Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers

Large language models can infer psychological dispositions of social media users

Large language models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychological dispositions of social media users and whether their ability to do so varies across socio-demographic groups. Specifically, we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users' Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r = 0.29 ( range = [ 0.22 , 0.33 ] ) between LLM-inferred and self-reported trait scores-a level of accuracy that is similar to that of supervised machine learning models specifically trained to infer personality. Our findings also highlight heterogeneity in the accuracy of personality inferences across different age groups and gender categories: predictions were found to be more accurate for women and younger individuals on several traits, suggesting a potential bias stemming from the underlying training data or differences in online self-expression. The ability of LLMs to infer psychological dispositions from user-generated text has the potential to democratize access to cheap and scalable psychometric assessments for both researchers and practitioners. On the one hand, this democratization might facilitate large-scale research of high ecological validity and spark innovation in personalized services. On the other hand, it also raises ethical concerns regarding user privacy and self-determination, highlighting the need for stringent ethical frameworks and regulation.

Speaking Well and Feeling Good: Age-Related Differences in the Affective Language of Resting State Thought

Despite the prevalence and importance of resting state thought for daily functioning and psychological well-being, it remains unclear how such thoughts differ between young and older adults. Age-related differences in the affective tone of resting state thoughts, including the affective language used to describe them, could be a novel manifestation of the positivity effect, with implications for well-being. To examine this possibility, a total of 77 young adults (M = 24.9 years, 18-35 years) and 74 cognitively normal older adults (M = 68.6 years, 58-83 years) spoke their thoughts freely during a think-aloud paradigm across two studies. The emotional properties of spoken words and participants' retrospective self-reported affective experiences were computed and examined for age differences and relationships with psychological well-being. Study 1, conducted before the start of the COVID-19 pandemic, revealed that older adults exhibited more diversity of positive, but not negative, affectively tinged words compared to young adults and more positive self-reported thoughts. Despite being conducted virtually during the COVID-19 pandemic, study 2 replicated many of study 1's findings, generalizing results across samples and study contexts. In an aggregated analysis of both samples, positive diversity predicted higher well-being beyond other metrics of affective tone, and the relationship between positive diversity and well-being was not moderated by age. Considering that older adults also exhibited higher well-being, these results hint at the possibility that cognitively healthy older adults' propensity to experience more diverse positive concepts during natural periods of restful thought may partly underlie age-related differences in well-being and reveal a novel expression of the positivity effect.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-024-00239-z.

Increase in Linguistic Complexity in Older Adults During COVID-19

The reported psychological impact of the COVID-19 pandemic and related public health measures included a decline in cognitive functioning in older adults. Cognitive functioning is known to correlate with the lexical and syntactic complexity of an individual's linguistic productions. We examined written narratives from the CoSoWELL corpus (v 1.0), collected from over 1,000 U.S. and Canadian older adults (55+ y.o.) before and during the first year of the pandemic. We expected a decrease in the linguistic complexity of the narratives, given the oft-reported reduction in cognitive functioning associated with COVID-19. Contrary to this expectation, all measures of linguistic complexity showed a steady increase from the pre-pandemic level throughout the first year of the global lockdown. We discuss possible reasons for this boost in light of existing theories of cognition and offer a speculative link between the finding and reports of increased creativity during the pandemic.

Personality of organizational social media accounts and its relationship with characteristics of their photos: analyses of startups' Instagram photos

BACKGROUND

Organizational accounts of social networking sites (SNSs) are similar to individual accounts in terms of their online behaviors. Thus, they can be investigated from the perspective of personality, as individual accounts have been in the literature. Focusing on startups' Instagram accounts, this study aimed to investigate the characteristics of Big Five personality traits and the relationships between the traits and the characteristics of photos in organizational SNS accounts.

METHODS

The personality traits of 108 startups' accounts were assessed with an online artificial intelligence service, and a correspondence analysis was performed to identify the key dimensions where the account were distributed by their personality. Photo features were extracted at the content and pixel levels, and correlational analyses between personality traits and photo features were conducted. Moreover, predictive analyses were performed using random forest regression models.

RESULTS

The results indicated that personality of the accounts had high openness, agreeableness, and conscientiousness and moderate extraversion and neuroticism. In addition, the two dimensions of high vs. low in neuroticism and extraversion/openness vs. conscientiousness/agreeableness in the accounts' distribution by their personality traits were identified. Conscientiousness was the trait most associated with photo features-in particular, with content category, pixel-color, and visual features, while agreeableness was the trait least associated with photo features. Neuroticism was mainly correlated with pixel-level features, openness was correlated mainly with pixel-color features, and extraversion was correlated mainly with facial features. The personality traits, except neuroticism, were predicted from the photo features.

