Linguistic Inquiry And Word Count Research Articles

The Psychological Effects of Digital Companies’ Employees during the Phase of COVID-19 Pandemic Extracted from Online Employee Reviews

The ways people use words online can furnish psychological processes about their beliefs, fears, thinking patterns, and so on. Extracting from online employees’ reviews on the workplace community websites, we can quantify the psychological effects of employees during the phase of the COVID-19 pandemic. We collect the anonymous employees’ reviews of Top 100 digital companies from the Glassdoor website which allows people to evaluate and review the companies they have worked for or are working for. Here, based on the data of numerical evaluations and textual reviews, we firstly use Z-score to investigate the psychological effects of employees in digital companies during the phase of COVID-19 pandemic. Next, we use a text analysis application called Linguistic Inquiry and Word Count (LIWC), which provides an efficient and effective method for studying the various emotional, cognitive, and structural components existing in individuals’ verbal and written speech samples, to mine these reviews to obtain changes in personal pronouns and 10 dimensions of psychological processes. Finally, we use Z-score to count on all aspects of drives and personal concerns in psychological processes.

Nursing in the 21st century: results of a pilot survey on attitudes towards nurses and nursing.

This study piloted an electronic survey with open and closed questions. Descriptive statistics were collated for closed questions using Excel. Open-ended questions were analysed using the text analysis program Linguistic Inquiry and Word Count (LIWC) for tone, emotion and criticality. This pilot study recruited 72 participants from professional and non-professional backgrounds. Respondents displayed diversity in their perceptions of nursing, the role of nurses and the role that nurses perform. Nursing is a complex, multifaceted profession. The view of nursing was generally positive and authentic although not easy to define. Nursing was felt to be an inclusive profession; however, it is extremely diverse in nature. Further research is required to explore these concepts in greater depth.

Exploring Language Markers of Mental Health in Psychiatric Stories

Diagnosing mental disorders is complex due to the genetic, environmental and psychological contributors and the individual risk factors. Language markers for mental disorders can help to diagnose a person. Research thus far on language markers and the associated mental disorders has been done mainly with the Linguistic Inquiry and Word Count (LIWC) program. In order to improve on this research, we employed a range of Natural Language Processing (NLP) techniques using LIWC, spaCy, fastText and RobBERT to analyse Dutch psychiatric interview transcriptions with both rule-based and vector-based approaches. Our primary objective was to predict whether a patient had been diagnosed with a mental disorder, and if so, the specific mental disorder type. Furthermore, the second goal of this research was to find out which words are language markers for which mental disorder. LIWC in combination with the random forest classification algorithm performed best in predicting whether a person had a mental disorder or not (accuracy: 0.952; Cohen’s kappa: 0.889). SpaCy in combination with random forest predicted best which particular mental disorder a patient had been diagnosed with (accuracy: 0.429; Cohen’s kappa: 0.304).

Cues that Work: Designing the Optimal Restaurant Crowdfunding Campaign in the US

ABSTRACT This study explores how restaurant entrepreneurs incorporate various intrinsic and extrinsic cues into the design of reward-based crowdfunding campaigns to maximize their probability of success. This study aims to identify the optimal levels of each of these cues to guide entrepreneurs’ strategic behavior. Based on the cue utilization theory and the concept of optimal arousal, this study collected restaurant project data from Kickstarter. Linguistic Inquiry and Word Count (LIWC) was also used to quantify linguistic cues in the project title. Linear and quadratic terms of cues were included in the logistic regression model. An optimal campaign should have 13 pictures, 2 videos, a detailed but succinct description, and many rewards. The project title should have a neutral and conservative style instead of being extremely positive.

Using text analysis to assess the mental health impacts of COVID-19 on rural healthcare providers

The pandemic increased the stress levels of frontline healthcare providers in every country across the globe, and continues to do so. Health care providers in the rural regions of the U.S. were more vulnerable to the impact of COVID-19 due to the lack of resources and the population they serve. A qualitative study of 25 rural healthcare providers in a western U.S. state was conducted during 2020. In this report, Linguistic Inquiry and Word Count (LIWC) was used to compare the use of emotion and reward words in 12 providers interviewed before the onset of COVID-19 on March 11, 2020, to 13 interviews conducted after COVID-19 onset. Results indicated that providers used fewer positive emotion words after COVID-19 onset compared to before COVID. Negative emotion words also decreased. Words related to the rewarding aspects of their work increased after COVID-19, but differed among the pre- and post-COVID groups. Positive emotion words was more strongly related to rewards in the post-COVID group. Providers reported concerns about access to care for those unable to be treated for the increased depression, anxiety and suicide ideation related to the pandemic. The rural health care system needs policies directed at improving patient access to care and greater funding.

