Emotional experiences of ghosting

Although ghosting (i.e., unilaterally ending a relationship by ceasing communication) has only recently entered the lexicon, it is a regularly used form of relationship dissolution. However, little research has examined the emotional experiences of ghosting, particularly the experiences of those on both sides of the ghosting process. In a multi-method study, participants who had both ghosted and been ghosted in previous romantic relationships (N = 80) provided narratives of their experiences and completed questionnaires. The narrative responses were analyzed by coders and by using LIWC. Ghosters and ghostees used similar overall levels of positively and negatively valenced words to describe their experiences, but ghosters were more likely to express guilt and relief, whereas ghostees were more likely to express sadness and hurt feelings. Ghostees also experienced more of a threat to their fundamental needs - control, self-esteem, belongingness, meaningful existence - than ghosters.

Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter

Introduction

The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints. As a result, there is an increasing interest in accessing social media data to study the effects of wildfires on mental health.

Methods

In this study, we focused on Twitter users affected by the California Tubbs Fire in 2017 to extract data signals related to emotional well-being and mental health. Our analysis aimed to investigate tweets posted during the Tubbs Fire disaster to gain deeper insights into their impact on individuals. Data were collected from October 8 to October 31, 2017, encompassing the peak activity period. Various analytical methods were employed to explore word usage, sentiment, temporal patterns of word occurrence, and emerging topics associated with the unfolding crisis.

Results

The findings show increased user engagement on wildfire-related Tweets, particularly during nighttime and early morning, especially at the onset of wildfire incidents. Subsequent exploration of emotional categories using Linguistic Inquiry and Word Count (LIWC) revealed a substantial presence of negative emotions at 43.0%, juxtaposed with simultaneous positivity in 23.1% of tweets. This dual emotional expression suggests a nuanced and complex landscape, unveiling concerns and community support within conversations. Stress concerns were notably expressed in 36.3% of the tweets. The main discussion topics were air quality, emotional exhaustion, and criticism of the president's response to the wildfire emergency.

Discussion

Social media data, particularly the data collected from Twitter during wildfires, provides an opportunity to evaluate the psychological impact on affected communities immediately. This data can be used by public health authorities to launch targeted media campaigns in areas and hours where users are more active. Such campaigns can raise awareness about mental health during disasters and connect individuals with relevant resources. The effectiveness of these campaigns can be enhanced by tailoring outreach efforts based on prevalent issues highlighted by users. This ensures that individuals receive prompt support and mitigates the psychological impacts of wildfire disasters.

A search for commonalities in defining the common good: Using folk theories to unlock shared conceptions

Throughout the course of scholarly history, some concepts have been notoriously hard to define. The 'common good' is one such concept. While the common good has a long and contested scholarly history, social psychology research on folk theories - lay beliefs that represent an individual's informal and subjective understanding of the world - may provide a key for unlocking this nebulous concept. In the current paper, we analysed lay definitions of the common good using the linguistic inquiry and word count's meaning extraction method. From a nationally representative Australian sample of open-ended text responses (n = 14,303), we uncovered a consistent conceptual structure, with nine themes corresponding to three core aspects: (i) outcomes and objects, (ii) principles and processes and (iii) stakeholders and beneficiaries. From this, we developed a working definition of the folk concept of the common good: 'achieving the best possible outcome for the largest number of people, which is underpinned by decision-making that is ethically and morally sound and varies by the context in which the decisions are made'. A working definition benefits the academic community and society more broadly, particularly when diverse stakeholders come together to act for the common good to address shared challenges.

Moral universals: A machine-reading analysis of 256 societies

What is the cross-cultural prevalence of the seven moral values posited by the theory of "morality-as-cooperation"? Previous research, using laborious hand-coding of ethnographic accounts of ethics from 60 societies, found examples of most of the seven morals in most societies, and observed these morals with equal frequency across cultural regions. Here we replicate and extend this analysis by developing a new Morality-as-Cooperation Dictionary (MAC-D) and using Linguistic Inquiry and Word Count (LIWC) to machine-code ethnographic accounts of morality from an additional 196 societies (the entire Human Relations Area Files, or HRAF, corpus). Again, we find evidence of most of the seven morals in most societies, across all cultural regions. The new method allows us to detect minor variations in morals across region and subsistence strategy. And we successfully validate the new machine-coding against the previous hand-coding. In light of these findings, MAC-D emerges as a theoretically-motivated, comprehensive, and validated tool for machine-reading moral corpora. We conclude by discussing the limitations of the current study, as well as prospects for future research.

Unraveling the Cross-Cultural Differences in Online Expression of Social Anxiety in Online Support Communities

Individuals suffering from social anxiety disorder (SAD) are increasingly turning to online support communities for self-disclosure and social support. Despite the extensive body of research on online mental health discourses, the cultural nuances within SAD-related discussions remain underexplored. In this study, we examine the cultural differences in online expression of social anxiety by analyzing individuals' self-disclosure and support-seeking behaviors in social media posts. Using two-week data (n = 1,681) from two SAD support communities on the Reddit and Douban groups, we used both qualitative thematic analysis and quantitative semantic analysis to discern prevalent themes and linguistic attributes characterizing these online expressions. Our findings not only uncover common themes such as sharing personal experiences and seeking mutual validations in both communities but also identify their divergences, as Western users primarily sought advice and information in posts, whereas Chinese users were more inclined toward networking. Cultural variations in language use were evident, particularly in individuals' affect and their expression of personal and social concerns. Western users were more likely to convey negative emotions and delve into personal matters related to SAD, whereas Chinese users tended to grapple more with workplace anxieties. This study contributes to the cultural understanding of online mental health discourses and offers insights for crafting culturally sensitive interventions and supports for people with SAD.

