Linguistic styles: language use as an individual difference

Parental linguistic content and distancing predict beliefs about emotion and child emotion regulation

ABSTRACTEmploying a constructionist framework of emotion, this study examines whether parental language during emotion belief discussions predicts parents' self-reported beliefs about emotion and child emotion regulation (ER). 102 parents of children ages 8 through 12 participated in focus groups about emotion beliefs, and nine months later, completed questionnaires on their emotion beliefs and child ER. Focus group content was analyzed for positive and negative emotion talk, cognitive process talk, and an established linguistic marker of psychological distancing. Parents' positive emotion talk and parental linguistic distancing when discussing their child's (but not their own) emotion experiences positively predicted beliefs about children's emotional capabilities. Finally, negative emotion talk negatively predicted parental beliefs about children's capacity to control their own emotions and the value of anger expression as well as child ER. Current findings contribute to our understanding of how parental communication patterns about emotions may influence emotion beliefs and child emotion development.

Natural language sentiment as an indicator of depression and anxiety symptoms: a longitudinal mixed methods study1

The study tested how the use of positive- (e.g. beautiful) and negative-valenced (e.g. horrible) words in natural language and its change in time affects the severity of depression and anxiety symptoms among depressed and non-depressed individuals. This longitudinal mixed methods study (N = 40 participants, n = 1440 narratives) with three measurements within a year showed that at the between-person level the use of negative-valenced words was strongly associated with the increase in anxiety and depression symptoms over time while the use of positive-valenced words was slightly associated with the decrease in anxiety and depression symptom. These effects were not supported for within-person level (i.e. changes in word usage). No significant differences were observed in the effects between depressed and non-depressed groups. Summing up, the overall use of positive- and negative-valenced words (particularly negative-valenced words) had a stronger effect on the severity of psychopathological symptoms than their change over time. The results were discussed in the context of natural language processing and its application in diagnosing depression and anxiety symptoms.

Personality prediction from task-oriented and open-domain human-machine dialogues

If a dialogue system can predict the personality of a user from dialogue, it will enable the system to adapt to the user's personality, leading to better task success and user satisfaction. In a recent study, personality prediction was performed using the Myers-Briggs Type Indicator (MBTI) personality traits with a task-oriented human-machine dialogue using an end-to-end (neural-based) system. However, it is still not clear whether such prediction is generally possible for other types of systems and user personality traits. To clarify this, we recruited 378 participants, asked them to fill out four personality questionnaires covering 25 personality traits, and had them perform three rounds of human-machine dialogue with a pipeline task-oriented dialogue system or an end-to-end task-oriented dialogue system. We also had another 186 participants do the same with an open-domain dialogue system. We then constructed BERT-based models to predict the personality traits of the participants from the dialogues. The results showed that prediction accuracy was generally better with open-domain dialogue than with task-oriented dialogue, although Extraversion (one of the Big Five personality traits) could be predicted equally well for both open-domain dialogue and pipeline task-oriented dialogue. We also examined the effect of utilizing different types of dialogue on personality prediction by conducting a cross-comparison of the models trained from the task-oriented and open-domain dialogues. As a result, we clarified that the open-domain dialogue cannot be used to predict personality traits from task-oriented dialogue, and vice versa. We further analyzed the effects of system utterances, task performance, and the round of dialogue with regard to the prediction accuracy.

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.

Emoji use in social media posts: relationships with personality traits and word usage

Prior research has demonstrated relationships between personality traits of social media users and the language used in their posts. Few studies have examined whether there are relationships between personality traits of users and how they use emojis in their social media posts. Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, which depict faces with expressions, objects, animals, and activities. We conducted a study with two samples (n = 76 and n = 245) in which we examined how emoji use on X (formerly Twitter) related to users' personality traits and language use in posts. Personality traits were assessed from participants in an online survey. With participants' consent, we analyzed word usage in posts. Word frequencies were calculated using the Linguistic Inquiry Word Count (LIWC). In both samples, the results showed that those who used the most emojis had the lowest levels of openness to experience. Emoji use was unrelated to the other personality traits. In sample 1, emoji use was also related to use of words related to family, positive emotion, and sadness and less frequent use of articles and words related to insight. In sample 2, more frequent use of emojis in posts was related to more frequent use of you pronouns, I pronouns, and more frequent use of negative function words and words related to time. The results support the view that social media users' characteristics may be gleaned from the content of their social media posts.

Validating computer-generated measures of linguistic style matching and accommodation in patient-clinician communication

OBJECTIVE

To explore the validity of computer-analyzed linguistic style matching (LSM) in patient-clinician communication.