Applied Behavior Analysis as Treatment for Autism Spectrum Disorders: Topic Modeling and Linguistic Analysis of Reddit Posts.

Background: It is critical for professionals to understand the discourse landscape within various online and social media outlets in order to support families of children with autism in treatment decision-making. This need is heightened when considering treatments that have garnered excitement and controversy, such as applied behavioral analysis (ABA) therapy.Method: The specific aims of this study were to identify the main themes in Reddit posts about ABA-based interventions for autism using topic modeling, to examine the linguistic aspects of Reddit conversations using the Linguistic Inquiry and Word Count (LIWC) analysis, and to examine the relationship between linguistic aspects and user category (i.e., pro- vs. anti-ABA vs. undecided, parent vs. professional vs. an individual with autism).Results: The topic modeling resulted in 11 themes that ranged across various elements, such as autism as a condition and its management, stakeholders, and consequences of autism and the support needed. The posts of individuals were focused on personal experiences and opinions as opposed to clinical and research information sharing. Linguistic analysis indicated that the posts reveal an intimate stance rather than an empirical stance.Conclusions: Results provide insight into perspectives of ABA. This type of research may help in developing and distributing appropriate and evidence-based information.

Supplemental Material for The Kernel of Truth in Text-Based Personality Assessment: A Meta-Analysis of the Relations Between the Big Five and the Linguistic Inquiry and Word Count (LIWC)

Supplemental Material for The Kernel of Truth in Text-Based Personality Assessment: A Meta-Analysis of the Relations Between the Big Five and the Linguistic Inquiry and Word Count (LIWC)

The association of language style matching in text messages with mood and anxiety symptoms

Context: Impairment in social functioning is a feature and consequence of depression and anxiety disorders. For example, in depression, anhedonia and negative feelings about the self may impact relationships; in anxiety, fear of negative evaluation may interfere with getting close to others. It is unknown whether social impairment associated with depression and anxiety symptoms is reflected in day-to-day language exchanges with others, such as through reduced language style matching (LSM). Methods: Over 16 weeks, we collected text message data from 458 adults and evaluated differences in LSM between people with average scores above/below the clinical cutoff for depression, generalized anxiety, and social anxiety in text message conversations. Text message sentiment scores were computed across 73 Linguistic Inquiry and Word Count (LIWC) categories for each participant. T-tests were used to compare LSM across two groups (average scores above/below clinical cutoff) for each of the 3 diagnostic categories (depression, generalized anxiety, social anxiety), and each of the 73 LIWC categories, with correction for multiple comparisons. Results: We found reduced LSM of function words (namely, prepositions [t=-2.82, p=.032], articles [t=-5.26, p<.001], and auxiliary verbs [t=-2.64, p=.046]) in people with average scores above the clinical cutoff for generalized anxiety, and reduced LSM of prepositions (t=-4.26, p<.001) and articles (t=-3.39, p=.010) in people with average scores above the clinical cutoff for social anxiety. There were no significant differences in LSM of function words between people with average scores above and below the clinical cutoff for depression. Across all symptom categories, elevated affective psychopathology was associated with being more likely to style match on formality, including netspeak (generalized anxiety, t=5.77, p<.001; social anxiety, t=4.14, p<.001; depression, t=3.13, p=.021) and informal language (generalized anxiety, t=6.65, p<.001; social anxiety, t=5.14, p<.001; depression, t=3.20, p=.020).We also observed content-specific LSM differences across the three groups. Conclusions: Reduced LSM of function words among patients reporting elevated anxiety symptoms suggests that anxiety-related psychosocial difficulties may be perceptible in subtle cues from day-to-day language. Conversely, the absence of differences in the LSM of function words among people with average scores above and below the clinical cutoff for depression indicates a potentially distinct mechanism of social impairment. Implications: Results point to potential markers of psychosocial difficulties in daily conversations, particularly among those experiencing heightened anxiety symptoms. Future studies may consider the degree to which LSM is associated with self-reported psychosocial impairment, with the promise of informing cognitive-behavioral mechanisms and tailoring digital interventions for social skills.