What words can tell us about social determinants of mental health: A multi-method analysis of sentiment towards migration experiences and community life in Lima, Perú

To support resilience in contexts of migration, a deeper understanding of the experiences of both receiving communities and migrants is required. Research on the impacts of migration on community life is limited in contexts with high internal migration (i.e., migrating within one's country of origin). Evidence suggests that cultural similarity, community relationships, and access to resources may be protective factors that could be leveraged to support the mental health of internal migrants. The current study uses data drawn from a sample of pregnant Peruvian women (N = 251), 87 of whom reported being internal migrants and 164 of whom reported being from the locale of the study (Lima, Perú). The aim was to better understand the social experience of internal migration for both local and migrant women. Inductive thematic analysis was used to examine migration experience and perceived impact of migration on community life. Internal migrants discussed three themes relative to their experiences: motivations, adjustment, and challenges. Experiences of women in receiving communities consisted of four themes related to migration: positive, negative, neutral, and mixed perceptions. Linguistic Inquiry and Word Count (LIWC-22) software was also used to assess sentiment towards migration. Across both analytic methods, migration motivations and perceptions were multifaceted and migrants reported a wide range of challenges before, during, and after migration. Findings indicated that attitudes toward migration are broadly positive, and that there is a more positive appraisal of migration's impact on the community life for internal as opposed to international migration.

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure

Introduction

The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts.

Aim

This study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count).

Materials and methods

A sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them.

Assessment tools

(1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 - Linguistic Inquiry and Word Count - a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use.

Results

Depressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form - not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money.

Conclusion

Depressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.

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.

Language use on Twitter reflects social structure and social disparities

Large-scale mental health assessments increasingly rely upon user-contributed social media data. It is widely known that mental health and well-being are affected by minority group membership and social disparity. But do these factors manifest in the language use of social media users? We elucidate this question using spatial lag regressions. We examined the county-level (N = 1069) associations of lexical indicators linked to well-being and mental health, notably depression (e.g., first-person singular pronouns, negative emotions) with markers of social disparity (e.g., the Area Deprivation Index-3) and ethnicity, using a sample of approximately 30 million content-coded tweets (U.S. county-level aggregation). Results confirmed most expected associations: County-level lexical indicators of depression are positively linked with county-level area disparity (e.g., economic hardship and inequity) and percentage of ethnic minority groups. Predictive validity checks show that lexical indicators are related to future health and mental health outcomes. Lexical indicators of depression and adjustment coded from tweets aggregated at the county level could play a crucial role in prioritizing public health campaigns, particularly in socially deprived counties.

Can generative AI infer thinking style from language? Evaluating the utility of AI as a psychological text analysis tool

Generative AI, short for Generative Artificial Intelligence, a class of artificial intelligence systems, is not currently the choice technology for text analysis, but prior work suggests it may have some utility to assess dynamics like emotion. The current work builds upon this empirical foundation to consider how analytic thinking scores from a large language model chatbot, ChatGPT, were linked to analytic thinking scores from dictionary-based tools like Linguistic Inquiry and Word Count (LIWC). Using over 16,000 texts from four samples and tested against three prompts and two large language models (GPT-3.5, GPT-4), the evidence suggests there were small associations between ChatGPT and LIWC analytic thinking scores (meta-analytic effect sizes: .058 < rs < .304; ps < .001). When given the formula to calculate the LIWC analytic thinking index, ChatGPT performed incorrect mathematical operations in 22% of the cases, suggesting basic word and number processing may be unreliable with large language models. Researchers should be cautious when using AI for text analysis.

How Do Care Partners of People with Rare Dementia Use Language in Online Peer Support Groups? A Quantitative Text Analysis Study

We used quantitative text analysis to examine conversations in a series of online support groups attended by care partners of people living with rare dementias (PLWRD). We used transcripts of 14 sessions (>100,000 words) to explore patterns of communication in trained facilitators' (n = 2) and participants' (n = 11) speech and to investigate the impact of session agenda on language use. We investigated the features of their communication via Poisson regression and a clustering algorithm. We also compared their speech with a natural speech corpus. We found that differences to natural speech emerged, notably in emotional tone (d = -3.2, p < 0.001) and cognitive processes (d = 2.8, p < 0.001). We observed further differences between facilitators and participants and between sessions based on agenda. The clustering algorithm categorised participants' contributions into three groups: sharing experience, self-reflection, and group processes. We discuss the findings in the context of Social Comparison Theory. We argue that dedicated online spaces have a positive impact on care partners in combatting isolation and stress via affiliation with peers. We then discuss the linguistic mechanisms by which social support was experienced in the group. The present paper has implications for any services seeking insight into how peer support is designed, delivered, and experienced by participants.

Diurnal patterns in Twitter sentiment in Italy and United Kingdom are correlated

Diurnal variations in indicators of emotion have been reliably observed in Twitter content, but confirmation of their circadian nature has not been possible due to the many confounding factors present in the data. We report on correlations between those indicators in Twitter content obtained from 9 cities of Italy and 54 cities in the United Kingdom, sampled hourly at the time of the 2020 national lockdowns. This experimental setting aims at minimizing synchronization effects related to television, eating habits, or other cultural factors. This correlation supports a circadian origin for these diurnal variations, although it does not exclude the possibility that similar zeitgebers exist in both countries including during lockdowns.

Linguistic Variables and Gender Differences Within a Messenger-Based Psychosocial Chat Counseling Service for Children and Adolescents: Cross-Sectional Study

BACKGROUND

Text messaging is widely used by young people for communicating and seeking mental health support through chat-based helplines. However, written communication lacks nonverbal cues, and language usage is an important source of information about a person's mental health state and is known to be a marker for psychopathology.

OBJECTIVE

The aim of the study was to investigate language usage, and its gender differences and associations with the presence of psychiatric symptoms within a chat counseling service for adolescents and young adults.

METHODS

For this study, the anonymized chat content of a German messenger-based psychosocial chat counseling service for children and adolescents ("krisenchat") between May 2020 and July 2021 was analyzed. In total, 661,131 messages from 6962 users were evaluated using Linguistic Inquiry and Word Count, considering the following linguistic variables: first-person singular and plural pronouns, negations, positive and negative emotion words, insight words, and causation words. Descriptive analyses were performed, and gender differences of those variables were evaluated. Finally, a binary logistic regression analysis examined the predictive value of linguistic variables on the presence of psychiatric symptoms.