METHODS

Using 330 transcribed HIV patient encounters, we quantified word use with Linguistic Inquiry and Word Count (LIWC), a dictionary-based text analysis software. We measured LSM by calculating the degree to which clinicians matched patients in the use of LIWC "function words" (e.g., articles, pronouns). We tested associations of different LSM metrics with patients' perceptions that their clinicians spoke similiarly to them.

RESULTS

We developed 3 measures of LSM: 1) at the whole-visit level; (2) at the turn-by-turn level; and (3) using a "rolling-window" approach, measuring matching between clusters of 8 turns per conversant. None of these measures was associated with patient-rated speech similarity. However, we found that increasing trajectories of LSM, from beginning to end of the visit, were associated with higher patient-rated speech similarity (β 0.35, CI 0.06, 0.64), compared to unchanging trajectories.

CONCLUSIONS

Our findings point to the potential value of clinicians' adapting their communication style to match their patients, over the course of the visit.

PRACTICE IMPLICATIONS

With further validation, computer-based linguistic analyses may prove an efficient tool for generating data on communication patterns and providing feedback to clinicians in real time.

PIRNet: Personality-Enhanced Iterative Refinement Network for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) is important for enhancing user experience in human-computer interaction. Unlike vanilla emotion recognition in individual utterances, ERC aims to classify constituent utterances in a dialog into corresponding emotion labels, which makes contextual information crucial. In addition to contextual information, personality traits also affect emotional perception based on psychological findings. Although researchers have proposed several approaches and achieved promising results on ERC, current works in this domain rarely incorporate contextual information and personality influence. To this end, we propose a novel framework to integrate these factors seamlessly, called "Personality-enhanced Iterative Refinement Network (PIRNet)." Specifically, PIRNet is a multistage iterative method. To capture personality influence, PIRNet leverages personality traits to mimic emotional transitions and generates personality-enhanced results. Then we exploit sequence models to capture contextual information in conversations. To verify the effectiveness of our proposed method, we conduct experiments on three benchmark datasets for ERC, that is, IEMOCAP, CMU-MOSI, and CMU-MOSEI. Experimental results demonstrate that our PIRNet succeeds over currently advanced approaches to emotion recognition.

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.

The influence of conversational agents' role and communication style on user experience

Conversational Agents (CAs) are characterized by their roles within a narrative and the communication style they adopt during conversations. Within computer games, users' evaluation of the narrative is influenced by their estimation of CAs' intelligence and believability. However, the impact of CAs' roles and communication styles on users' experience remains unclear. This research investigates such influence of CAs' roles and communication styles through a crime-solving textual game. Four different CAs were developed and each of them was assigned to a role of either witness or suspect and to a communication style than can be either aggressive or cooperative. Communication styles were simulated through a Wizard of Oz method. Users' task was to interact, through real-time written exchanges, with the four CAs and then to identify the culprit, assess the certainty of their judgments, and rank the CAs based on their conversational preferences. In addition, users' experience was evaluated using perceptual measures (perceived intelligence and believability scales) and behavioral measures (including analysis of users' input length, input delay, and conversation length). The results revealed that users' evaluation of CAs' intelligence and believability was primarily influenced by CAs' roles. On the other hand, users' conversational behaviors were mainly influenced by CAs' communication styles. CAs' communication styles also significantly determined users' choice of the culprit and conversational preferences.

'Sleepy Joe' and 'Donald, King of Whoppers': Reality Monitoring and Verbal Deception in the 2020 U.S. Presidential Election Debates

The 2020 U.S. Presidential election was a campaign that could be characterized as 'one of the nastiest presidential campaigns in recent memory,' partly because the general election debates were highly contentious and featured frequent interruptions and several insults and invectives between candidates. This research compared the language used in the debates to fact-checked truths and lies using a Reality Monitoring (RM) deception detection algorithm in Linguistic Inquiry and Word Count (LIWC) to investigate the veracity of real-life high-stakes verbal messages in the political context. We found that overall RM scores were lower and not significantly different between debate language and fact-checked lies, and RM scores were significantly higher in fact-checked truth statements, indicating that most debate language uttered was deceptive. This result supports the finding that the RM algorithm in LIWC distinguishes truth from lies and debate language in the context of politics. The 60.7% classification rate in this study may reflect a problem with the relatively short word counts of fact-checked lie and truth statements, but most probably reflects individual candidates' deviations in RM features used in their statements. Each individual has a style that they use in communication-'the way people talk and write have been recognized as stamps of individual identity.' Even with a corpus of many statements from the same individual candidates, they probably regularly amplify certain features of RM and diminish other features of RM in their truthful and deceptive messages. This is a fruitful area of research that could be explored in future studies.

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer ( https://lingualyzer.com ), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface.

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.

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.

