Comparing the language style of heads of state in the US, UK, Germany and Switzerland during COVID-19.

The COVID-19 pandemic posed a global threat to nearly every society around the world. Individuals turned to their political leaders to safely guide them through this crisis. The most direct way political leaders communicated with their citizens was through official speeches and press conferences. In this report, we compare psychological language markers of four different heads of state during the early stage of the pandemic. Specifically, we collected all pandemic-related speeches and press conferences delivered by political leaders in the USA (Trump), UK (Johnson), Germany (Merkel), and Switzerland (Swiss Federal Council) between February 27th and August 31st, 2020. We used natural language analysis to examine language markers of expressed positive and negative emotions, references to the community (we-talk), analytical thinking, and authenticity and compare these language markers across the four nations. Level differences in the language markers between the leaders can be detected: Trump's language was characterized by a high expression of positive emotion, Merkel's by a strong communal focus, and Johnson's and the Swiss Federal Council by a high level of analytical thinking. Overall, these findings mirror different strategies used by political leaders to deal with the COVID-19 pandemic.

Quantifying the narrative flow of imagined versus autobiographical stories.

Lifelong experiences and learned knowledge lead to shared expectations about how common situations tend to unfold. Such knowledge of narrative event flow enables people to weave together a story. However, comparable computational tools to evaluate the flow of events in narratives are limited. We quantify the differences between autobiographical and imagined stories by introducing sequentiality, a measure of narrative flow of events, drawing probabilistic inferences from a cutting-edge large language model (GPT-3). Sequentiality captures the flow of a narrative by comparing the probability of a sentence with and without its preceding story context. We applied our measure to study thousands of diary-like stories, collected from crowdworkers, about either a recent remembered experience or an imagined story on the same topic. The results show that imagined stories have higher sequentiality than autobiographical stories and that the sequentiality of autobiographical stories increases when the memories are retold several months later. In pursuit of deeper understandings of how sequentiality measures the flow of narratives, we explore proportions of major and minor events in story sentences, as annotated by crowdworkers. We find that lower sequentiality is associated with higher proportions of major events. The methods and results highlight opportunities to use cutting-edge computational analyses, such as sequentiality, on large corpora of matched imagined and autobiographical stories to investigate the influences of memory and reasoning on language generation processes.

Evaluating voice samples as a potential source of information about personality.

Speech is a powerful medium through which a variety of psychologically relevant phenomena are expressed. Here we take a first step in evaluating the potential of using voice samples as non-self-report measures of personality. In particular, we examine the extent to which linguistic and vocal information extracted from semi-structured vocal samples can be used to predict conventional measures of personality. We extracted 94 linguistic features (using Linquistic Inquiry Word Count, 2015) and 272 vocal features (using pyAudioAnalysis) from 614 voice samples of at least 50 words. Using a two-stage, fully automatable machine learning pipeline we evaluated the extent to which these features predicted self-report personality scales (Big Five Inventory). For comparison purposes, we also examined the predictive performance of these voice features with respect to depression, age, and gender. Results showed that voice samples accounted for 10.67 % of the variance in personality traits on average and that the same samples could also predict depression, age, and gender. Moreover, the results reported here provide a conservative estimate of the degree to which features derived from voice samples could be used to predict personality traits and suggest a number of opportunities to optimize personality prediction and better understand how voice samples carry information about personality.

Tracking group identity through natural language within groups.

To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a-1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students' likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates-Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members' joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.

Social Media Discussions Predict Mental Health Consultations on College Campuses.

The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. To address this gap, researchers and practitioners have encouraged the use of passive technologies. Social media is one such "passive sensor" that has shown potential as a viable "passive sensor" of mental health. However, the construct validity and in-practice reliability of computational assessments of mental health constructs with social media data remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011-2016, and collected 66,000 posts from the university's Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r = 0.86 and SMAPE = 13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students' mental health, particularly their mental health treatment needs.

Climate Denial Fuels Climate Change Discussions More Than Local Climate-Related Disasters.