RESULTS

Across all analyzed chats, first-person singular pronouns were used most frequently (965,542/8,328,309, 11.6%), followed by positive emotion words (408,087/8,328,309, 4.9%), insight words (341,460/8,328,309, 4.1%), negations (316,475/8,328,309, 3.8%), negative emotion words (266,505/8,328,309, 3.2%), causation words (241,520/8,328,309, 2.9%), and first-person plural pronouns (499,698/8,328,309, 0.6%). Female users and users identifying as diverse used significantly more first-person singular pronouns and insight words than male users (both P<.001). Negations were significantly more used by female users than male users or users identifying as diverse (P=.007). Similar findings were noted for negative emotion words (P=.01). The regression model of predicting psychiatric symptoms by linguistic variables was significant and indicated that increased use of first-person singular pronouns (odds ratio [OR] 1.05), negations (OR 1.11), and negative emotion words (OR 1.15) was positively associated with the presence of psychiatric symptoms, whereas increased use of first-person plural pronouns (OR 0.39) and causation words (OR 0.90) was negatively associated with the presence of psychiatric symptoms. Suicidality, self-harm, and depression showed the most significant correlations with linguistic variables.

CONCLUSIONS

This study highlights the importance of examining linguistic features in chat counseling contexts. By integrating psycholinguistic findings into counseling practice, counselors may better understand users' psychological processes and provide more targeted support. For instance, certain linguistic features, such as high use of first-person singular pronouns, negations, or negative emotion words, may indicate the presence of psychiatric symptoms, particularly among female users and users identifying as diverse. Further research is needed to provide an in-depth look into language processes within chat counseling services.

Alexithymia in the Narratization of Romantic Relationships: The Mediating Role of Fear of Intimacy

PURPOSE

The purpose of the study was to verify the hypothesis concerning the relationship between alexithymia and selected indicators used to describe emotional events, specifically romantic relationships. Alexithymia, due to significant distortions in cognitive processing of emotional content, is demonstrated by poor recognition of emotions in oneself and others and, as a result, by deficits in empathy, avoidance of social relationships, and deficits in the ability to mentalize. Differences in narrations were tested by alexithymia levels (high vs. low) and the relation between specific narration features and individual alexithymia factors, i.e., difficulties in identifying emotions, difficulties in verbalising emotions, and externally oriented thinking.

METHOD

A total of 356 people who had been in a romantic relationship for at least six months participated in the study. The TAS-20 was applied to measure alexithymia, and the FIS questionnaire was used to investigate anxiety in close relationships. Participants were asked to freely describe the romantic relationship they were in at that moment. The Linguistic Inquiry Word Count (LIWCLIWC2015 v1.6-unlimited duration academic licence) software was used for content analysis. The study was conducted online.

RESULTS

On the basis of the analyses conducted, high levels of alexithymia were found to be significantly associated with a lower total number of words used in narrative, a lower number relating to positive emotions, a lower number relating to causation and insight, and a higher number relating to negative emotions. Various results were obtained for individual dimensions of alexithymia in relation to the LIWC categories and the mediating role of fear of intimacy. For the difficulty identifying feelings (DIF), a significant mediating effect was observed only for words associated with negative emotions, whereas for the difficulty describing feelings (DDF), significant mediating effects were found for words relating to negative emotions and causality. In the case of externally oriented thinking (EOT), significant mediating effects were obtained for all analysed categories from LIWC.

Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study

BACKGROUND

Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit.

OBJECTIVE

This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories.

METHODS

Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services.

RESULTS

There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function.

CONCLUSIONS

Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.

The Language of Compassion: Hospital Chaplains' Compassion Capacity Reduces Patient Depression via Other-Oriented, Inclusive Language

Objectives

Although hospital chaplains play a critical role in delivering emotional and spiritual care to a broad range of both religious and non-religious patients, there is remarkably little research on the best practices or "active ingredients" of chaplain spiritual consults. Here, we examined how chaplains' compassion capacity was associated with their linguistic behavior with hospitalized inpatients, and how their language in turn related to patient outcomes.

Methods

Hospital chaplains (n = 16) completed self-report measures that together were operationalized as self-reported "compassion capacity." Next, chaplains conducted consultations with inpatients (n = 101) in five hospitals. Consultations were audio-recorded, transcribed, and analyzed using Linguistic Inquiry Word Count (LIWC). We used exploratory structural equation modeling to identify associations between chaplain-reported compassion capacity, chaplain linguistic behavior, and patient depression after the consultation.

Results

We found that compassion capacity was significantly associated with chaplains' LIWC clout scores, a variable that reflects a confident leadership, inclusive, and other-oriented linguistic style. Clout scores, in turn, were negatively associated with patient depression levels controlling for pre-consult distress, indicating that patients seen by chaplains displaying high levels of clout had lower levels of depression after the consultation. Compassion capacity exerted a statistically significant indirect effect on patient depression via increased clout language.

Conclusions

These findings inform our understanding of the linguistic patterns underlying compassionate and effective chaplain-patient consultations and contribute to a deeper understanding of the skillful means by which compassion may be manifest to reduce suffering and enhance well-being in individuals at their most vulnerable.

Clues from caregiver emotional language usage highlight the link between putative social environment and the psychosis-risk syndrome

Familial emotional word usage has long been implicated in symptom progression in schizophrenia. However, few studies have examined caregiver emotional word usage prior to the onset of psychosis, among those with a clinical high-risk (CHR) syndrome. The current study examined emotional word usage in a sample of caregivers of CHR individuals (N = 37) and caregivers of healthy controls (N = 40) and links with clinical symptoms in CHR individuals. Caregivers completed a speech sample task in which they were asked to speak about the participant; speech samples were then transcribed and analyzed for general positive (e.g. good) and negative (e.g., worthless) emotional words as well as words expressing three specific negative emotions (i.e., anxiety, anger, and sadness) using Linguistic Inquiry and Word Count (LIWC). Findings indicated that (1) CHR caregivers used more negative and anxiety words compared to control caregivers; and (2) less positive word usage among CHR caregivers were related to more positive symptomatology among CHR individuals. These findings point toward the utility of automated language analysis in assessing the intersections between caregiver emotional language use and psychopathology.