Promotion and prevention regulatory focus LIWC dictionary. Polish adaptation and validation

This article describes the adaptation and validation of a Polish version of the regulatory focus (RF) Linguistic Inquiry and Word Count (LIWC) dictionary. RF theory proposes that there are two types of self-regulation: promotion (focus on gains, growth, and ideals) and prevention (focus on losses, security, and oughts). Apart from self-report questionnaires, one method to measure RF includes a linguistic analysis. LIWC counts the frequency of words from relevant categories and presents the output as a percentage of all words used in a writing sample. RF LIWC contains two categories: promotion (e.g., achieve, ideal) and prevention (e.g., afraid, fail). To test the psychometric properties of our Polish adaptation of the RF LIWC instrument, we performed three studies. In Study 1 (N = 10), experts in RF theory rated the extent to which each dictionary entry was related to promotion and prevention foci. Results showed that words from the promotion category were rated as more promotion than prevention-related, and the pattern was reversed for words from the prevention category. In Study 2 (N = 130) we examined the divergent validity of the instrument by experimentally manipulating RF and testing the writing patterns. When a promotion focus was activated, individuals wrote more words from the promotion than prevention category, and the pattern was reversed in the prevention group. Study 3 (N = 414) investigated whether the promotion and prevention scores obtained through RF LIWC are linked with results obtained using a self-report questionnaire that measures chronic RF. Promotion scores from RF LIWC correlated positively with chronic promotion RF and prevention scores from RF LIWC correlated positively with chronic prevention RF. These preliminary findings provide initial support for the validity of the Polish adaptation of the RF LIWC.

Does resilience mediate the association between mental health symptoms and linguistic markers of trauma processing? Analyzing the narratives of women survivors of intimate partner violence

Intimate partner violence (IPV) is a serious issue for women from all cultures and backgrounds. Studies on the negative consequences of violence suggest that women with a history of abuse are more likely to display depressive and PTSD symptoms. However, recent research has focused on the mechanisms underpinning resilience and the processing of traumatic memories, including linguistic markers and how they may reflect the mental health of traumatized individuals. In this study, we analyzed trauma narratives to investigate whether resilience mediates the impact of PTSD and depression symptoms on five trauma-processing mechanisms (cognitive processing, emotional processing, perceived threat to life, self-perspective, and integration of traumatic memories). Forty-three abused women (mean age = 38.74 years; SD = 9.41) wrote about their traumatic experiences and completed instruments assessing their levels of PTSD, depression, and resilience. We used LIWC software to analyze the women's narratives in terms of linguistic markers of psychological processing. Mediation analysis indicated that resilience fully mediated the impact of mental health symptoms on emotional processing, perceived threat to life, and integration of traumatic memories and partially mediated cognitive processing and self-perspective. We discuss the clinical implications of these findings, emphasizing the need to focus on the resources and strengths of women survivors of abuse in designing targeted psychological interventions.

Spinning Your Wheels: Psychological Overinvolvement and Actigraphy-Assessed Sleep Efficiency Following Marital Separation

BACKGROUND

This study investigated the ways in which adults reflect on their psychological experiences amid a recent marital separation and how these patterns of thought, manifest in language, are associated with self-reported negative affect and actigraphy-assessed sleep disturbance.

METHODS

In a sample of 138 recently separated adults assessed three times over five months, we examined within- and between-person associations among psychological overinvolvement (operationalized using verbal immediacy derived as a function of the language participants used to discuss their relationship history and divorce experience), continued attachment to an ex-partner, negative affect, and sleep efficiency.

RESULTS

The association between psychological overinvolvement and negative affect operated at the within-person level, whereas the associations between psychological overinvolvement and sleep disturbance, as well as negative affect and sleep disturbance, operated at the between-person level.

CONCLUSIONS

These findings shed light on the intraindividual processes that may explain why some people are more susceptible to poor outcomes after separation/divorce than others. Our findings suggest that individuals who express their divorce-related thoughts and feelings in a psychologically overinvolved manner may be at greatest risk for sleep disturbances after marital separation/divorce.

Perseverative thinking is associated with features of spoken language

Perseverative thinking (PT), such as rumination or worry, is a transdiagnostic process implicated in the onset and maintenance of emotional disorders. Existing measures of PT are limited by demand and expectancy effects, cognitive biases, and reflexivity, leading to calls for unobtrusive, behavioral measures. In response, we developed a behavioral measure of PT based on language. A mixed sample of 188 participants with major depressive disorder, generalized anxiety disorder, or no psychopathology completed self-report PT measures. Participants were also interviewed, providing a natural language sample. We examined language features associated with PT, then built a language-based PT model and examined its predictive power. PT was associated with multiple language features, most notably I-usage (e.g., "I", "me"; β = 0.25) and negative emotion language (e.g., "anxiety", "difficult"; β = 0.19). In machine learning analyses, language features accounted for 14% of the variance in self-reported PT. Language-based PT predicted the presence and severity of depression and anxiety, psychiatric comorbidity, and treatment seeking, with effects in the r = 0.15-0.41 range. PT has face-valid linguistic correlates and our language-based measure holds promise for assessing PT unobtrusively. With further development, this measure could be used to passively detect PT for deployment of "just-in-time" interventions.