CONCLUSIONS

This study applied the theoretical lens of personality, which has been mainly used to examine individuals' behaviors, to investigate the SNS communication of startups. Moreover, it focused on the visual communication of organizational accounts, which has not been actively studied in the literature. This study has implications for expanding the realm of personality research to organizational SNS accounts.

Tone in politics is not systematically related to macro trends, ideology, or experience

What explains the variation in tone in politics? Different literatures argue that changes in the tone of politicians reflect changes in the economy, general language, well-being, or ideology. So far, these claims have been empirically tested only in isolation, in single country studies, or with a small subset of indicators. We offer an overarching view by modelling the use of tone in European national parliaments in 7 countries across 30 years. Using a semi-supervised sentiment-topic model to measure polarity and arousal in legislative debates, we show in a preregistered multiverse analysis that the tone in legislative debates is not systematically related to previously claimed factors. We also replicate the absence of such systematic relationships using national leader speeches and parties' election manifestos. There is also no universal trend towards more negativity or emotionality in political language. Overall, our results highlight the importance of multi-lingual and cross-country multiverse analyses for generalizing findings on emotions in politics.

The online language of work-personal conflict

With the blurring of boundaries in this digital age, there is increasing concern around work-personal conflict. Assessing and tracking work-personal conflict is critical as it not only affects individual workers but is also a vital measure among broader well-being and economic indices. This inductive study examines the extent to which work-personal conflict corresponds to individuals' language use on social media. We apply an open-vocabulary analysis to the posts of 2810 Facebook users who also completed a survey for an established work-personal conflict scale. It was found that the language-based model can predict personal-to-work conflict (r = 0.23) and work-to-personal conflict (r = 0.15) and provide important insights into such conflicts. Specifically, we found that high personal-to-work conflict was associated with netspeak and swearing, while low personal-to-work conflict was associated with language about work and positivity. We found that high work-to-personal conflict was associated with negative emotion and negative tone, while low work-to-personal conflict was associated with positive emotion and language about birthdays.

Voice Disorders and Personality: New Steps on an Old Path

OBJECTIVES

This study examined the association between voice disorders and personality by comparing a heterogenic group of dysphonic patients to non-dysphonic speakers using the NEO-FFI big-five personality inventory.

METHODS

A group of 100 dysphonic patients diagnosed with 24 different pathologies was compared to 149 non-dysphonic speakers. Inter-group differences on the five NEO-FFI scales were evaluated using three analysis approaches, a general comparison between the dysphonic and non-dysphonic groups and arranging pathologies using a categorical and a continuous approach.

RESULTS

Of the five NEO-FFI scales, Openness emerged as the single personality trait that yielded a statistically significant difference between the dysphonic group and the non-dysphonic group (P < 0.001). Moreover, when the 24 pathologies were arranged categorically, people with structural pathologies were lower on the Openness scale than non-dysphonic speakers (P < 0.001). Similarly, when pathologies were arranged continuously, people with pathologies characterized by high organicity were low on the Openness scale compared to the non-dysphonic group (P < 0.001).

CONCLUSIONS

This study represents a new approach to examining the association between voice disorders and personality. Openness emerged as the single personality trait that repeatedly and consistently differentiated between dysphonic and non-dysphonic people and among specific pathologies, using all analysis approaches. Our findings suggest that examining a spectrum of pathologies, rather than focusing on a particular pathology, provides a new perspective and sheds light on the complex association between voice disorders and personality.

Cognitive and social well-being in older adulthood: The CoSoWELL corpus of written life stories

This paper presents the Cognitive and Social WELL-being (CoSoWELL) project that consists of two components. One is a large corpus of narratives written by over 1000 North American older adults (55+ years old) in five test sessions before and during the first year of the COVID-19 pandemic. The other component is a rich collection of socio-demographic data collected through a survey from the same participants. This paper introduces the first release of the corpus consisting of 1.3 million tokens and the survey data (CoSoWELL version 1.0). It also presents a series of analyses validating design decisions for creating the corpus of narratives written about personal life events that took place in the distant past, recent past (yesterday) and future, along with control narratives. We report results of computational topic modeling and linguistic analyses of the narratives in the corpus, which track the time-locked impact of the COVID-19 pandemic on the content of autobiographical memories before and during the COVID-19 pandemic. The main findings demonstrate a high validity of our analytical approach to unique narrative data and point to both the locus of topical shifts (narratives about recent past and future) and their detailed timeline. We make the CoSoWELL corpus and survey data available to researchers and discuss implications of our findings in the framework of research on aging and autobiographical memories under stress.

Detecting Narcissism From Older Adults' Daily Language Use: A Machine Learning Approach

OBJECTIVES

Narcissism has been associated with poorer quality social connections in late life, yet less is known about how narcissism is associated with older adults' daily social interactions. This study explored the associations between narcissism and older adults' language use throughout the day.