Cultural differences in the efficacy of unexpected questions, sketching, and timeline methods in eliciting cues to deception

Asking unexpected questions, asking the interviewee to sketch the room, and asking the interviewee to make a timeline are techniques that have been shown to help an interviewer detect deceit. However, evidence of the efficacy of these techniques comes from studies of North American and North-West European participants, who are on average more individualistic (i.e., value individual achievements and uniqueness over group achievements) than people from other parts of the world. In two experiments involving participants with individualistic and collectivistic cultural backgrounds, we provide a more culturally diverse test of these techniques. Specifically, this study describes two experiments that investigated these interviewing techniques with people who are recent migrants to the UK. Experiment 1 used the LIWC categories "I," "we," "cognitive processes," and "social processes" as the dependent variables; Experiment 2 measured details provided in a sketch and a timeline. The results show no effects of veracity in either of these experiments, although various effects of cultural differences in the outcome variables were observed. This suggests that cues to deception may not necessarily generalize to people from different cultural backgrounds. These results highlight the importance of conducting lie detection research across different countries and cultures.

Corrigendum: The language of healthcare worker emotional exhaustion: a linguistic analysis of longitudinal survey

[This corrects the article DOI: 10.3389/fpsyt.2022.1044378.].

Linguistic predictors of symptom change in an internet-based cognitive behavioural intervention for prolonged grief symptoms

This study investigates linguistic predictors of reduction in prolonged grief symptoms following a writing intervention in an internet-based cognitive behavioural therapy for people bereaved by cancer. Data stem from a randomized control clinical trial with 70 people. The Linguistic Inquiry and Word Count program was used to analyse patient language. Absolute change scores and reliable change index were used to calculate reduction in grief symptoms and clinical significant change. Best subset regression and Mann-Whitney U tests were conducted. A higher reduction of prolonged grief symptoms was correlated with more social words in the first module (β = -.22, p = .042), less risk (β = .33, p = .002) and body words (β = .22, p = .048) in the second module and more time words in the third module (β = -.26, p = .018). Patients with clinically significant change showed a higher median in function words in the first module (p = .019), a lower median in risk words in the second module (p = .019) and a higher median in assent words in the last module (p = .014) compared to patients without clinically significant change. Findings suggest that it may be beneficial for therapists to encourage a more detailed description of patients' relationship with their deceased relative during the first module, a change in perspective during the second module and a summary of past, present and future aspects at the end of therapy. Future studies should include mediation analyses to allow causal attribution of the studied effects.

Linguistic markers of processing the first months of the pandemic COVID-19: a psycholinguistic analysis of Italian university students' diaries

A longitudinal psycholinguistic study was conducted with 107 students from different Italian universities that produced daily photo-diary entries for two weeks, one at the beginning and the other at the end of the first Italian lockdown period, imposed in view of the rapid dissemination of COVID -19. The task was to take a daily photo accompanied by a short description (text). The texts accompanying the photos were analysed using Linguistic Inquiry and Word Count (LIWC) software to analyze linguistic markers representing psychological processes related to the experience of the pandemic and the lockdown, identifying potential changes in psycholinguistic variables useful for understanding the psychological impact of such harsh and extended restricted living conditions on Italian students. LIWC categories related to negation, anger, cognitive mechanisms, tentative discourse, past, and future increased statistically significantly between the two time points, while word count, prepositions, communication, leisure, and home decreased statistically significantly. While male participants used more articles at both time points, females used more words related to anxiety, social processes, past, and present at T1 and more related to insight at T2. Participants who lived with their partner showed higher scores on negative emotions, affect, positive feelings, anger, optimism, and certainty. Participants from southern Italy tended to describe their experiences from a collective and social perspective rather than an individual perspective. By identifying, discussing, and comparing these phenomena with the broader literature, a spotlight is shed for the first time on the psycholinguistic analysis of students at the national level who faced the first COVID -19 lockdown in Italy.

Word usage in spontaneous speech as a predictor of depressive symptoms among youth at high risk for mood disorders

BACKGROUND

We examined whether digital phenotyping of spontaneous speech, such as the use of specific word categories during speech samples, was associated with depressive symptoms in youth who were at familial and clinical risk for mood disorders.

METHODS

Participants (ages 13-19) had active mood symptoms, mood instability, and at least one parent with bipolar or major depressive disorder. During a randomized trial of family-focused therapy, participants were instructed to make weekly calls to a central voice server and leave speech samples in response to automated prompts. We coded youths' speech samples with the Linguistic Inquiry and Word Count system and used machine learning to identify the combination of speech features that were most closely associated with the course of depressive symptoms over 18 weeks.

RESULTS

A total of 253 speech samples were collected from 44 adolescents (mean age = 15.8 years; SD = 1.6) over 18 weeks. Speech containing affective processes, social processes, drives toward risk or reward, nonfluencies, and time orientation words were correlated with depressive symptoms at concurrent time periods (ps < 0.01). Machine learning analyses revealed that affective processes, nonfluencies, drives and risk words combined to most strongly predict changes in depressive symptoms over 18 weeks of treatment.

LIMITATIONS

Study results were limited by the small sample and the exclusion of paralinguistic or contextual variables in analyzing speech samples.

CONCLUSIONS

In youth at high risk for mood disorders, knowledge of speech patterns may inform prognoses during outpatient psychosocial treatment.

Mind the gap: Exploring differences in suicide literacy between cybersuicide and offline suicide

Introduction

The highly public nature of cybersuicide contradicts long-held beliefs of offline suicide, which may cause differences in the way people perceive and respond to both of them. However, knowledge of whether and how suicide literacy differs between cybersuicide and offline suicide is limited.

Methods

By analyzing social media data, this paper focused on livestreamed suicide and aimed to compare suicide literacy between cybersuicide and offline suicide on three aspects, including false knowledge structure, extent of association with stigma, and linguistic expression pattern. 7,236 Sina Weibo posts with relevant keywords were downloaded and analyzed. First, a content analysis was performed by human coders to determine whether each post reflected suicide-related false knowledge and stigma. Second, a text analysis was conducted using the Simplified Chinese version of LIWC software to automatically extract psycholinguistic features from each post. Third, based on selected features, classification models were developed using machine learning techniques to differentiate false knowledge of cybersuicide from that of offline suicide.

Results

Results showed that, first, cybersuicide-related posts generally reflected more false knowledge than offline suicide-related posts ( χ 1 2 = 255.13, p < 0.001). Significant differences were also observed in seven false knowledge types. Second, among posts reflecting false knowledge, cybersuicide-related posts generally carried more stigma than offline suicide-related posts ( χ 1 2 = 116.77, p < 0.001). Significant differences were also observed in three false knowledge types. Third, among established classification models, the highest F1 value reached 0.70.