Implicit Perception of Differences between NLP-Produced and Human-Produced Language in the Mentalizing Network

Natural language processing (NLP) is central to the communication with machines and among ourselves, and NLP research field has long sought to produce human-quality language. Identification of informative criteria for measuring NLP-produced language quality will support development of ever-better NLP tools. The authors hypothesize that mentalizing network neural activity may be used to distinguish NLP-produced language from human-produced language, even for cases where human judges cannot subjectively distinguish the language source. Using the social chatbots Google Meena in English and Microsoft XiaoIce in Chinese to generate NLP-produced language, behavioral tests which reveal that variance of personality perceived from chatbot chats is larger than for human chats are conducted, suggesting that chatbot language usage patterns are not stable. Using an identity rating task with functional magnetic resonance imaging, neuroimaging analyses which reveal distinct patterns of brain activity in the mentalizing network including the DMPFC and rTPJ in response to chatbot versus human chats that cannot be distinguished subjectively are conducted. This study illustrates a promising empirical basis for measuring the quality of NLP-produced language: adding a judge's implicit perception as an additional criterion.

Linguistic Predictors of Psychological Adjustment in Healthcare Workers during the COVID-19 Pandemic

COVID-19 broke out in China in December 2019 and rapidly became a worldwide pandemic that demanded an extraordinary response from healthcare workers (HCWs). Studies conducted during the pandemic observed severe depression and PTSD in HCWs. Identifying early predictors of mental health disorders in this population is key to informing effective treatment and prevention. The aim of this study was to investigate the power of language-based variables to predict PTSD and depression symptoms in HCWs. One hundred thirty-five HCWs (mean age = 46.34; SD = 10.96) were randomly assigned to one of two writing conditions: expressive writing (EW n = 73) or neutral writing (NW n = 62) and completed three writing sessions. PTSD and depression symptoms were assessed both pre- and post-writing. LIWC was used to analyze linguistic markers of four trauma-related variables (cognitive elaboration, emotional elaboration, perceived threat to life, and self-immersed processing). Changes in PTSD and depression were regressed onto the linguistic markers in hierarchical multiple regression models. The EW group displayed greater changes on the psychological measures and in terms of narrative categories deployed than the NW group. Changes in PTSD symptoms were predicted by cognitive elaboration, emotional elaboration, and perceived threat to life; changes in depression symptoms were predicted by self-immersed processing and cognitive elaboration. Linguistic markers can facilitate the early identification of vulnerability to mental disorders in HCWs involved in public health emergencies. We discuss the clinical implications of these findings.

Mapping Linguistic Shifts During Psychological Coping With the COVID-19 Pandemic

How does language change reveal the psychological trajectories of people coping with a COVID-19 infection? This study examined writings on social media over 12 weeks from people who self-reported having tested positive for COVID-19. People used fewer words reflecting anxiety and distancing but more words indicating reinterpretation over time. The language patterns for describing the experience of COVID-19 infections differed from those for describing other unrelated topics. The findings reveal the temporal dynamics of psychological adjustment to an unfolding crisis.

Anti-social behaviour and economic decision-making: Panel experimental evidence in the wake of COVID-19

We systematically examine the acute impact of exposure to a public health crisis on anti-social behaviour and economic decision-making using unique experimental panel data from China, collected just before the outbreak of COVID-19 and immediately after the first wave was overcome. Exploiting plausibly exogenous geographical variation in virus exposure coupled with a dataset of longitudinal experiments, we show that participants who were more intensely exposed to the virus outbreak became more anti-social than those with lower exposure, while other aspects of economic and social preferences remain largely stable. The finding is robust to multiple hypothesis testing and a similar, yet less pronounced pattern emerges when using alternative measures of virus exposure, reflecting societal concern and sentiment, constructed using social media data. The anti-social response is particularly pronounced for individuals who experienced an increase in depression or negative affect, which highlights the important role of psychological health as a potential mechanism through which the virus outbreak affected behaviour.