METHODS

Participants aged 65-89 (N = 281) wore electronically activated recorders which captured ambient sound for 30 s every 7 min across 5-6 days. Participants also completed the Narcissism Personality Inventory-16 scale. We used Linguistic Inquiry and Word Count to extract 81 linguistic features from sound snippets and applied a supervised machine learning algorithm (random forest) to evaluate the strength of links between narcissism and each linguistic feature.

RESULTS

The random forest model showed that the top 5 linguistic categories that displayed the strongest associations with narcissism were first-person plural pronouns (e.g., we), words related to achievement (e.g., win, success), to work (e.g., hiring, office), to sex (e.g., erotic, condom), and that signal desired state (e.g., want, need).

DISCUSSION

Narcissism may be demonstrated in everyday life via word use in conversation. More narcissistic individuals may have poorer quality social connections because their communication conveys an emphasis on self and achievement rather than affiliation or topics of interest to the other party.

Uncovering the Social-Cognitive Contributors to Social Dysfunction in Borderline Personality Disorder Through Language Analysis

Borderline personality disorder (BPD) is characterized by severe interpersonal dysfunction, yet the underlying nature of such dysfunction remains poorly understood. The present study adopted a behavioral approach to more objectively describe the social-cognitive contributors to interpersonal dysfunction in BPD. Participants (N = 530) completed an online survey comprising validated measures of BPD features and other problematic interpersonal traits (e.g., narcissism), as well as a writing prompt where they were asked to share their personal thoughts about relationships. Computerized language analysis methods were used to quantify various psychosocial dimensions of participants' writing, which were incorporated into a principal component analysis. Analyses revealed four core social dimensions of thought: (1) Connectedness/Intimacy; (2) Immediacy; (3) Social Rumination; (4) Negative Affect. All four dimensions correlated with BPD features in intuitive ways, some of which were specific to BPD. This study highlights the value of natural language analysis to explore fundamental dimensions of personality disorder.

Dominants, Submissives, and Bottom-up Text Analysis: Exploring BDSM Roles Through Romantic and Erotic Narratives

Powerplay, or the consensual power exchange between "Dominant" and "submissive" (D/s) role partners, is common practice within BDSM culture. To many BDSM practitioners, their D/s role is an integral part of their sexual identity, informing not only their sexual scripts but also their non-sexual social interactions. Linguistic Inquiry and Word Count (LIWC) text analysis software was used to process 261 participants' responses to prompts asking them to write erotic and relationship-themed narratives. Using a data-driven approach to model selection, we regressed participants' engagement in BDSM and D/s powerplay role identification on standardized language frequencies. Stories from more active BDSM practitioners' narratives used more perceptual words, suggesting potentially mindful, intimate, and detailed storytelling, whereas non-practitioners used more tentativeness and death in their writings. Moreover, language reflected D/s role attributes, with Dominants exhibiting ownership, responsibility, and other-focus, and submissives referencing power dynamics and self-focus in their responses. Findings are consistent with existing literature on BDSM power relationships and relate to psychosexual well-being.

Characteristics of online user-generated text predict the emotional intelligence of individuals

Emotional intelligence is a well-established indicator of performance and the ability to maintain successful social relationships. Moreover, it is potentially an important factor in social dynamics occurring on large digital platforms, e.g., opinion polarization, social conflict, and social influence. Users publicly exchange enormous amounts of text on digital platforms, which can potentially be used to extract real-life insights. Yet, currently, the prevalent approach to measuring emotional intelligence uses mainly self-report surveys and tasks-considerably limiting the feasibility of real-life large-scale studies. We analyze the online public texts of users, who also completed emotional intelligence measures, to find that characteristics of online public texts can be used to predict emotional intelligence at a level like that of commonly used psychometric indicators (e.g., SATs) to predict real-life outcomes. For example, we find that high emotional intelligence individuals consistently use more positive-affect language, less negative-affect language and use more social-oriented language than low emotional intelligence individuals. Our findings provide insight into the role of personality on digital platforms and open the possibility of studying emotional intelligence in large and diverse real-life data. To support the use of online public text as a tool to research emotional intelligence, we provide an anonymized version of the data.

Neuroticism modulates the qualitative effects of inferior parietal tDCS on negatively-valenced memories