Discussion

The findings provide evidence of differences in suicide literacy between cybersuicide and offline suicide, and indicate the need for public awareness campaigns that specifically target cybersuicide.

The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey

Importance

Emotional exhaustion (EE) rates in healthcare workers (HCWs) have reached alarming levels and been linked to worse quality of care. Prior research has shown linguistic characteristics of writing samples can predict mental health disorders. Understanding whether linguistic characteristics are associated with EE could help identify and predict EE.

Objectives

To examine whether linguistic characteristics of HCW writing associate with prior, current, and future EE.

Design setting and participants

A large hospital system in the Mid-West had 11,336 HCWs complete annual quality improvement surveys in 2019, and 10,564 HCWs in 2020. Surveys included a measure of EE, an open-ended comment box, and an anonymous identifier enabling HCW responses to be linked across years. Linguistic Inquiry and Word Count (LIWC) software assessed the frequency of one exploratory and eight a priori hypothesized linguistic categories in written comments. Analysis of covariance (ANCOVA) assessed associations between these categories and past, present, and future HCW EE adjusting for the word count of comments. Comments with <20 words were excluded.

Main outcomes and measures

The frequency of the linguistic categories (word count, first person singular, first person plural, present focus, past focus, positive emotion, negative emotion, social, power) in HCW comments were examined across EE quartiles.

Results

For the 2019 and 2020 surveys, respondents wrote 3,529 and 3,246 comments, respectively, of which 2,101 and 1,418 comments (103,474 and 85,335 words) contained ≥20 words. Comments using more negative emotion (p < 0.001), power (i.e., references relevant to status, dominance, and social hierarchies, e.g., own, order, and allow) words (p < 0.0001), and words overall (p < 0.001) were associated with higher current and future EE. Using positive emotion words (p < 0.001) was associated with lower EE in 2019 (but not 2020). Contrary to hypotheses, using more first person singular (p < 0.001) predicted lower current and future EE. Past and present focus, first person plural, and social words did not predict EE. Current EE did not predict future language use.

Conclusion

Five linguistic categories predicted current and subsequent HCW EE. Notably, EE did not predict future language. These linguistic markers suggest a language of EE, offering insights into EE's etiology, consequences, measurement, and intervention. Future use of these findings could include the ability to identify and support individuals and units at high risk of EE based on their linguistic characteristics.

Associations between emotions expressed in internet news and subsequent emotional content on twitter

We report on the first investigation of large-scale temporal associations between emotions expressed in online news media and those expressed on social media (Twitter). This issue has received little attention in previous research, although the study of emotions expressed on social media has bloomed owing to its importance in the study of mental health at the population level. Relying on automatically emotion-coded data from almost 1 million online news articles on disease and the coronavirus and more than 6 million tweets, we examined such associations. We found that prior changes in generic emotional categories (positive and negative emotions) in the news on the topic of disease were associated with lagged changes in these categories in tweets. Discrete negative emotions did not robustly feature this pattern. Emotional categories coded in online news stories on the coronavirus generally featured weaker and more disparate lagged associations with emotional categories coded in subsequent tweets.

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.

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.

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.

How to Develop Reliable Instruments to Measure the Cultural Evolution of Preferences and Feelings in History?

While we cannot directly measure the psychological preferences of individuals, and the moral, emotional, and cognitive tendencies of people from the past, we can use cultural artifacts as a window to the zeitgeist of societies in particular historical periods. At present, an increasing number of digitized texts spanning several centuries is available for a computerized analysis. In addition, developments form historical economics have enabled increasingly precise estimations of sociodemographic realities from the past. Crossing these datasets offer a powerful tool to test how the environment changes psychology and vice versa. However, designing the appropriate proxies of relevant psychological constructs is not trivial. The gold standard to measure psychological constructs in modern texts - Linguistic Inquiry and Word Count (LIWC) - has been validated by psychometric experimentation with modern participants. However, as a tool to investigate the psychology of the past, the LIWC is limited in two main aspects: (1) it does not cover the entire range of relevant psychological dimensions and (2) the meaning, spelling, and pragmatic use of certain words depend on the historical period from which the fiction work is sampled. These LIWC limitations make the design of custom tools inevitable. However, without psychometric validation, there is uncertainty regarding what exactly is being measured. To overcome these pitfalls, we suggest several internal and external validation procedures, to be conducted prior to diachronic analyses. First, the semantic adequacy of search terms in bags-of-words approaches should be verified by training semantic vector spaces with the historical text corpus using tools like word2vec. Second, we propose factor analyses to evaluate the internal consistency between distinct bag-of-words proxying the same underlying psychological construct. Third, these proxies can be externally validated using prior knowledge on the differences between genres or other literary dimensions. Finally, while LIWC is limited in the analysis of historical documents, it can be used as a sanity check for external validation of custom measures. This procedure allows a robust estimation of psychological constructs and how they change throughout history. Together with historical economics, it also increases our power in testing the relationship between environmental change and the expression of psychological traits from the past.

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.

Modeling Linguistic (A)Synchrony: A Case Study of Therapist-Client Interaction

Interpersonal synchrony is the alignment of responses between social interactants, and is linked to positive outcomes including cooperative behavior, affiliation, and compassion in different social contexts. Language is noted as a key aspect of interpersonal synchrony, but different strands of existing work on linguistic (a)synchrony tends to be methodologically polarized. We introduce a more complementary approach to model linguistic (a)synchrony that is applicable across different interactional contexts, using psychotherapy talk as a case study. We define linguistic synchrony as similarity between linguistic choices that reflect therapists and clients' socio-psychological stances. Our approach involves (i) computing linguistic variables per session, (ii) k-means cluster analysis to derive a global synchrony measure per dyad, and (iii) qualitative analysis of sample extracts from each dyad. This is demonstrated on sample dyads from psychoanalysis, cognitive-behavioral, and humanistic therapy. The resulting synchrony measures reflect the general philosophy of these therapy types, while further qualitative analyses reveal how (a)synchrony is contextually co-constructed. Our approach provides a systematic and replicable tool for research and self-reflection in psychotherapy and other types of purposive dialogic interaction, on more representative and limited datasets alike.