Toward a Personalized Psychological Counseling Service in Assisted Reproductive Technology Centers: A Qualitative Analysis of Couples' Needs

Infertility may have a very strong emotional impact on individuals, requiring adequate support, but few studies on patients' demands toward psychological support have been conducted. This study aims to explore the emotions related to the infertility and to the Assisted Reproductive Technology (ART) procedure for which patients consider useful a psychological support. A total of 324 women completed a sociodemographic and clinical questionnaire and an open-ended questionnaire on emotional needs for psychological support. The written texts were explored by the Linguistic Inquiry and Word Count (LIWC) programme and linguistic characteristics were related to sociodemographic and anamnestic variables. Specific linguistic features were connected to several individual characteristics. More specifically, differences in linguistic processes emerged comparing women with an age over or under 40 years, women undergoing their first attempts versus more attempts, women undergoing ART with or without gamete donation, and women undergoing ART for male or unknown causes, as well as those undergoing ART for female or both partners' problems. These differences seem to confirm that older age, more attempts, gamete donation, and ART for unknown or male causes are risk factors that may worsen women's psychological well-being. This study contributes to increase the knowledge about the emotional needs of patients undergoing an ART procedure to develop specific psychological intervention programs.

Text-based automatic personality prediction using KGrAt-Net: a knowledge graph attention network classifier

Nowadays, a tremendous amount of human communications occur on Internet-based communication infrastructures, like social networks, email, forums, organizational communication platforms, etc. Indeed, the automatic prediction or assessment of individuals' personalities through their written or exchanged text would be advantageous to ameliorate their relationships. To this end, this paper aims to propose KGrAt-Net, which is a Knowledge Graph Attention Network text classifier. For the first time, it applies the knowledge graph attention network to perform Automatic Personality Prediction (APP), according to the Big Five personality traits. After performing some preprocessing activities, it first tries to acquire a knowing-full representation of the knowledge behind the concepts in the input text by building its equivalent knowledge graph. A knowledge graph collects interlinked descriptions of concepts, entities, and relationships in a machine-readable form. Practically, it provides a machine-readable cognitive understanding of concepts and semantic relationships among them. Then, applying the attention mechanism, it attempts to pay attention to the most relevant parts of the graph to predict the personality traits of the input text. We used 2467 essays from the Essays Dataset. The results demonstrated that KGrAt-Net considerably improved personality prediction accuracies (up to 70.26% on average). Furthermore, KGrAt-Net also uses knowledge graph embedding to enrich the classification, which makes it even more accurate (on average, 72.41%) in APP.

Linguistic distancing predicts response to a digital single-session intervention for adolescent depression

Examining the linguistic characteristics of youths' writing may be a promising method for detecting youth who are struggling. In this study, we examined linguistic patterns of adolescent responses to writing prompts in a large, well-powered trial of an evidence-based, digital single-session intervention teaching malleability beliefs about personal traits and symptoms ("growth mindset"). Participants who completed the intervention as part of a larger randomized control trial were included in this preregistered study (n = 638, https://osf.io/zqmxt). Participants' responses were processed using Linguistic Inquiry and Word Count. We tested correlations between linguistic variables (i.e., linguistic distancing, positive affect, negative affect, insight, certainty), baseline outcome variables, post-intervention outcome variables, and 3-month post-intervention outcome variables. We also used Least Absolute Shrinkage and Selection Operator (LASSO) regression models to identify key predictors of treatment outcomes. As hypothesized, greater use of linguistic distancing was associated with lower levels of baseline hopelessness and higher levels of perceived agency. Additionally, per LASSO models including all linguistic variables, greater use of linguistic distancing predicted larger reductions in depressive symptoms from baseline to three-month follow-up. Linguistic distancing appeared to account for 27% of the variance in depression trajectories when also accounting for baseline depression. CLINICAL REGISTRATION NO: NCT04634903.

State of Mind Assessment in Relation to Adult Attachment and Text Analysis of Adult Attachment Interviews in a Sample of Patients with Anorexia Nervosa

BACKGROUND

Attachment theory represents one of the most important references for the study of the development of an individual throughout their life cycle and provides the clinician with a profound key for the purposes of understanding the suffering that underlies severe psychopathologies such as eating disorders. As such, we conducted a cross-sectional study with a mixed-methods analysis on a sample of 32 young women with anorexia nervosa (AN); this study was embedded in the utilized theoretical framework with the following aims: 1. to evaluate the state of mind (SoM) in relation to adult attachment, assuming a prevalence of the dismissing (DS) SoM and 2. to analyze the linguistic attachment profile emerging from the transcripts of the AAIs.

METHODS

Interviews were transcribed verbatim, coded, and analyzed using the linguistic inquiry and word count (LIWC) method.

RESULTS

The results were observed to be consistent with the referenced literature. The prevalence of a DS SoM (68.75%) is observed in the study sample, whereas the results of the lexical analysis of the stories deviate from expectations. Notably, the lexical results indicate the coexistence of the dismissing and entangled aspects at the representational level.