For individuals with increased levels of neuroticism, experiencing criticism or receiving negative feedback has been associated with worse psychological and cognitive outcomes. Transcranial direct current stimulation (tDCS) can change cognitive processes in clinical populations. We bilaterally stimulated the posterior inferior parietal lobule (pIPL), a critical superficial node of the default model network. We investigated how baseline neuroticism modulates the impact of bilateral tDCS to pIPL on qualitative measures of memory after hearing criticism, hypothesizing that cathodal stimulation of the IPL would offer qualitative memory improvements for individuals with higher levels of neuroticism. Ninety individuals from the community were randomly assigned to receive anodal, cathodal, or sham stimulation while they were exposed to critical comments before and after stimulation. Participants then recalled the critical comments, and their linguistic responses were analyzed using Pennebaker's Linguistic Inquiry and Word Count software, a quantitative analysis software for linguistic data. Results showed that for individuals receiving cathodal tDCS, higher neuroticism scores corresponded with greater proportions of non-personal language (i.e., words such as "us," "they," or "other" instead of "I" or "me") when recalling negative feedback. For individuals with higher neuroticism, cathodal tDCS stimulation, rather than anodal or sham, of the pIPL prompted increased emotional distancing and perspective taking strategies when recalling criticism. These results further highlight the state-dependent nature of tDCS effects and the role of the IPL in interpersonal processing - a clinically meaningful outcome that current tDCS studies solely examining quantitative measures of memory (e.g., task-based accuracy or speed) do not reveal.

Three families of automated text analysis

Since the beginning of this millennium, data in the form of human-generated text in a machine-readable format has become increasingly available to social scientists, presenting a unique window into social life. However, harnessing vast quantities of this highly unstructured data in a systematic way presents a unique combination of analytical and methodological challenges. Luckily, our understanding of how to overcome these challenges has also developed greatly over this same period. In this article, I present a novel typology of the methods social scientists have used to analyze text data at scale in the interest of testing and developing social theory. I describe three "families" of methods: analyses of (1) term frequency, (2) document structure, and (3) semantic similarity. For each family of methods, I discuss their logical and statistical foundations, analytical strengths and weaknesses, as well as prominent variants and applications.

A Failed Cross-Validation Study on the Relationship between LIWC Linguistic Indicators and Personality: Exemplifying the Lack of Generalizability of Exploratory Studies

(1) Background: Previous meta-analytic research found small to moderate relationships between the Big Five personality traits and different linguistic computational indicators. However, previous studies included multiple linguistic indicators to predict personality from an exploratory framework. The aim of this study was to conduct a cross-validation study analyzing the relationships between language indicators and personality traits to test the generalizability of previous results; (2) Methods: 643 Spanish undergraduate students were tasked to write a self-description in 500 words (which was evaluated with the LIWC) and to answer a standardized Big Five questionnaire. Two different analytical approaches using multiple linear regression were followed: first, using the complete data and, second, by conducting different cross-validation studies; (3) Results: The results showed medium effect sizes in the first analytical approach. On the contrary, it was found that language and personality relationships were not generalizable in the cross-validation studies; (4) Conclusions: We concluded that moderate effect sizes could be obtained when the language and personality relationships were analyzed in single samples, but it was not possible to generalize the model estimates to other samples. Thus, previous exploratory results found on this line of research appear to be incompatible with a nomothetic approach.

Personality and Word Use: Study on Czech Language and the Big Five

The study is a follow-up to three published anglophone researches examining the relation between the use of linguistic categories and personality characteristics as outlined in the Big Five model, with the purpose of replicating these and elaborating for the Czech language. The comparative research study in Czech focuses on analysis of both grammatical and semantic variables in six types of text (written and oral), produced by N = 200 participants. Within the study, there were six confirmed relations, however, these appear only in certain types of text. The results show not only an essential role of the text register, but they also allow us to evaluate the universality of findings of studies in English in comparison with other, especially Slavic, languages.

Personality of nonprofit organizations’ Instagram accounts and its relationship with their photos’ characteristics at content and pixel levels

Nonprofit organizations (NPO) can utilize social networking sites (SNSs) for their activities. Like individual users, they can create SNS accounts, upload posts to show what they are doing, and communicate with other users. Thus, their accounts can be investigated from the same perspective of personality which has been one of the key lenses through which SNS posts of individual users was investigated. In the line of literature that analyzed the personality of non-human objects such as products, stores, brands, and websites, the present research analyzed the personality of NPOs’ Instagram accounts using an online AI service. Also, it investigated how their personality traits were related to the characteristics of the uploaded photos at content and pixel levels. The results of analysis of 223,446 photos on 177 Instagram accounts suggested that the personality of NPOs’ Instagram accounts can be summarized as being high in openness and agreeableness but low in extraversion and neuroticism. And it was found that openness and agreeableness were the personality traits that associated the most with the photo features. Also, the personality traits of NPOs’ Instagram accounts, except neuroticism, were predicted from the photo features with an acceptable level of accuracy. Implications of this research and suggestions for further research were presented.

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.