The Relationship Between Dreams and Subsequent Morning Mood Using Self-Reports and Text Analysis

While material from waking life is often represented in dreams, it is less clear whether and how dreams impact waking life. Here, we assessed whether dream mood and content from home diaries predict subsequent waking mood using both subjective self-reports and an objective automated word detection approach. Subjective ratings of dream and morning mood were highly correlated within participants for both negative and positive valence, suggesting that dream mood persists into waking. Text analyses revealed similar relationships between affect words in dreams and morning mood. Moreover, dreams referencing death or the body were related to worse morning mood, as was first-person singular pronoun usage (e.g., "I"). Dreams referencing leisure or ingestion, or including first-person plural pronouns (e.g., "we"), were related to better morning mood. Together, these results suggest that subjective experiences during sleep, while often overlooked, may be an important contributor to waking mood.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-021-00080-8.

Tracking group identity through natural language within groups

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

Us versus Them: The Debates on the Legislation of Same-Sex Marriage (1994 - 2015) in Taiwan

Taiwan, the first country in Asia to provide same-sex couples a legal right to marriage, has witnessed furious debate on both sides. To understand this debate, we combined content analysis and the Linguistic Inquiry and Word Count (LIWC) method to capture themes and states of mind reflected in the news articles. Arguments based in human rights and reflecting heterosexual preferences which were observed elsewhere also dominated the discourses in Taiwan. First-person pronouns, positive and negative emotions, and differentiation were found to be associated with rights discourses and, in turn, support for same-sex marriage. The themes, the state of mind of the involved parties, and same-sex marriage legalization are further discussed.

Development of the Japanese Version of the Linguistic Inquiry and Word Count Dictionary 2015

The Linguistic Inquiry and Word Count Dictionary 2015 (LIWC2015) is a standard text analysis dictionary that quantifies the linguistic and psychometric properties of English words. A Japanese version of the LIWC2015 dictionary (J-LIWC2015) has been expected in the fields of natural language processing and cross-cultural research. This study aims to create the J-LIWC2015 through systematic investigations of the original dictionary and Japanese corpora. The entire LIWC2015 dictionary was initially subjected to human and machine translation into Japanese. After verifying the frequency of use of the words in large corpora, frequent words and phrases that are unique to Japanese were added to the dictionary, followed by recategorization by psychologists. The updated dictionary indicated good internal consistency, semantic equivalence with the original LIWC2015 dictionary, and good construct validity in each category. The evidence suggests that the J-LIWC2015 dictionary is a powerful research tool in computational social science to scrutinize the psychological processes behind Japanese texts and promote standardized cross-cultural investigations in combination with LIWC dictionaries in different languages.

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.

In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study

BACKGROUND

Emotions and mood are important for overall well-being. Therefore, the search for continuous, effortless emotion prediction methods is an important field of study. Mobile sensing provides a promising tool and can capture one of the most telling signs of emotion: language.

OBJECTIVE

The aim of this study is to examine the separate and combined predictive value of mobile-sensed language data sources for detecting both momentary emotional experience as well as global individual differences in emotional traits and depression.

METHODS

In a 2-week experience sampling method study, we collected self-reported emotion ratings and voice recordings 10 times a day, continuous keyboard activity, and trait depression severity. We correlated state and trait emotions and depression and language, distinguishing between speech content (spoken words), speech form (voice acoustics), writing content (written words), and writing form (typing dynamics). We also investigated how well these features predicted state and trait emotions using cross-validation to select features and a hold-out set for validation.

RESULTS

Overall, the reported emotions and mobile-sensed language demonstrated weak correlations. The most significant correlations were found between speech content and state emotions and between speech form and state emotions, ranging up to 0.25. Speech content provided the best predictions for state emotions. None of the trait emotion-language correlations remained significant after correction. Among the emotions studied, valence and happiness displayed the most significant correlations and the highest predictive performance.

CONCLUSIONS

Although using mobile-sensed language as an emotion marker shows some promise, correlations and predictive R² values are low.

Analysis of recreational psychedelic substance use experiences classified by substance

RATIONALE AND OBJECTIVES

Differences among psychedelic substances regarding their subjective experiences are clinically and scientifically interesting. Quantitative linguistic analysis is a powerful tool to examine such differences. This study compared five psychedelic substance report groups and a non-psychedelic report group on quantitative linguistic markers of psychological states and processes derived from recreational use-based online experience reports.

METHODS

Using 2947 publicly available online reports, we compared Ayahuasca and N,N-dimethyltryptamine (DMT, analyzed together), ketamine, lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (MDMA), psilocybin (mushroom), and antidepressant drug use experiences. We examined word frequencies related to various psychological states and processes and semantic proximity to psychedelic and mystical experience scales.

RESULTS

Linguistic markers of psychological function indicated distinct effect profiles. For example, MDMA experience reports featured an emotionally intensifying profile accompanied by many cognitive process words and dynamic-personal language. In contrast, Ayahuasca and DMT experience reports involved relatively little emotional language, few cognitive process words, increased analytical thinking-associated language, and the most semantic similarity with psychedelic and mystical experience descriptions. LSD, psilocybin mushroom, and ketamine reports showed only small differences on the emotion-, analytical thinking-, psychedelic, and mystical experience-related language outcomes. Antidepressant reports featured more negative emotional and cognitive process-related words, fewer positive emotional and analytical thinking-related words, and were generally not similar to mystical and psychedelic language.

CONCLUSION

This article addresses an existing research gap regarding the comparison of different psychedelic drugs on linguistic profiles of psychological states, processes, and experiences. The large sample of experience reports involving multiple psychedelic drugs provides valuable information that would otherwise be difficult to obtain. The results could inform experimental research into psychedelic drug effects in healthy populations and clinical trials for psychedelic treatments of psychiatric problems.

A Personal Model of Trumpery: Linguistic Deception Detection in a Real-World High-Stakes Setting

Language use differs between truthful and deceptive statements, but not all differences are consistent across people and contexts, complicating the identification of deceit in individuals. By relying on fact-checked tweets, we showed in three studies (Study 1: 469 tweets; Study 2: 484 tweets; Study 3: 24 models) how well personalized linguistic deception detection performs by developing the first deception model tailored to an individual: the 45th U.S. president. First, we found substantial linguistic differences between factually correct and factually incorrect tweets. We developed a quantitative model and achieved 73% overall accuracy. Second, we tested out-of-sample prediction and achieved 74% overall accuracy. Third, we compared our personalized model with linguistic models previously reported in the literature. Our model outperformed existing models by 5 percentage points, demonstrating the added value of personalized linguistic analysis in real-world settings. Our results indicate that factually incorrect tweets by the U.S. president are not random mistakes of the sender.