Most scientists agree that climate change is the largest existential threat of our time. Despite the magnitude of the threat, surprisingly few climate-related discussions take place on social media. What factors drive online discussions about climate change? In this study, we examined the occurrence of Reddit discussions around three types of climate-related events: natural disasters (e.g., hurricanes, wildfires), political events (i.e., 2016 United States Presidential election), and policy events (i.e., United States' withdrawal from Paris Climate Agreement, release of IPCC report). The objective was to understand how different types of events influence collective action as measured by discussions of climate change. Six large US cities were selected based on the occurrence of at least one locally-relevant natural disaster since 2014. Posts (N = 4.4 million) from subreddits of the selected cities were collected to obtain a six-month period before and after local natural disasters as well as climate-related political and policy events (which applied equally to all cities). Climate change discussions increased significantly for all three types of events, with the highest discussion during the 2016 elections. Further, discussions returned to baseline levels within 2 months following natural disasters and policy events but continued at elevated rates for up to 4 months following the 2016 elections. The findings suggest that collective discussions on climate change are driven more by political leaders' controversial positions than life-threatening local natural disasters themselves. Implications for collective action are discussed.

Social media conversations reveal large psychological shifts caused by COVID-19's onset across U.S. cities.

The current research chronicles the unfolding of the early psychological impacts of coronavirus disease 2019 (COVID-19) by analyzing Reddit language from 18 U.S. cities (200,000+ people) and large-scale survey data (11,000+ people). Large psychological shifts were found reflecting three distinct phases. When COVID-19 warnings first emerged ("warning phase"), people's attentional focus switched to the impending threat. Anxiety levels surged, and positive emotion and anger dropped. In parallel, people's thinking became more intuitive rather than analytic. When lockdowns began ("isolation phase"), analytic thinking dropped further. People became sadder, and their thinking reflected attempts to process the uncertainty. Familial ties strengthened, but ties to broader social groups weakened. Six weeks after COVID-19's onset ("normalization phase"), people's psychological states stabilized but remained elevated. Most psychological shifts were stronger when the threat of COVID-19 was greater. The magnitude of the observed shifts dwarfed responses to other events that occurred in the previous decade.

Hearing Aid Consumer Reviews: A Linguistic Analysis in Relation to Benefit and Satisfaction Ratings.

Purpose Online reviews have been used by hearing aid owners to share their experiences and to provide suggestions to potential hearing aid buyers, although they have not been systematically examined. The study was aimed at examining the hearing aid consumer reviews using automated linguistic analysis, and how the linguistic variables relate to self-reported hearing aid benefit and satisfaction ratings. Method The study used a cross-sectional design. One thousand three hundred seventy-eight consumer hearing aid reviews (i.e., text response to open-ended question), self-reported benefit and satisfaction ratings on hearing aids in a 5-point scale with meta-data (e.g., hearing aid brand, technology level) extracted from the Hearing Tracker website were analyzed using automated text analysis method known as the Linguistic Inquiry and Word Count. Results Self-reported hearing aid benefit and satisfaction ratings were high (i.e., mean rating of 4.04 in a 5-point scale). Examining the association between overall rating and the key linguistic variables point to two broad findings. First, the more people were personally, socially, and emotionally engaged with the hearing device experience, the higher they rated their hearing device(s). Second, a minimal occurrence of clinic-visit language dimensions points to factors that likely affect benefit and satisfaction ratings. For example, if people mention paying too much money (money), their overall ratings are generally lower. Conversely, if people write about their health or home, the ratings were higher. There was no significant difference in linguistic analysis across different hearing aid brands and technology levels. Conclusions Hearing aid consumers are generally satisfied with their hearing device(s), and their online reviews contain information about social/emotional dimensions as well as clinic-visit related aspects that have bearing toward hearing aid benefit and satisfaction ratings. These results suggest that the natural language used by consumers provide insights on their perceived benefit/satisfaction from their hearing device.

Hearing aid acquisition and ownership: what can we learn from online consumer reviews?