Linguistic measures of personality in group discussions

This investigation sought to find the relationships among multiple dimensions of personality and multiple features of language style. Unlike previous investigations, after controlling for such other moderators as culture and socio-demographics, the current investigation explored those dimensions of naturalistic spoken language that most closely align with communication. In groups of five to eight players, participants (N = 340) from eight international locales completed hour-long competitive games consisting of a series of ostensible missions. Composite measures of quantity, lexical diversity, sentiment, immediacy and negations were measured with an automated tool called SPLICE and with Linguistic Inquiry and Word Count. We also investigated style dynamics over the course of an interaction. We found predictors of extraversion, agreeableness, and neuroticism, but overall fewer significant associations than prior studies, suggesting greater heterogeneity in language style in contexts entailing interactivity, conversation rather than solitary message production, oral rather than written discourse, and groups rather than dyads. Extraverts were found to maintain greater linguistic style consistency over the course of an interaction. The discussion addresses the potential for Type I error when studying the relationship between language and personality.

Linguistic tones in MRI reports correlate with severity of pathology for rotator cuff tendinopathy

BACKGROUND

Written communication can convey one's emotions, personality, and sentiments. Radiology reports employ medical jargon and serve to document a patients' condition. Patients might misinterpret this medical jargon in a way that increases their anxiety and makes them feel unwell. We were interested whether linguistic tones in MRI reports vary between radiologists and correlate with the severity of pathology.

QUESTIONS/PURPOSES

(1) Is there variation in linguistic tones among different radiologists reporting MRI results for rotator cuff tendinopathy? (2) Is the retraction of the supraspinatus tendon in millimeters associated with linguistic tones?

METHODS

Two hundred twenty consecutive MRI reports of patients with full-thickness rotator cuff defects were collected. Supraspinatus retraction was measured on the MRI using viewer tools. Using Kruskal-Wallis H tests, we measured variation between 11 radiologists for the following tones: positive emotion, negative emotion, analytical thinking, cause, insight, tentativeness, certainty, and informal speech. We also measured the correlation of tones and the degree of tendon retraction. Multilevel mixed-effects linear regression models were constructed, seeking factors associated with the tone, accounting for retraction, the presence of prior imaging, and for the effects of each radiologist (nesting).

RESULTS

There were statistically significant differences for all of the tones by radiologist. In bivariate analysis, greater retraction of the supraspinatus muscle in millimeters was associated with more negative emotion and certainty, and with less tentativeness. In multilevel mixed-effects linear regression, more negative tones were associated with greater retraction and absence of prior imaging. Greater tentativeness was associated with the absence of prior imaging, but not with retraction.

CONCLUSIONS

Radiology reports have emotional content that is relatively negative, varies by radiologist and is affected by pathology. Strategies for more hopeful, positive, optimistic descriptions of pathology have the potential to help patients feel better without introducing inaccuracies even if unlikely.

LEVEL OF EVIDENCE

Level III, Diagnostic.

Swear Word Fluency, Verbal Fluency, Vocabulary, Personality, and Drug Involvement

Abstract. Swearing is stereotypically associated with socially undesirable traits and behaviors, including limited verbal ability, disagreeable personality, and alcohol use. We sought to demonstrate that, contrary to such stereotypes, swear word fluency (i.e., ability to generate swear words) does not arise from a lack of verbal skills. We also explored whether swear word fluency might serve as an index of personality traits related to drug use. Accordingly, we conducted a preregistered study in which 266 undergraduates at a US university ( Mage = 19.36; 66.9% self-identified as women and 49.6% as White) completed measures of swear word fluency, verbal fluency (i.e., overall ability to generate words), vocabulary, Big Five traits, sensation seeking, and drug use. We observed positive associations between swear word fluency and verbal fluency, vocabulary, Openness, and Extraversion, and a negative association with Agreeableness. Moreover, swear word fluency accounted for unique variance in self-reported drug use over and above that accounted for by personality and general verbal ability. Swear word fluency might serve as one of few tasks where higher scores predict more drug involvement, justifying further work linking this measure with other aspects of personality and drug use.

Opportunities and Challenges of Extracting Values in Autobiographical Narratives

We report three studies in which we applied a value dictionary to narratives. Our objective was to test a theory-driven value dictionary for extracting valuable information from autobiographical and narrative texts. In Studies 1 (N = 106) and 2 (N = 152), participants wrote short autobiographical narratives and in Study 3 (N = 150), participants wrote narratives based on ambiguous stimuli. Participants in all three studies also completed the Portrait Value Questionnaire as a self-report measure of values. Overall, our results demonstrate that it is possible to extract value-relevant information from these narratives. Extracted values from autobiographical narratives showed average correlations of 0.07 (Study 1) and 0.12 (Study 2) with self-reports compared to an average correlation of 0.01 for the extracted values from implicit motive tasks (Study 3). The correlations with self-reports were in line with previous validation studies. The most salient values in narratives diverged somewhat, with a stronger emphasis on achievement values compared to self-reports, probably due to the nature of salient episodes within one's life that require demonstrating success according to social standards. Benevolence values were consistently most important in both self-ratings and text-based scoring. The value structure emerging from narratives diverged from the theoretically predicted structure, yet broad personally vs. socially focused value dimensions were qualitatively discernible. We highlight opportunities and challenges for future value research using autobiographical stories.