CONCLUSIONS

The study results suggest a high level of specificity in the emotional functioning of patients with AN, with a focusing on a pervasive control of emotions that is well illustrated by the avoidant/ambivalent (A/C) strategy described in Crittenden's dynamic-maturational model. These findings and considerations have important implications for clinical work and treatment, which we believe must be structured on the basis of starting from a reappraisal of emotional content.

Language of violence: Do words matter more than we think?

Firearm violence is a leading cause of morbidity and mortality among young adults. Identification of intervention targets is crucial to developing and implementing effective prevention efforts. Hospital Violence Intervention Programs (HVIPs) have used a multiprong social care approach to mediate the cycle of interpersonal violence. One struggle continually encountered is how to change the conversation around the future. Speech patterns have been associated with health outcomes and overall behavior modification. During violence prevention efforts, young victims of violence say things such as ‘I’m living on borrowed time’ and ‘why should I worry about getting an education when I’ll likely die soon anyway?’ Such speech patterns may contribute to the cycle of violence and increase the likelihood of reinjury. Presented is a narrative review of the impact language has on health outcomes and how psychotherapy may be able to change thought patterns, alter language structure, and ultimately reduce risk of reinjury.

The biopsychosocial model of health posits that a person’s health is dictated by a combination of biological, psychological, and social factors. By understanding that language exists in the personal context, it can serve as both an indicator and a tool for targeted interventions. Cognitive–behavioral therapy (CBT) works by retraining thought and speech patterns to affect change in emotion, physiology, and behavior. It is proposed here that CBT could be used in the HVIPs’ multidisciplinary case management model by involving trained psychotherapists. Language is an important indicator of a patient’s psychological state and approach to life-changing decisions. As such, language alteration through CBT could potentially be used as a novel method of injury prevention. This concept has not before been explored in this setting and may be an effective supplement to HVIPs’ success.

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.

The Implicit Achievement Motive in the Writing Style

Linguistic theories and research indicate that unconscious processes should influence the content, but moreover also the way how things are expressed. As the first is well researched and the second is almost neglected, I want to assess how the writing style of a person is related to the implicit achievement motive and its two components hope of success (HS) and fear of failure (FF). Therefore, thematic apperception test/picture story exercise responses of 2942 persons were analyzed regarding the three writing style features (1) syntax, (2) nominal/verbal writing, and (3) function words. According to the assumptions, the results of two independent measures (Stanford Parser and LIWC) show that a verbal fluent writing style with simple syntax is associated with HS, whereby FF-motivated people show nominal writing with interjections, conjunctions, and complex punctuations.

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.

“Dear CEO and Board”: How Activist Investors’ Confidence in Tone Influences Campaign Success

Activist hedge funds represent the most potent form of financial activism. Yet we do not fully understand how these activist investors, despite holding only a small stake in target firms, are able to influence management and the board to acquiesce to their demands, especially given the large uncertainty that their demands will improve shareholder value. Building on impression management (IM) theory, we propose that activist investors who express their concerns and demands with high confidence in their letters to target firms are likely to be perceived as knowledgeable and competent by the firm’s other shareholders, thus influencing the response of the firm’s management and board. In support of this theoretical proposition, results of our empirical analysis of 475 U.S. activist campaigns against U.S. companies between 2007 and 2019 suggest that confidence in tone in an activist’s letter, as a form of self-promotion, is positively associated with campaign success. We also observe that the positive association between confidence in tone in activist letters and campaign success is less for activists with greater success in prior campaigns and in campaigns with multiple activists. Our paper contributes to financial activism research by showing that activists’ verbal impression management can serve as an effective influence tactic in their campaigns. Our study also contributes to the emerging research stream on verbal IM by introducing a language attribute, confidence in tone, that has not been studied in management research and is distinct from past constructs examined in verbal IM research.

Linguistic predictors from Facebook postings of substance use disorder treatment retention versus discontinuation

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

Testing associations between language use in descriptions of playfulness and age, gender, and self-reported playfulness in German-speaking adults

Adult playfulness describes individual differences in (re)framing everyday situations as personally interesting, and/or entertaining, and/or intellectually stimulating. We aimed at extending initial evidence on the interconnectedness between language use and adult playfulness by asking 264 participants (M = 26.5 years, SD = 9.7; 66.7% women) to provide written descriptions of their understanding of playfulness (mean length: 30.6 words; SD = 24.1) and collected self-reports of their playfulness. We used the Linguistic Inquiry and Word Count methodology to quantitatively analyze the language use in these descriptions and tested the associations with individual differences in participants’ age, gender, and playfulness. While higher expressions in all measures of playfulness did go along with writing more content when describing playfulness (rs = 0.13 to 0.25), facet-wise analyses revealed differential findings (e.g., intellectual playfulness relates to using words describing cognitive processes); but the effects were small. We found that being a women and younger age were related to writing longer texts (0.13 ≤ rs ≤ 0.24), and we discovered additional associations between certain LIWC categories and age and gender. Our study expands the knowledge about adult playfulness and its manifestations in natural language use. We embed our findings into previous research and discuss limitations and potential approaches for replication studies.