Exploring Bohdan Lepky’s Translation Ethics Using Linguistic Inquiry and Word Count

The present empirical study aims to outline ethical landmarks of Bohdan Lepky, the renowned Ukrainian writer and translator, in his Ukrainian translation of Salomé by Oscar Wilde. We assess the ethics of translation data defined by Kalina (2015) in terms of accuracy, impartiality, and confidentiality. In order to address these ethical issues, the study envisaged the following steps. First, source and target texts were analyzed using the Linguistic Inquiry and Word Count (LIWC) computerized program based on its built-in French 2007 and Ukrainian 2015 dictionaries. Second, all 'style words' (Tausczik &amp; Pennebaker, 2010) represented by functional words, e.g., conjunctions, prepositions, and pronouns that bear procedural meaning, were compared in both texts. Findings showed that the translator followed the ethical "tradition of sameness" (Wyke, 2011), being less "visible" (Venuti, 1995) in his target language version. Despite a greater number of impersonal pronouns causing slight implicitation, we observed no traces of simplification or explicitation deforming tendencies in Lepky's translated text. Similar indices of conjunctions and prepositions, and the average number of words per sentence in both texts, confirmed the accuracy of meaning and style. Although markers of oral speech (fillers) prevailed in translation, this strategy manifests his agency and attempt to be ethically "accountable" for his product in the sense of Schlesinger’s (1989) "equalizing." This shift moves along the oral-literate continuum towards more natural, i.e., rich in pragmatic discourse markers (Schiffrin, 1989) oral communication. The LIWC psychological category of "affect" filled with emotionally charged words was less dense in the Ukrainian version, contributing both to the translator's "ethics of difference" (Venuti, 1999) and his impartiality. Thus, results of the LIWC-processed data demonstrated high ethical standards of translating Bohdan Lepky met in his Ukrainian rendition of Salomé by Oscar Wilde.&#x0D; Acknowledgements&#x0D; This research was made possible through funds from The Remeza Family Research and Publications Grant 2021-2022, Canadian Institute of Ukrainian Studies at the University of Alberta, Canada.&#x0D;

The psychological impact of COVID-19 on people suffering from dysfunctional eating behaviours: a linguistic analysis of the contents shared in an online community during the lockdown.

The coronavirus disease 2019 (COVID-19) spread several months ago from China and it is now a global pandemic. The experience of lockdown has been an undesirable condition for people with mental health problems, including eating disorders. The present study has the aim of understanding the impact of the first wave of the COVID-19 pandemic on people with selfreported disordered eating behaviours. A linguistic analysis was carried out with regard to the online posts and comments published by 1971 individuals (86% women) in a Facebook online community focusing on EDs during the lockdown. A total of 244 posts and 3603 comments were collected during the 56 days of lockdown (from the 10th of March until the 4th of May 2020) in Italy and were analysed by Linguistic Inquiry and Word Count (LIWC) software. The results showed that words related to peer support decreased in posts over time, and that anxiety and anger increased in the published comments. Moreover, greater feelings of negativity and anxiety were found in posts and comments throughout the COVID-19 pandemic, as well as lesser use of words related to positive emotions. Thematic qualitative analysis revealed eight themes that described the main subjective components of ED symptomatology and distress during the first COVID-19 lockdown. The current findings can help in delivering tailored treatments to people with eating disorders during the COVID-19 pandemic.

Internet Rumors During the COVID-19 Pandemic: Dynamics of Topics and Public Psychologies.

The capturing of social opinions, especially rumors, is a crucial issue in digital public health. With the outbreak of the COVID-19 pandemic, the discussions of related topics have increased exponentially in social media, with a large number of rumors on the Internet, which highly impede the harmony and sustainable development of society. As human health has never suffered a threat of this magnitude since the Internet era, past studies have lacked in-depth analysis of rumors regarding such a globally sweeping pandemic. This text-based analysis explores the dynamic features of Internet rumors during the COVID-19 pandemic considering the progress of the pandemic as time-series. Specifically, a Latent Dirichlet Allocation (LDA) model is used to extract rumor topics that spread widely during the pandemic, and the extracted six rumor topics, i.e., “Human Immunity,” “Technology R&D,” “Virus Protection,” “People's Livelihood,” “Virus Spreading,” and “Psychosomatic Health” are found to show a certain degree of concentrated distribution at different stages of the pandemic. Linguistic Inquiry and Word Count (LIWC) is used to statistically test the psychosocial dynamics reflected in the rumor texts, and the results show differences in psychosocial characteristics of rumors at different stages of the pandemic progression. There are also differences in the indicators of psychosocial characteristics between truth and disinformation. Our results reveal which topics of rumors and which psychosocial characteristics are more likely to spread at each stage of progress of the pandemic. The findings contribute to a comprehensive understanding of the changing public opinions and psychological dynamics during the pandemic, and also provide reference for public opinion responses to major public health emergencies that may arise in the future.