The Effects of Partner Extraversion and Agreeableness on Trust

Existing research has documented the social benefits (i.e., higher popularity and liking) of extraversion and agreeableness. Do these positive reputational consequences extend to social dilemma situations that require trust? We found that people do not trust extraverts more than introverts. Instead, people's trust decisions are guided by their partner's level of agreeableness. In a trust game (Studies 1 and 2), individuals were more likely to trust a partner who was described as agreeable (vs. disagreeable); and, in a laboratory study of work groups, participants trusted more (vs. less) agreeable group members (Study 3). Individuals anticipated others' preferences for agreeable partners and tried to come across as more agreeable, but not more extraverted, in social dilemmas (Study 4). These findings suggest that the social benefits of agreeableness (but not extraversion) extend to social interactions involving trust and highlight the importance of target personality traits in shaping trust decisions.

Predicting Academic Performance: Analysis of Students' Mental Health Condition from Social Media Interactions

Social media have become an indispensable part of peoples' daily lives. Research suggests that interactions on social media partly exhibit individuals' personality, sentiment, and behavior. In this study, we examine the association between students' mental health and psychological attributes derived from social media interactions and academic performance. We build a classification model where students' psychological attributes and mental health issues will be predicted from their social media interactions. Then, students' academic performance will be identified from their predicted psychological attributes and mental health issues in the previous level. Firstly, we select samples by using judgmental sampling technique and collect the textual content from students' Facebook news feeds. Then, we derive feature vectors using MPNet (Masked and Permuted Pre-training for Language Understanding), which is one of the latest pre-trained sentence transformer models. Secondly, we find two different levels of correlations: (i) users' social media usage and their psychological attributes and mental health status and (ii) users' psychological attributes and mental health status and their academic performance. Thirdly, we build a two-level hybrid model to predict academic performance (i.e., Grade Point Average (GPA)) from students' Facebook posts: (1) from Facebook posts to mental health and psychological attributes using a regression model (SM-MP model) and (2) from psychological and mental attributes to the academic performance using a classifier model (MP-AP model). Later, we conduct an evaluation study by using real-life samples to validate the performance of the model and compare the performance with Baseline Models (i.e., Linguistic Inquiry and Word Count (LIWC) and Empath). Our model shows a strong performance with a microaverage f-score of 0.94 and an AUC-ROC score of 0.95. Finally, we build an ensemble model by combining both the psychological attributes and the mental health models and find that our combined model outperforms the independent models.

Beyond English: Considering Language and Culture in Psychological Text Analysis

The paper discusses the role of language and culture in the context of quantitative text analysis in psychological research. It reviews current automatic text analysis methods and approaches from the perspective of the unique challenges that can arise when going beyond the default English language. Special attention is paid to closed-vocabulary approaches and related methods (and Linguistic Inquiry and Word Count in particular), both from the perspective of cross-cultural research where the analytic process inherently consists of comparing phenomena across cultures and languages and the perspective of generalizability beyond the language and the cultural focus of the original investigation. We highlight the need for a more universal and flexible theoretical and methodological grounding of current research, which includes the linguistic, cultural, and situational specifics of communication, and we provide suggestions for procedures that can be implemented in future studies and facilitate psychological text analysis across languages and cultures.

Altered electromyographic responses to emotional and pain information in awake bruxers: case-control study

OBJECTIVES

This study aims to investigate how emotional information and pain-related information affect the activity of the masticatory muscles in participants with awake bruxism and controls.

MATERIAL AND METHODS

Different videos and texts, with positive, negative, and neutral valence or related to pain, were presented to a sample of university students, while their electromyographic (EMG) activity around the masseter muscle and their skin conductance were recorded. Two groups were selected, with 24 subjects each: one group of subjects with definitive awake bruxism (confirmed by posterior EMG activity) who also suffered from moderate jaw discomfort, and another group of subjects without bruxism.

RESULTS

The results demonstrated that the subjects with definitive awake bruxism displayed greater muscular activity when presented videos and texts with negative valence, especially when related to pain, than the non-bruxist group.

CONCLUSIONS

This study supports the idea that persons with bruxism who also suffer moderate levels of jaw discomfort present greater bruxism activity when watching pain-related stimuli, and to a lesser extent when watching negative stimuli.

CLINICAL RELEVANCE

The increased muscular activity induced by negative and pain-related information might contribute to pain exacerbation and perpetuation in persons with bruxism who suffer from discomfort.