Motivational Tendency Differences Between the Pre-qin Confucianism and Legalism by Psycholinguistic Analysis

Among the hundred schools of thought that flourished during the pre-Qin era, Confucianism and Legalism are the most important ones as their thoughts cast a longstanding influence on the Chinese culture—cultural-psychological formation of the Chinese people. Most of the previous researches focused on analyzing the similarities and differences of the thoughts of Confucianism and Legalism, and few of them analyzed their motivational tendencies. This paper conducted a word frequency analysis of pre-Qin Confucian and Legalist classics with CC-LIWC, an independently developed program for classical text analysis, and made comparative research into the motivational tendencies of the two schools of thought in terms of psycholinguistic differentials. According to our research results, the use of words representing power (M = 0.1377, SD = 0.0104, p = 0.014) and reward (M = 0.0151, SD = 0.0042, p = 0.037) is more frequent in Legalist classics than in Confucian classics, whereas the use of words representing affiliation (p = 0.066), risk (p = 0.086), and achieve (p = 0.27) shows no significant difference between Confucian and Legalist classics. This paper believes that both Confucianism and Legalism are mainly motivated by power, which is the most distinct feature of their motivational tendencies, and that Legalism is more motivated by power and reward than Confucianism; both Confucianism and Legalism are outcomes of the monarchy society with the former showing the reserved side of monarchy and the latter showing the uninhibited side of monarchy; an effective political methodology is absent in Confucianism, while utilitarianism constitutes the cornerstone of the political philosophy of Legalism.

Using machine learning analysis to interpret the relationship between music emotion and lyric features

Melody and lyrics, reflecting two unique human cognitive abilities, are usually combined in music to convey emotions. Although psychologists and computer scientists have made considerable progress in revealing the association between musical structure and the perceived emotions of music, the features of lyrics are relatively less discussed. Using linguistic inquiry and word count (LIWC) technology to extract lyric features in 2,372 Chinese songs, this study investigated the effects of LIWC-based lyric features on the perceived arousal and valence of music. First, correlation analysis shows that, for example, the perceived arousal of music was positively correlated with the total number of lyric words and the mean number of words per sentence and was negatively correlated with the proportion of words related to the past and insight. The perceived valence of music was negatively correlated with the proportion of negative emotion words. Second, we used audio and lyric features as inputs to construct music emotion recognition (MER) models. The performance of random forest regressions reveals that, for the recognition models of perceived valence, adding lyric features can significantly improve the prediction effect of the model using audio features only; for the recognition models of perceived arousal, lyric features are almost useless. Finally, by calculating the feature importance to interpret the MER models, we observed that the audio features played a decisive role in the recognition models of both perceived arousal and perceived valence. Unlike the uselessness of the lyric features in the arousal recognition model, several lyric features, such as the usage frequency of words related to sadness, positive emotions, and tentativeness, played important roles in the valence recognition model.

Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media-A Pragmalinguistic Approach

In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.

Can MOOC Instructor Be Portrayed by Semantic Features? Using Discourse and Clustering Analysis to Identify Lecture-Style of Instructors in MOOCs

Nowadays, most courses in massive open online course (MOOC) platforms are xMOOCs, which are based on the traditional instruction-driven principle. Course lecture is still the key component of the course. Thus, analyzing lectures of the instructors of xMOOCs would be helpful to evaluate the course quality and provide feedback to instructors and researchers. The current study aimed to portray the lecture styles of instructors in MOOCs from the perspective of natural language processing. Specifically, 129 course transcripts were downloaded from two major MOOC platforms. Two semantic analysis tools (linguistic inquiry and word count and Coh-Metrix) were used to extract semantic features including self-reference, tone, effect, cognitive words, cohesion, complex words, and sentence length. On the basis of the comments of students, course video review, and the results of cluster analysis, we found four different lecture styles: “perfect,” “communicative,” “balanced,” and “serious.” Significant differences were found between the different lecture styles within different disciplines for notes taking, discussion posts, and overall course satisfaction. Future studies could use fine-grained log data to verify the results of our study and explore how to use the results of natural language processing to improve the lecture of instructors in both MOOCs and traditional classes.

An Equivalence Study of Four Languages

Today, there is a range of computer-aided techniques to convert text into data. However, they convey not only strengths but also vulnerabilities compared to traditional content analysis. One of the challenges that have gained increasing attention is performing automatic language analysis to make sound inferences in a multilingual assessment setting. The current study is the first to test the equivalence of multiple versions of one of the most appealing and widely used lexicon-based tools worldwide, Linguistic Inquiry and Word Count 2015 (LIWC2015). For this purpose, we employed supervised learning in a classification problem and computed Pearson's correlations and intraclass correlation coefficients on a large corpus of parallel texts in English, Dutch, Brazilian Portuguese, and Romanian. Our findings suggested that LIWC2015 is a valuable tool for multilingual analysis, but within-language standardization is needed when the aim is to analyze texts sourced from different languages.

Changes in Language Style and Topics in an Online Eating Disorder Community at the Beginning of the COVID-19 Pandemic: Observational Study

BACKGROUND

COVID-19 has affected individuals with lived experience of eating disorders (EDs), with many reporting higher psychological distress, higher prevalence of ED symptoms, and compensatory behaviors. The COVID-19 pandemic and the health and safety measures taken to contain its spread also disrupted routines and reduced access to familiar coping mechanisms, social support networks, and health care services. Social media and the ED communities on social media platforms have been an important source of support for individuals with EDs in the past. So far, it is unknown how discussions in online ED communities changed as offline support networks were disrupted and people spent more time at home in the first months of the COVID-19 pandemic.

OBJECTIVE

The aim of this study is to identify changes in language content and style in an online ED community during the initial onset of the COVID-19 pandemic.

METHODS

We extracted posts and their comments from the ED community on the social media website Reddit and concatenated them to comment threads. To analyze these threads, we applied top-down and bottom-up language analysis methods based on topic modeling with latent Dirichlet allocation and 13 indicators from the Linguistic Inquiry and Word Count program, respectively. Threads were split into prepandemic (before March 11, 2020) and midpandemic (after March 11, 2020) groups. Standardized mean differences were calculated to estimate change between pre- and midpandemic threads.