High Cognitive Load Situations With Different Conversation Topics Affect Walking Speed and Cognitive Complexity

Walking and talking on the phone are common high-cognitive-load-situations (HCLS; e.g. dual-tasks), requiring extra attentional allocation and increasing perceived stress. We explored whether two load types, 1) single-task (ST) walking or talking on a phone and 2) HCLS walking while talking on a phone, influenced walking and/or cognitive performance among young (n=7; age=23.00±2.08yrs), middle-aged (n=14; age=44.79±7.42yrs), and older (n=15; age=74.47±3.91yrs) adults while controlling for perceived stress. Participants completed 3-minute trials of single-task walking (ST-W), single-task phone conversations with common (e.g., weather; ST-C) and uncommon topics (e.g., life experience; ST-U), and walking while talking on a phone (HCLS-C and HCLS-U). Walking speed was analyzed with 3(ST-W;HCLS-C;HCLS-U) x 3(Age) ANCOVA. HCLS resulted in slower walking speed (p<.001). Older adults exhibited slower speed across conditions compared to young (p=.015). Cognitive complexity (i.e., conversational tone and words greater than six letters (SIXLTR)) on the Linguistic Inquiry and Word Count (LIWC) were analyzed with 2(Cvs.U) x 2(STvs.HCLS) x 3(Age) ANCOVAs. Older age was associated with less cognitive complexity; positive tone (p=.014) and SIXLTR (p=.016), respectively in conversations. Uncommon topics reduced positive tone (p=.022) and SIXLTR (p=.003). Effects of HCLS on tone (p=.040) and SIXLTR (p=.005) varied with age. HCLS with different conversation topics resulted in reduced walking and cognitive complexity while controlling for perceived stress. The analysis of cognitive complexity using common/uncommon conversation topics is a novel method to assess the impact of HCLS. This research will disrupt the transformation of aging leading to a better understanding of attentional allocation and its effects on function.

Language of Criminal Confessions

&#x0D; Confession evidence is powerfully persuasive, and yet many wrongful convictions involving false confessions have surfaced in recent years (Innocence Project, 2021; National Registry of Exonerations, 2021). Although police are trained to corroborate admissions of guilt, research shows that most false confessions contain accurate details and other content cues suggesting credibility as well as extrinsic evidence of guilt. Hence, a method is needed to help distinguish true and false confessions. In this study, we utilized a corpus-based approach to outline the linguistic features of two sets of confessions: those that are presumed true (n = 98) and those that have been proven false (n = 37). After analyzing the two corpora in LIWC (Linguistic Inquiry and Word Count) to identify significant categories, we created a logistic regression model that distinguished the two corpora based on three identified predictors: personal pronouns, impersonal pronouns, and conjunctions. In a first sample comprised of 25 statements per set, the model correctly categorized 37 out of 50 confessions (74%); in a second out-of-model sample, the predictors accurately classified 20 of 24 confessions (83.3%). A high frequency of impersonal pronouns was associated with confessions proven false, while a high frequency of conjunctions and personal pronouns were associated with confessions presumed to be true. Several patterns were observed in the corpora. In the latter set of confessions, for example, “I” was often followed by a lexical verb, a pattern less frequent in false confessions. Although these data are preliminary and not to be used for practical diagnostic purposes, the findings suggest that additional research is warranted.&#x0D;

Viewing Your Kapwa: Elaboration of a Social-Relational Construct through Language

Enriquez (1978) presented Kapwa as a core social psychological construct that could underpin Filipino social perception and interaction. However, issues arose regarding its conceptual and operational clarity as well as lack of empirical support. This two-phased study aims to address the issues presented through a psychometric approach. The first phase aimed to uncover construct characteristics by asking participants to provide definitions and examples of kapwa and di-kapwa, which were then examined via content analysis and the Linguistic Inquiry and Word Count (LIWC) program. Results from this phase elicited four (4) themes indicating that people viewed their social other in terms of dispositional attribution, level of interaction, connection, and inclusion. The second phase utilized this data to develop a kapwa measure which was found to have three (3) dimensions: Relations with the Social Other, Perceived Negative Characteristics, and Difference from the Self. Initial reliability and validity tests were favorable. Implications on Filipino social perception and behaviors highlight that we view our social others in terms of the aforementioned three dimensions and how differentially we might treat them depending on whether they are perceived as kapwa or di-kapwa. Furthermore, the results of the study further elaborate and nuance kapwa, which adds to theorizing in Sikolohiyang Pilipino (SP).