Integrated entitymetrics analysis for health information on bipolar disorder using social media data and scientific literature

Purpose

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Design/methodology/approach

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Findings

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Originality/value

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

Do we behave differently on Twitter and Facebook: Multi-view social network user personality profiling for content recommendation

Human personality traits are key drivers behind our decision making, influencing our lives on a daily basis. Inference of personality traits, such as the Myers-Briggs personality type, as well as an understanding of dependencies between personality traits and user behavior on various social media platforms, is of crucial importance to modern research and industry applications such as recommender systems. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse; the impact of different social network data on profiling performance and of personality traits on applications such as recommender systems is yet to be evaluated. Furthermore, large-scale datasets are also lacking in the research community. To fill these gaps, in this work we develop a novel multi-view fusion framework PERS that infers Myers-Briggs personality type indicators. We evaluate the results not just across data modalities but also across different social networks, and also evaluate the impact of inferred personality traits on recommender systems. Our experimental results demonstrate that PERS is able to learn from multi-view data for personality profiling by efficiently leveraging highly varied data from diverse social multimedia sources. Furthermore, we demonstrate that inferred personality traits can be beneficial to other industry applications. Among other results, we show that people tend to reveal multiple facets of their personality in different social media avenues. We also release a social multimedia dataset in order to facilitate further research on this direction.

Contrasting compulsive behaviour: Computerized text analysis of compulsion narratives

Those who gamble compulsively, and those who shop or buy in a compulsive manner share a number of common characteristics, stemming from similar impulse-control issues. As such, it is predicted that a lexical analysis of personal narratives of compulsion would share similarities. Using secondary data from an online mental health forum, Psychforums, the research analyzed narratives of compulsive gambling (n = 199) and compulsive buying (n = 196) using the automated text analysis tool, LIWC. The results indicated that compulsive buying narratives rated significantly higher in clout and emotional tone and significantly lower in authenticity, with no significant differences noted in analytical thinking between the two compulsion narratives. Recommendations for future research include that demographic variables be incorporated and that narratives sourced from different online platforms should be contrasted.

The Story of Us: Older and Younger Couples' Language Use and Emotional Responses to Jointly Told Relationship Narratives

OBJECTIVES

Social-emotional well-being is said to improve over adulthood, and studies of couples' age differences have focused primarily on marital conflict. The way couples discuss their relationship story predicts marital quality among newlyweds and long-married couples alike, yet older and younger couples' accounts have never been compared. The current study examined age differences in couples' use of I/we-talk, emotion words, and immediacy (i.e., an urgent and unresolved style) during a relationship history discussion and their subsequent mood reactivity and appraisals.

METHOD

Married couples (N = 186 individuals within 93 couples, aged 22-77) recounted the story of their relationship then rated the discussion and their negative mood. Mediation models assessed the 3 linguistic features as parallel dyadic mediators linking couple age to negative mood responses and appraisals, controlling for global marital satisfaction, and baseline negative mood. Secondary analyses examined partners' concordance in language use.

RESULTS

Compared with younger couples, older couples used more positive than negative words and less immediacy which, in turn, was associated with husbands' and wives' less negative mood and more positive appraisals, only among husbands. Partners in older couples used more similar I/we-talk and emotional language, but these were unrelated to mood or appraisals.

DISCUSSION

This study extends our understanding of how marital interactions differ by age in the understudied context of relationship history discussions, which may grow increasingly important for couples' well-being with older age. Findings broadly align with social-emotional aging theories and uncover novel linguistic features relevant to the age-related emotional benefits of joint reminiscing.

Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.

The development and validation of the Romanian version of Linguistic Inquiry and Word Count 2015 (Ro-LIWC2015)

Today, performing automatic language analysis to extract meaning from natural language is one of the top-notch directions in social science research, but it can be challenging. Linguistic Inquiry and Word Count 2015 (LIWC2015; Pennebaker et al. 2015) is one of the most versatile, yet easy to master instruments to transform any text into data, meeting the needs of psychologists who are not usually proficient in data science. Moreover, LIWC2015 is already available in multiple languages, which opens the door to exciting intercultural quests. The current article introduces the first Romanian version of LIWC2015, Ro-LIWC2015, and thus, contributes to the line of research concerning multilingual analysis. Throughout the paper, we describe the challenges of creating the Romanian dictionary and discuss other linguistics aspects, which could be useful for new adaptations of LIWC2015. Also, we present the results of two studies for assessing the criterion validity of Ro-LIWC2015. The first study focuses on the consistency between the Romanian and the English dictionaries in analyzing a corpus of books. The second study tests whether Ro-LIWC2015 can acquire linguistic differences in contrasting corpora. For this purpose, we analyzed posts from help-seeking forums for anxiety, depression, and health issues, and leveraged supervised learning to address several classification problems. The selected algorithm allows feature ranking, which facilitates more thorough interpretations. The linguistic markers extracted with Ro-LIWC2015 mirrored a number of disorder-specific features of depression and anxiety. Given the obtained results, this research encourages the use of Ro-LIWC2015 for hypothesis testing.