Designing Personality-Adaptive Conversational Agents for Mental Health Care

Millions of people experience mental health issues each year, increasing the necessity for health-related services. One emerging technology with the potential to help address the resulting shortage in health care providers and other barriers to treatment access are conversational agents (CAs). CAs are software-based systems designed to interact with humans through natural language. However, CAs do not live up to their full potential yet because they are unable to capture dynamic human behavior to an adequate extent to provide responses tailored to users' personalities. To address this problem, we conducted a design science research (DSR) project to design personality-adaptive conversational agents (PACAs). Following an iterative and multi-step approach, we derive and formulate six design principles for PACAs for the domain of mental health care. The results of our evaluation with psychologists and psychiatrists suggest that PACAs can be a promising source of mental health support. With our design principles, we contribute to the body of design knowledge for CAs and provide guidance for practitioners who intend to design PACAs. Instantiating the principles may improve interaction with users who seek support for mental health issues.

The Language of Creativity: Validating Linguistic Analysis to Assess Creative Scientists and Artists

The purpose of this study was twofold: first, to be among the first attempts to validate linguistic analysis as a method of creativity assessment and second, to differentiate between individuals in varying scientific and artistic creativity levels using personality language patterns. Creativity is most commonly assessed through methods such as questionnaires and specific tasks, the validity of which can be weakened by scorer or experimenter error, subjective and response biases, and self-knowledge constraints. Linguistic analysis may provide researchers with an automatic, objective method of assessing creativity, and free from human error and bias. The current study used 419 creativity text samples from a wide range of creative individuals mostly in science (and some in the arts and humanities) to investigate whether linguistic analysis can, in fact, distinguish between creativity levels and creativity domains using creativity dictionaries and personality dimension language patterns, from the linguistic inquiry and word count (LIWC) text analysis program. Creative individuals tended to use more words on the creativity keyword dictionaries as well as more introversion and openness to experience language pattern words than less creative individuals. Regarding creativity domains, eminent scientists used fewer introversion, and openness to experience language pattern words than eminent artists. Text analysis through LIWC was able to partially distinguish between the three creativity levels, in some cases, and the two creativity domains (science and art). These findings lend support to the use of linguistic analysis as a partially valid assessment of scientific and artistic creative achievement.

The politics of piracy: political ideology and the usage of pirated online media

There is a lack of clarity in information systems research on which factors lead people to use or not use technologies of varying degrees of perceived legality. To address this gap, we use arguments from the information systems and political ideology literatures to theorize on the influence of individuals’ political ideologies on online media piracy. Specifically, we hypothesize that individuals with a more conservative ideology, and thus lower openness to experience and higher conscientiousness, generally engage in less online media piracy. We further hypothesize that this effect is stronger for online piracy technology that is legally ambiguous. Using clickstream data from 3873 individuals in the U.S., we find that this effect in fact exists only for online media piracy technologies that are perceived as legally ambiguous. Specifically, more conservative individuals, who typically have lower ambiguity intolerance, use (legal but ambiguously perceived) pirated streaming websites less, while there is no difference for the (clearly illegal) use of pirated file sharing websites.

Leveraging Text Mining Approach to Identify What People Want to Know About Mental Disorders From Online Inquiry Platforms

Online inquiry platforms, which is where a person can anonymously ask questions, have become an important information source for those who are concerned about social stigma and discrimination that follow mental disorders. Therefore, examining what people inquire about regarding mental disorders would be useful when designing educational programs for communities. The present study aimed to examine the contents of the queries regarding mental disorders that were posted on online inquiry platforms. A total of 4,714 relevant queries from the two major online inquiry platforms were collected. We computed word frequencies, centralities, and latent Dirichlet allocation (LDA) topic modeling. The words like symptom, hospital and treatment ranked as the most frequently used words, and the word my appeared to have the highest centrality. LDA identified four latent topics: (1) the understanding of general symptoms, (2) a disability grading system and welfare entitlement, (3) stressful life events, and (4) social adaptation with mental disorders. People are interested in practical information concerning mental disorders, such as social benefits, social adaptation, more general information about the symptoms and the treatments. Our findings suggest that instructions encompassing different scopes of information are needed when developing educational programs.

Regional personality assessment through social media language

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.

Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human-Robot Conversations

Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited in number and scope. To date, psychological resilience—the ability to cope with a crisis and quickly return to the pre-crisis state—has been identified as an important predictor of psychological well-being but has not been commonly considered by AI systems (e.g., smart wearable devices) or social robots to personalize services such as emotion coaching. To address the dearth of investigations, the present study explores the possibility of estimating personal resilience using physiological and speech signals measured during human–robot conversations. Specifically, the physiological and speech signals of 32 research participants were recorded while the participants answered a humanoid social robot’s questions about their positive and negative memories about three periods of their lives. The results from machine learning models showed that heart rate variability and paralinguistic features were the overall best predictors of personal resilience. Such predictability of personal resilience can be leveraged by AI and social robots to improve user understanding and has great potential for various mental healthcare applications in the future.