OBJECTIVE: To explore the publicised opinions of consumers actively participating in online hearing aid reviews. DESIGN: A retrospective design examining data generated from an online consumer review website (www.HearingTracker.com). Qualitative data (open text responses) were analysed using the open source automated topic modelling software IRaMuTeQ (http://www.iramuteq.org/) to identify themes. Outputs were compared with quantitative data from the consumer reviews (short response questions exploring hearing aid performance and benefit, and some meta-data such as hearing aid brand and years of hearing aid ownership). STUDY SAMPLE: 1378 online consumer hearing aid reviews. RESULTS: Six clusters within two domains were identified. The domain Device Acquisition included three clusters: Finding the right provider, device and price-point; Selecting a hearing aid to suit the hearing loss; Attaining physical fit and device management skills. The domain Device Use included three clusters: Smartphone streaming to hearing aids; Hearing aid adjustment using smartphone; and Hearing in noise. CONCLUSIONS: Although online hearing aid consumers indicate positive performance on multiple-choice questions relating to hearing aid performance and benefit, their online reviews describe a number of barriers limiting their success. Hearing healthcare clinicians must employ a personalised approach to audiological rehabilitation to ensure individual clients' needs are met.

A New Pathway to University Retention? Identity Fusion With University Predicts Retention Independently of Grades.

Individuals who are "strongly fused" with a group view the group as self-defining. As such, they should be particularly reluctant to leave it. For the first time, we investigate the implications of identity fusion for university retention. We found that students who were strongly fused with their university (+1 SD) were 7-9% points more likely than weakly fused students (-1SD) to remain in school up to a year later. Fusion with university predicted subsequent retention in four samples (N = 3,193) and held while controlling for demographics, personality, prior academic performance, and belonging uncertainty. Interestingly, fusion with university was largely unrelated to grades, suggesting that identity fusion provides a novel pathway to retention independent of established pathways like academic performance. We discuss the theoretical and practical implications of these findings.

Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup.

Using archived social media data, the language signatures of people going through breakups were mapped. Text analyses were conducted on 1,027,541 posts from 6,803 Reddit users who had posted about their breakups. The posts include users' Reddit history in the 2 y surrounding their breakups across the various domains of their life, not just posts pertaining to their relationship. Language markers of an impending breakup were evident 3 mo before the event, peaking on the week of the breakup and returning to baseline 6 mo later. Signs included an increase in I-words, we-words, and cognitive processing words (characteristic of depression, collective focus, and the meaning-making process, respectively) and drops in analytic thinking (indicating more personal and informal language). The patterns held even when people were posting to groups unrelated to breakups and other relationship topics. People who posted about their breakup for longer time periods were less well-adjusted a year after their breakup compared to short-term posters. The language patterns seen for breakups replicated for users going through divorce (n = 5,144; 1,109,867 posts) or other types of upheavals (n = 51,357; 11,081,882 posts). The cognitive underpinnings of emotional upheavals are discussed using language as a lens.

Stereotyping in the digital age: Male language is "ingenious", female language is "beautiful" - and popular.

The huge power for social influence of digital media may come with the risk of intensifying common societal biases, such as gender and age stereotypes. Speaker's gender and age also behaviorally manifest in language use, and language may be a powerful tool to shape impact. The present study took the example of TED, a highly successful knowledge dissemination platform, to study online influence. Our goal was to investigate how gender- and age-linked language styles-beyond chronological age and identified gender-link to talk impact and whether this reflects gender and age stereotypes. In a pre-registered study, we collected transcripts of TED Talks along with their impact measures, i.e., views and ratios of positive and negative talk ratings, from the TED website. We scored TED Speakers' (N = 1,095) language with gender- and age-morphed language metrics to obtain measures of female versus male, and younger versus more senior language styles. Contrary to our expectations and to the literature on gender stereotypes, more female language was linked to higher impact in terms of quantity, i.e., more talk views, and this was particularly the case among talks with a lot of views. Regarding quality of impact, language signatures of gender and age predicted different types of positive and negative ratings above and beyond main effects of speaker's gender and age. The differences in ratings seem to reflect common stereotype contents of warmth (e.g., "beautiful" for female, "courageous" for female and senior language) versus competence (e.g., "ingenious", "informative" for male language). The results shed light on how verbal behavior may contribute to stereotypical evaluations. They also illuminate how, within new digital social contexts, female language might be uniquely rewarded and, thereby, an underappreciated but highly effective tool for social influence. WC = 286 (max. 300 words).

Natural emotion vocabularies as windows on distress and well-being.