RESULTS

A total of 17,715 threads (n=8772, 49.5% prepandemic threads; n=8943, 50.5% midpandemic threads) were extracted from the ED community and analyzed. The final topic model contained 21 topics. CIs excluding zero were found for standardized mean differences of 15 topics and 9 Linguistic Inquiry and Word Count categories covering themes such as ED symptoms, mental health, treatment for EDs, cognitive processing, social life, and emotions.

CONCLUSIONS

Although we observed a reduction in discussions about ED symptoms, an increase in mental health and treatment-related topics was observed at the same time. This points to a change in the focus of the ED community from promoting potentially harmful weight loss methods to bringing attention to mental health and treatments for EDs. These results together with heightened cognitive processing, increased social references, and reduced inhibition of negative emotions detected in discussions indicate a shift in the ED community toward a pro-recovery orientation.

Linguistic and Personological Features of the Doka and Martin Grieving Style Continuum

While there is extensive research on the adaptive grief styles developed by Doka and Martin, this study is the first of its kind to explore the language used among each style of grief. This study used clinical vignettes from a variety of sources on instrumental and intuitive grieving in an attempt to decipher the language use across various linguistic and psychological processes. Following this analysis, latent Dirichlet allocation (LDA) was used fitting a two-topic model to analyze differences between topics while additionally performing a supervised LDA analysis. The strongest data from this study relate to intuitive grief, which found a higher use of present-tense language in comparison to the instrumental grief style. In addition, results found that the language used by intuitive grievers is slightly more distinguishable than that of its instrumental counterpart. Several implications for counseling and research were developed in response to these findings.

Moral Expressions in 280 Characters or Less: An Analysis of Politician Tweets Following the 2016 Brexit Referendum Vote

Ideas about morality are deeply entrenched into political opinions. This article examines the online communication of British parliamentarians from May 2017-December 2019, following the 2016 referendum that resulted in Britain's exit (Brexit) from the European Union. It aims to uncover how British parliamentarians use moral foundations to discuss the Brexit withdrawal agreement on Twitter, using Moral Foundations Theory as a classification basis for their tweets. It is found that the majority of Brexit related tweets contain elements of moral reasoning, especially relating to the foundations of Authority and Loyalty. There are common underlying foundations between parties, but parties express opposing viewpoints within a single foundation. The study provides useful insights into Twitter's use as an arena for moral argumentation, as well as uncovers the politician's uses of moral arguments during Brexit agreement negotiations on Twitter. It contributes to the limited body of work focusing on the moral arguments made by politicians through Twitter.

Sexual Dimorphism in Language, and the Gender Shift Hypothesis of Homosexuality

Psychological sex differences have been studied scientifically for more than a century, yet linguists still debate about the existence, magnitude, and causes of such differences in language use. Advances in psychology and cognitive neuroscience have shown the importance of sex and sexual orientation for various psychobehavioural traits, but the extent to which such differences manifest in language use is largely unexplored. Using computerised text analysis (Linguistic Inquiry and Word Count: LIWC 2015), this study found substantial psycholinguistic sexual dimorphism in a large corpus of English-language novels (n = 304) by heterosexual authors. The psycholinguistic sex differences largely aligned with known psychological sex differences, such as empathising–systemising, people–things orientation, and men’s more pronounced spatial cognitive styles and abilities. Furthermore, consistent with predictions from cognitive neuroscience, novels (n = 158) by lesbian authors showed minor signs of psycholinguistic masculinisation, while novels (n = 167) by homosexual men had a female-typical psycholinguistic pattern, supporting the gender shift hypothesis of homosexuality. The findings on this large corpus of 66.9 million words indicate how psychological group differences based on sex and sexual orientation manifest in language use in two centuries of literary art.

Effect of the lockdown on diurnal patterns of emotion expression in Twitter

Diurnal variation in psychometric indicators of emotion found in Twitter content has been known for many years. The degree to which this pattern depends upon different environmental zeitgebers has been difficult to determine. The nationwide lockdown in the United Kingdom in spring 2020 provided a unique government-mandated experiment to observe the temporal variation of psychometric indicators in the absence of certain specific social rhythms related to commuting and workplace social activities as well as many normal home-based social activities. We therefore analyzed the aggregated Twitter content of 54 UK cities in the 9 weeks of complete lockdown, comparing them with the 10 weeks that preceded them (as well as with the corresponding weeks of 2019). We observed that the key indicators of emotion retained their diurnal behavior. This suggests that even during lockdown there are still sufficient zeitgebers to maintain this diurnal variation in indicators of emotion.

Poor Sleep Quality and Its Relationship with Individual Characteristics, Personal Experiences and Mental Health during the COVID-19 Pandemic

While the COVID-19 has dramatically altered our lifestyle and sleep practices, the links between sleep, individual characteristics, personal experiences and mental health during the pandemic require further examination. This cross-sectional, multi-methods study examined differences in language used to describe personal experiences, and mental health, based on sleep quality during the early stages of the pandemic. N = 1745 participants (mean age 42.97 ± 14.46 years) from 63 countries responded to the survey. Sleep quality was assessed using the Pittsburgh Sleep Quality Index and mental health was examined using the Patient Health Questionnaire-9, the State Trait Anxiety Inventory, the Perceived Stress Scale and the UCLA-Loneliness Scale. Quantitative analysis of qualitative, language content of personal experiences was conducted using free-text responses and comments to a question on the survey. Almost 50% of the participants reported poor sleep quality, which was linked to a more negative emotional tone and greater mentions of money or finance related words. Good sleepers reported more positive emotional tone in their experiences. Greater reports of clinical state anxiety, moderate depression and moderate stress were observed in poor sleepers, even after accounting for demographics and pandemic-related factors such as loneliness, financial concerns and risk of contracting COVID-19 disease. Results from this study highlight an urgent need for sleep-related public health interventions. Practitioner education, sleep screening for those with mental health conditions, and encouraging people to adopt digital tools may help to reduce the burden of poor sleep on mental health. While the pandemic itself is a stressful and uncertain time, improving sleep can support positive emotion regulation, improving mood and consequential action.