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

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

Is chatting with a sophisticated chatbot as good as chatting online or FTF with a stranger?

Emotionally-responsive chatbots are marketed as agents with which one can form emotional connections. They can also become weak ties in the outer layers of one's acquaintance network and available for social support. In this experiment, which was designed to study the acquaintance process, we randomly assigned 417 participants into three conditions: face-to-face (FTF) chat with a human, online chat with a human, and online chat with a commercially-available, emotionally-responsive chatbot, Replika. After a 20-min getting-acquainted chat, participants reported their affective state and relational evaluations of the chat. Additionally, all chats were recorded and text analyzed using the Linguistic Inquiry and Word Count (LIWC) program. In all conditions, participants reported moderate levels of positive emotions and low levels of negative emotions. Those who chatted FTF with a human reported significantly more negative emotions than those who chatted with a bot. However, those who chatted with a human also reported more homophily with and liking of their chat partner and that their partner was more responsive. Meanwhile, participants had fewest conversational concerns with the chatbot. These findings have implications for future computer-mediated interaction studies: conversations with chatbots appear to have different affordances and effects on chatter enjoyment and conversational concerns in getting-acquainted contexts. These results may help designers improve reception and marketability for chatbots in consumer markets.

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.

Using LIWC to Analyze Participants' Psychological Processing in Accounting JDM Research

SUMMARY This paper provides methodological guidance for judgment and decision-making (JDM) researchers in accounting who are interested in using the Linguistic Inquiry and Word Count (LIWC) text analysis program to analyze research participants' written responses to open-ended questions. We discuss how LIWC's measures of psychological constructs were developed and validated in psycholinguistic research. We then use data from an audit JDM study to illustrate the use of LIWC to guide researchers in identifying suitable measures, performing quality control procedures, and reporting the analysis. We also discuss research design considerations that will strengthen the inferences drawn from LIWC analysis. The paper concludes with examples where LIWC analysis has the potential to reveal participants' deep, complex, effortful psychological processing and affective states from their written responses.

What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?

Geopolitical forecasting tournaments have stimulated the development of methods for improving probability judgments of real-world events. But these innovations have focused on easier-to-quantify variables, like personnel selection, training, teaming, and crowd aggregation—bypassing messier constructs, like qualitative properties of forecasters’ rationales. Here, we adapt methods from natural language processing (NLP) and computational text analysis to identify distinctive reasoning strategies in the rationales of top forecasters, including: (a) cognitive styles, such as dialectical complexity, that gauge tolerance of clashing perspectives and efforts to blend them into coherent conclusions and (b) the use of comparison classes or base rates to inform forecasts. In addition to these core metrics, we explore metrics derived from the Linguistic Inquiry and Word Count (LIWC) program. Applying these tools to multiple tournaments and to forecasters of widely varying skill (from Mechanical Turkers to carefully culled “superforecasters”) revealed that: (a) top forecasters show higher dialectical complexity in their rationales and use more comparison classes; (b) experimental interventions, like training and teaming, that boost accuracy also influence NLP profiles of rationales, nudging them in a “superforecaster” direction.

What Does Patient-Centered Communication Look Like?: Linguistic Markers of Provider Compassionate Care and Shared Decision-Making and Their Impacts on Patient Outcomes

ABSTRACT Patient-centered communication promotes positive patient outcomes. This study examines the linguistic markers of two key dimensions of patient-centered communication (i.e., provider compassionate care and shared decision-making) and their mediating effects on patient perceived quality of and affective responses to the provider’s treatment recommendations. Transcripts (N = 343) of provider talk from provider-patient interactions in medical visits for upper respiratory infection symptoms where patients were not prescribed with antibiotics were analyzed with the Linguistic Inquiry and Word Count (LIWC) dictionary. Results showed that providers’ use of affiliation words positively predicted patients’ perceptions of their providers’ compassionate care. Providers’ use of insight words negatively predicted patients’ perceptions of provider shared decision-making. Meanwhile, providers’ use of first-person singular pronouns, causation and differentiation words, and clout words were positively related to perceived provider shared decision-making. Patient perceived compassionate care and shared decision-making further increased patients’ positive affect toward and perceived quality of non-antibiotic treatment recommendations. These perceptions also reduced their negative affect toward the recommendations. Implications of the findings are discussed with regard to patient-centered communication in relation to the promotion of antibiotic stewardship.