Million dollar personality: a systematic literature review on personality in crowdfunding

Expressed personality traits can play a pivotal role in convincing investors in crowdfunding. Our study answers the research question: What is the current body of knowledge regarding the relationship between personality factors and crowdfunding success and where are knowledge gaps where the literature is silent? In our literature review, we therefore analyze and categorize (1) the results provided by quantitative studies on the relationship between the personality of entrepreneurs and crowdfunding success and (2) the research gaps identified by the authors investigating personality in crowdfunding. We find that studies investigating the entrepreneur's personality, i.e. the Big Five, other baseline personality traits (self-efficacy, innovativeness, locus of control, and need for achievement) and the Dark Triad, find positive relationships between openness and crowdfunding success, while narcissism shows an inverted u-shaped relationship with crowdfunding success across articles. However, the effects of other personality traits on crowdfunding success are largely inconclusive. Further, we identify four main gaps in the literature. First, future studies should examine non-linear relationships between expressed personality traits and crowdfunding success. Second, there is a need for more studies that employ different methods like qualitative or mixed-method approaches. Third, replication studies in similar and different contexts are urgently needed. Fourth, a plurality of personality perspectives would strengthen future research (e.g., investor perspective, third party perspective). To our knowledge this is the first literature review of personality traits in crowdfunding. Our work aims to enrich our understanding of individual-level components in the underexplored alternative finance market.

Negotiator Consistency, Counterpart Consistency, and Reciprocity in Behavior Across Partners: A Round-Robin Study

This research takes a new perspective on the long-standing mystery of personality in negotiation, which has seen decades of null and inconsistent findings. Grounded in interactionist theories defining personality as consistency in behaviors when placed multiple times in the same situation, the investigation examines consistency in individuals’ behavioral profiles across negotiation partners. Such consistency supports efforts to identify enduring dispositions that can predict objective and subjective outcomes. A comprehensive set of behaviors related to negotiation was coded in a round-robin study using groups of four negotiators who each took turns working with each other person. Analysis using Kenny’s Social Relations Model revealed evidence for extensive actor effects (indicating consistency in negotiators’ behavior), as well as moderate partner effects (indicating consistency in counterparts’ behavior) and dyadic reciprocity (indicating similarity in the behavior of negotiators and counterparts). We conclude with optimism for investigating the effects of personality in negotiation.

Examining the Examiners: How Medical Death Investigators Describe Suicidal, Homicidal, and Accidental Death

This study describes differences in medicolegal death investigators' written descriptions for people who died by homicide, suicide, or accident. We evaluated 17 years of death descriptions from a midsized metropolitan midwestern county in the United States to assess how death investigators psychologically respond to different manners of death (N = 10,408 cases). Automated text analyses suggest investigators describe accidental deaths with more immediacy relative to homicides, while they also described suicidal deaths in less emotional terms than homicides as well. These data suggest medicolegal death investigators have different psychological reactions to circumstances and manners of death as indicated by their professional writing. Future research may surface context-specific psychological reactions to vicarious trauma that could inform the design or personalization of workplace-coping interventions.

Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality

Analysts, managers, and policymakers are interested in predictive analytics capable of offering better foresight. It is generally accepted that in forecasting scenarios involving organizational policies or consumer decision making, personal characteristics, including personality, may be an important predictor of downstream outcomes. The inclusion of personality features in forecasting models has been hindered by the fact that traditional measurement mechanisms are often infeasible. Text-based personality detection has garnered attention due to the public availability of digital textual traces, however state-of-the-art models proposed by IBM, Google, Facebook, and academic research are not accurate enough to be used for downstream real-world forecasting tasks. We propose a novel text-based personality measurement approach that improves detection of personality dimensions by 10–20 percentage points relative to the best existing methods developed in industry and academia. Using case studies in the finance and health domains, we show that more accurate text-based personality detection can translate into significant improvements in downstream applications such as forecasting future firm performance or predicting pandemic infection rates. Our findings have important implications for managers focused on enabling, producing, or consuming predictive analytics for enhanced agility in decision making.