To date we know little about natural emotion word repertoires, and whether or how they are associated with emotional functioning. Principles from linguistics suggest that the richness or diversity of individuals' actively used emotion vocabularies may correspond with their typical emotion experiences. The current investigation measures active emotion vocabularies in participant-generated natural speech and examined their relationships to individual differences in mood, personality, and physical and emotional well-being. Study 1 analyzes stream-of-consciousness essays by 1,567 college students. Study 2 analyzes public blogs written by over 35,000 individuals. The studies yield consistent findings that emotion vocabulary richness corresponds broadly with experience. Larger negative emotion vocabularies correlate with more psychological distress and poorer physical health. Larger positive emotion vocabularies correlate with higher well-being and better physical health. Findings support theories linking language use and development with lived experience and may have future clinical implications pending further research.

The narrative arc: Revealing core narrative structures through text analysis.

Scholars across disciplines have long debated the existence of a common structure that underlies narratives. Using computer-based language analysis methods, several structural and psychological categories of language were measured across ~40,000 traditional narratives (e.g., novels and movie scripts) and ~20,000 nontraditional narratives (science reporting in newspaper articles, TED talks, and Supreme Court opinions). Across traditional narratives, a consistent underlying story structure emerged that revealed three primary processes: staging, plot progression, and cognitive tension. No evidence emerged to indicate that adherence to normative story structures was related to the popularity of the story. Last, analysis of fact-driven texts revealed structures that differed from story-based narratives.

Optimal sampling strategies for characterizing behavior and affect from ambulatory audio recordings.

Advances in mobile and wearable technologies mean it is now feasible to record hours to days of participant behavior in its naturalistic context, a great boon for psychologists interested in family processes and development. While automated activity recognition algorithms exist for a limited set of behaviors, time-consuming human annotations are still required to robustly characterize the vast majority of behavioral and affective markers of interest. This report is the first to date which systematically tests the efficacy of different sampling strategies for characterizing behavior from audio recordings to provide practical guidelines for researchers. Using continuous audio recordings of the daily lives of 11 preschool-aged children, we compared sampling techniques to determine the most accurate and efficient approach. Results suggest that sampling both low and high frequency verbal and overt behaviors is best if samples are short in duration, systematically rather than randomly selected, and sampled to cover at least 12.5% of recordings. Implications for assessment of real-world behavior are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A Mentored Pathway into the Sciences for Underrepresented Populations: A Newly Crafted Educational Modality for Women Exploring Career Opportunities in Science, Technology, Engineering, Mathematics, and Medicine.

Purpose: The barriers generally facing women wishing to pursue careers in the disciplines of science, technology, engineering, mathematics, and medicine (STEMM) in the United States have been well described. However, additional layers of cultural beliefs and needs may pose further obstructions to women in certain cultural subgroups who wish to enter STEMM. Recognition of the challenges faced by such subgroups is important and culturally sensitive educational and training approaches may be necessary. Methods: We therefore created a science mentoring and education program incorporating the specific requirements of our test group, young Orthodox Jewish women. Our goals were to facilitate their knowledge, skills, and attitudes to embark on a scientific career in biomedicine. Interventions were designed to target physical, intellectual, emotional, and spiritual areas of growth with each intervention crafted to the sensitivity of the women's cultural and religious backgrounds. Results: Over the course of 6 years, we enrolled 59 Orthodox Jewish women, ages 16-20 years. These women spent their summers as part of the larger Summer Internship Program (SIP) at the National Institutes of Health. They participated in cohort sizes ranging from 6 to 26 in dozens of multilevel experiences in the SIP over 6-10 weeks. Participants reported strengthening interest to pursue careers in health care-related fields. Other graduates committed to pursue careers in the general sciences, and other graduate studies. Conclusion: This unique educational platform shows promise for other intersectional groups approaching educational barriers to careers in STEMM.

How do online learners study? The psychometrics of students' clicking patterns in online courses.