The Effect of Misalignment of CEO Personality and Corporate Governance Structures on Firm Performance

We utilize the IBM Watson Personality Insights service to analyze CEOs’ verbal communication during conference calls to infer CEOs’ Big Five personality traits, which we employ to estimate their risk tolerance levels. We then explore whether the misalignment of CEO risk tolerance and governance structures is associated with company performance. Using a two-stage contingency approach, we test two hypotheses: (1) CEO risk tolerance and corporate governance structures are associated; and (2) misalignment of these structures with risk tolerance is negatively associated with financial performance. Based on a sample of 8208 firm-year observations during 2002–2013, we find support for both predictions. Our results support upper echelons theory and suggest that knowledge about CEOs’ inherent personality traits is important and relevant for governance mechanisms to work effectively.

The Automatic but Flexible and Content-Dependent Nature of Syntax

Syntactic processing has often been considered an utmost example of unconscious automatic processing. In this line, it has been demonstrated that masked words containing syntactic anomalies are processed by our brain triggering event related potential (ERP) components similar to the ones triggered by conscious syntactic anomalies, thus supporting the automatic nature of the syntactic processing. Conversely, recent evidence also points out that regardless of the level of awareness, emotional information and other relevant extralinguistic information modulate conscious syntactic processing too. These results are also in line with suggestions that, under certain circumstances, syntactic processing could also be flexible and context-dependent. However, the study of the concomitant automatic but flexible conception of syntactic parsing is very scarce. Hence, to this aim, we examined whether and how masked emotional words (positive, negative, and neutral masked adjectives) containing morphosyntactic anomalies (half of the cases) affect linguistic comprehension of an ongoing unmasked sentence that also can contain a number agreement anomaly between the noun and the verb. ERP components were observed to emotional information (EPN), masked anomalies (LAN and a weak P600), and unmasked ones (LAN/N400 and P600). Furthermore, interactions in the processing of conscious and unconscious morphosyntactic anomalies and between unconscious emotional information and conscious anomalies were detected. The findings support, on the one hand, the automatic nature of syntax, given that syntactic components LAN and P600 were observed to unconscious anomalies. On the other hand, the flexible, permeable, and context-dependent nature of the syntactic processing is also supported, since unconscious information modulated conscious syntactic components. This double nature of syntactic processing is in line with theories of automaticity, suggesting that even unconscious/automatic, syntactic processing is flexible, adaptable, and context-dependent.

Quantitative language features identify placebo responders in chronic back pain

ABSTRACT

Although placebo effect sizes in clinical trials of chronic pain treatments have been increasing, it remains unknown if characteristics of individuals' thoughts or previous experiences can reliably infer placebo pill responses. Research using language to investigate emotional and cognitive processes has recently gained momentum. Here, we quantified placebo responses in chronic back pain using more than 300 semantic and psycholinguistic features derived from patients' language. This speech content was collected in an exit interview as part of a clinical trial investigating placebo analgesia (62 patients, 42 treated; 20 not treated). Using a nested leave-one-out cross-validated approach, we distinguished placebo responders from nonresponders with 79% accuracy using language features alone; a subset of these features-semantic distances to identity and stigma and the number of achievement-related words-also explained 46% of the variance in placebo analgesia. Importantly, these language features were not due to generic treatment effects and were associated with patients' specific baseline psychological traits previously shown to be predictive of placebo including awareness and personality characteristics, explaining an additional 31% of the variance in placebo analgesia beyond that of personality. Initial interpretation of the features suggests that placebo responders differed in how they talked about negative emotions and the extent that they expressed awareness to various aspects of their experiences; differences were also seen in time spent talking about leisure activities. These results indicate that patients' language is sufficient to identify a placebo response and implie that specific speech features may be predictive of responders' previous treatment.

Automatic Estimation of Multidimensional Personality From a Single Sound-Symbolic Word

Researchers typically use the “big five” traits (Extroversion, Agreeableness, Conscientiousness, Neuroticism, and Openness) as a standard way to describe personality. Evaluation of personality is generally conducted using self-report questionnaires that require participants to respond to a large number of test items. To minimize the burden on participants, this paper proposes an alternative method of estimating multidimensional personality traits from only a single word. We constructed a system that can convert a sound-symbolic word (SSW) that intuitively expresses personality traits into information expressed by 50 personality-related adjective pairs. This system can obtain information equivalent to the adjective scales using only a single word instead of asking many direct questions. To achieve this, we focused on SSWs in Japanese that have the association between linguistic sounds and meanings and express diverse and complex aspects of personality traits. We evaluated the prediction accuracy of the system and found that the multiple correlation coefficients for 48 personality-related adjective pairs exceeded 0.75, indicating that the model could explain more than half of the variations in the data. In addition, we conducted an evaluation experiment in which participants rated the appropriateness of the system output using a seven-point scale (with −3 as absolutely inappropriate and +3 as completely appropriate). The average score for 50 personality-related adjective pairs was 1.25. Thus, we believe that this system can contribute to the field of personality computing, particularly in terms of personality evaluation and communication.