The Language of the Roberts Court

Article published in the Michigan State Law Review

Language-based personality:a new approach to personality in a digital world

Personality is typically defined as the consistent set of traits, attitudes, emotions, and behaviors that people have. For several decades, a majority of researchers have tacitly agreed that the gold standard for measuring personality was with self-report questionnaires. Surveys are fast, inexpensive, and display beautiful psychometric properties. A considerable problem with this method, however, is that self-reports reflect only one aspect of personality — people's explicit theories of what they think they are like. We propose a complementary model that draws on a big data solution: the analysis of the words people use. Language use is relatively reliable over time, internally consistent, and differs considerably between people. Language-based measures of personality can be useful for capturing/modeling lower-level personality processes that are more closely associated with important objective behavioral outcomes than traditional personality measures. Additionally, the increasing availability of language data and advances in both statistical methods and technological power are rapidly creating new opportunities for the study of personality at ‘big data’ scale. Such opportunities allow researchers to not only better understand the fundamental nature of personality, but at a scale never before imagined in psychological research

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

Analyzing Connections Between User Attributes, Images, and Text

This work explores the relationship between a person’s demographic/ psychological traits (e.g., gender, personality) and selfidentity images and captions. We use a dataset of images and captions provided by N = 1,350 individuals, and we automatically extract features from both the images and captions. We identify several visual and textual properties that show reliable relationships with individual differences between participants. The automated techniques presented here allow us to draw interesting conclusions from our data that would be difficult to identify manually, and these techniques are extensible to other large datasets. We believe that our work on the relationship between user characteristics and user data has relevance in online settings, where users upload billions of images each day (Meeker M, 2014. Internet trends 2014–Code conference. Retrieved May 28, 2014)

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

Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions

In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.Comment: Best Paper Award at CSCW 201

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.https://pubs.asha.org/journal/ajahj2022Speech-Language Pathology and Audiolog

Study Protocol for Writing To Heal: A Culturally Based Brief Expressive Writing Intervention for Chinese Immigrant Breast Cancer Survivors

BACKGROUND: This study uses a randomized controlled trial (RCT) to test the health benefits of expressive writing that is culturally adapted for Chinese immigrant breast cancer survivors (BCSs) and to characterize how acculturation moderates the effects of expressive writing interventions. METHODS: We will recruit Chinese immigrant BCSs (N = 240) diagnosed with stage 0-III breast cancer and within 5 years of completion of primary treatment. Recruitment will occur primarily through community-based organizations and cancer registries. Participants will be randomly assigned either to a control condition to write about neutral topics or to one of two intervention conditions, self-regulation or self-cultivation, both of which aim to promote adaptive cognitive processes but differ in how they achieve this goal. The self-regulation intervention culturally adapts a Western expressive writing paradigm and incorporates emotional disclosure, whereas the self-cultivation intervention originates from Asian cultural values without disclosing emotions. Participants in all three conditions will be asked to write in their preferred language for three 30-minute sessions. The primary outcome will be quality of life (QOL) at the 6- and 12-month follow-ups, and the secondary outcomes will be perceived stress, stress biomarkers, and medical appointments for cancer-related morbidities. DISCUSSION: This project will be the first large RCT to test culturally based brief interventions to improve QOL and reduce stress among Chinese immigrant BCSs. This project is expected to address two important needs of Chinese immigrant BCSs: their unmet psychological needs and the lack of culturally competent mental health care for Chinese immigrant BCSs. The immediate product of this line of research will be empirically evaluated, culturally responsive interventions ready for dissemination to Chinese immigrant BCSs across the United States

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. While social media has shown potential as a viable “passive sensor” of mental health, the construct validity and in-practice reliability of such computational assessments 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