, many people consider this information private and are reluctant to share it. In this paper, we show that income is predictable from the digital footprints people leave on Facebook. Applying an established machine learning method to an income-representative sample of 2,623 U.S. Americans, we found that (i) Facebook Likes and Status Updates alone predicted a person’s income with an accuracy of up to r = 0.43, and (ii) Facebook Likes and Status Updates added incremental predictive power above and beyond a range of socio-demographic variables (ΔR2 = 6–16%, with a correlation of up to r = 0.49). Our findings highlight both opportunities for businesses and legitimate privacy concerns that such prediction models pose to individuals and society when applied without individual consent. [ABSTRACT FROM AUTHOR]

Beck's insight-that beliefs about one's self, future, and environment shape behavior-transformed depression treatment. Yet environment beliefs remain relatively understudied. We introduce a set of environment beliefs-primal world beliefs or primals-that concern the world's overall character (e.g., the world is interesting, the world is dangerous). To create a measure, we systematically identified candidate primals (e.g., analyzing tweets, historical texts, etc.); conducted exploratory factor analysis (N = 930) and two confirmatory factor analyses (N = 524; N = 529); examined sequence effects (N = 219) and concurrent validity (N = 122); and conducted test-retests over 2 weeks (n = 122), 9 months (n = 134), and 19 months (n = 398). The resulting 99-item Primals Inventory (PI-99) measures 26 primals with three overarching beliefs-Safe, Enticing, and Alive (mean α = .93)-that typically explain ∼55% of the common variance. These beliefs were normally distributed; stable (2 weeks, 9 months, and 19 month test-retest results averaged .88, .75, and .77, respectively); strongly correlated with many personality and wellbeing variables (e.g., Safe and optimism, r = .61; Enticing and depression, r = -.52; Alive and meaning, r = .54); and explained more variance in life satisfaction, transcendent experience, trust, and gratitude than the BIG 5 (3%, 3%, 6%, and 12% more variance, respectively). In sum, the PI-99 showed strong psychometric characteristics, primals plausibly shape many personality and wellbeing variables, and a broad research effort examining these relationships is warranted. (PsycINFO Database Record (c) 2018 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from behavioral data—language use—at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use. [ABSTRACT FROM AUTHOR]

Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures. [ABSTRACT FROM AUTHOR]

Objectives: The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. Methods: Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. Results: Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. ‘ready gettin leave’) can explain much of the variance associated between socioeconomics and excessive alcohol consumption. Conclusions: Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained. [ABSTRACT FROM AUTHOR]

Little is known about how real-time online rating platforms such as Yelp may complement the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, which is the US standard for evaluating patients' experiences after hospitalization. We compared the content of Yelp narrative reviews of hospitals to the topics in the HCAHPS survey, called domains in HCAHPS terminology. While the domains included in Yelp reviews covered the majority of HCAHPS domains, Yelp reviews covered an additional twelve domains not found in HCAHPS. The majority of Yelp topics that most strongly correlate with positive or negative reviews are not measured or reported by HCAHPS. The large collection of patient- and caregiver-centered experiences found on Yelp can be analyzed with natural language processing methods, identifying for policy makers the measures of hospital quality that matter most to patients and caregivers. The Yelp measures and analysis can also provide actionable feedback for hospitals. [ABSTRACT FROM AUTHOR]