College students' study strategies were explored by tracking the ways they navigated the websites of two large (Ns of 1384 and 671) online introductory psychology courses. Students' study patterns were measured analyzing the ways they clicked outside of the regularly scheduled class on study materials within the online Learning Management System. Three main effects emerged: studying course content materials (as opposed to course logistics materials) outside of class and higher grades are consistently correlated; studying at any time except in the late night/early morning hours was strongly correlated with grades; students with higher Scholastic Aptitude Test (SAT) scores made higher grades but accessed course materials at lower rates that those with lower SATs. Multiple regressions predicting grades using just SATs and click rates accounted for almost 43 and 36 percent of the grade variance for the Fall and Spring classes respectively. Implications for using click patterns to understand and shape student learning are discussed.

Examining long-term trends in politics and culture through language of political leaders and cultural institutions.

From many perspectives, the election of Donald Trump was seen as a departure from long-standing political norms. An analysis of Trump's word use in the presidential debates and speeches indicated that he was exceptionally informal but at the same time, spoke with a sense of certainty. Indeed, he is lower in analytic thinking and higher in confidence than almost any previous American president. Closer analyses of linguistic trends of presidential language indicate that Trump's language is consistent with long-term linear trends, demonstrating that he is not as much an outlier as he initially seems. Across multiple corpora from the American presidents, non-US leaders, and legislative bodies spanning decades, there has been a general decline in analytic thinking and a rise in confidence in most political contexts, with the largest and most consistent changes found in the American presidency. The results suggest that certain aspects of the language style of Donald Trump and other recent leaders reflect long-evolving political trends. Implications of the changing nature of popular elections and the role of media are discussed.

Depression, negative emotionality, and self-referential language: A multi-lab, multi-measure, and multi-language-task research synthesis.

Depressive symptomatology is manifested in greater first-person singular pronoun use (i.e., I-talk), but when and for whom this effect is most apparent, and the extent to which it is specific to depression or part of a broader association between negative emotionality and I-talk, remains unclear. Using pooled data from N = 4,754 participants from 6 labs across 2 countries, we examined, in a preregistered analysis, how the depression-I-talk effect varied by (a) first-person singular pronoun type (i.e., subjective, objective, and possessive), (b) the communication context in which language was generated (i.e., personal, momentary thought, identity-related, and impersonal), and (c) gender. Overall, there was a small but reliable positive correlation between depression and I-talk (r = .10, 95% CI [.07, .13]). The effect was present for all first-person singular pronouns except the possessive type, in all communication contexts except the impersonal one, and for both females and males with little evidence of gender differences. Importantly, a similar pattern of results emerged for negative emotionality. Further, the depression-I-talk effect was substantially reduced when controlled for negative emotionality but this was not the case when the negative emotionality-I-talk effect was controlled for depression. These results suggest that the robust empirical link between depression and I-talk largely reflects a broader association between negative emotionality and I-talk. Self-referential language using first-person singular pronouns may therefore be better construed as a linguistic marker of general distress proneness or negative emotionality rather than as a specific marker of depression. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Googling Gout: Exploring Perceptions About Gout Through a Linguistic Analysis of Online Search Activities.

OBJECTIVE: To understand what terms people seeking information about gout use most frequently in online searches and to explore the psychological and emotional tone of these searches. METHODS: A large de-identified data set of search histories from major search engines was analyzed. Participants who searched for gout (n = 1,117), arthritis (arthritis search control group, age and sex-matched, n = 2,036), and a random set of age and sex-matched participants (general control group, n = 2,150) were included. Searches were analyzed using Meaning Extraction Helper and Linguistic Inquiry and Word Count. RESULTS: The most frequent unique searches in the gout search group included gout-related and food-related terms. Those who searched for gout were most likely to search for words related to eating or avoidance. In contrast, those who searched for arthritis were more likely to search for disease- or health-related words. Compared with the general control group, higher information seeking was observed for the gout and arthritis search groups. Compared with the general control group, both the gout and arthritis search groups searched for more food-related words and fewer leisure and sex-related words. The searches of both the gout and arthritis search groups were lower in positivity and higher in the frequency of sadness-related words. CONCLUSION: The perception of gout as a condition managed by dietary strategies aligns with online information seeking about the disease and its management. In contrast, people searching for information about arthritis focus more on medical strategies. Linguistic analyses reflect greater disability in social and leisure activities and lower positive emotion for those searching for gout or arthritis.