Are numerical scores important for grant assessment? A cross-sectional study.

This study employed Linguistic Inquiry and Word Count (LIWC-22) software, a language analysis tool, to examine Spanish-language pro-NSSI Tumblr posts. Pro-NSSI, or “pro non suicidal self-injury”, refers to online content that normalizes or supports self-harming behaviors. Given the strong associations between NSSI and conditions such as post-traumatic stress disorder (PTSD), anxiety, and depression, understanding how these behaviors are discussed online can help improve interventions. A year’s worth of public posts were collected, focusing on captions and hashtags that included NSSI-related terms. Using Linguistic Inquiry and Word Count (LIWC) software, we analyzed linguistic and psychological markers. Log-likelihood ratio tests revealed significantly higher frequencies of words related to negative emotions, sadness, health, and death compared to standard blog norms. Mixed-language posts showed notable code-switching, suggesting a possible emotional distancing mechanism when discussing self-harm. The findings indicate that Spanish-speaking adolescents engaging in pro-NSSI communities exhibit unique linguistic and psychological characteristics, with important implications for clinical assessment and intervention. Mental health counselors and educators can use these insights to develop culturally and linguistically responsive strategies for prevention and support.

Expression of emotion has been linked to numerous critical and beneficial aspects of human functioning. Accurately capturing emotional expression in text grows in relevance as people continue to spend more time in an online environment. The Linguistic Inquiry and Word Count (LIWC) is a commonly used program for the identification of many constructs, including emotional expression. In an earlier study (Bantum & Owen, 2009) LIWC was demonstrated to have good sensitivity yet poor positive predictive value. The goal of the current study was to create an automated machine learning technique to mimic manual coding. The sample included online support groups, cancer discussion boards, and transcripts from an expressive writing study, which resulted in 39,367 sentence-level coding decisions. In examining the entire sample the machine learning approach outperformed LIWC, in all categories outside of Sensitivity for negative emotion (LIWC Sensitivity = .85; Machine Learning Sensitivity = .41), although LIWC does not take into consideration prosocial emotion, such as affection, interest, and validation. LIWC performed significantly better than the machine learning approach when removing the prosocial emotions (p = <.0001). The sample over-represented examples of emotion that fit into the overarching category of positive emotion. Remaining work is needed to create more effective machine learning features for codes that are thought to be important emotionally but were not well represented in the sample (e.g., frustration, contempt, and belligerence), and Machine Learning could be a fruitful method for continued exploration.

The text mining literature shows a growing body of work concerned with the automatic identification of sentiment in text. Sentiment polarity classification is one of the most important text mining tasks. The typical approach to polarity classification uses lexicons to count word usage from linguistic or emotional aspects. One of the most widely used lexicons is the Linguistic Inquiry and Word Count (LIWC). LIWC assigns words to categories (e.g., positive emotion) based on a lexicon of words associated with psycholinguist categories. It has been widely used in polarity classification task with good results. However, it only accounts for word count, discarding the text structure and ignoring important semantic relationships between words. In this work, we present LIWBC, an algorithm to count bigrams using the lexicon provided by LIWC. The goal is to incorporate text structure information to improve the polarity classification task with LIWC lexicon. We conducted experiments to evaluate LIWBC with two real datasets: the first one consists of blogger posts; the second one is the movie reviews dataset, which contains full-text movie reviews from IMDB. Both datasets were processed with LIWC and LIWBC. After that, we ran four classification algorithms in the data processed by LIWC and LIWBC. The SVM algorithm executed with LIWBC data yielded the best result in both datasets. The F1 score of SVM in blogger posts and movie reviews dataset had an improvement of 2.2% and 2.5%, respectively.

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

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. 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). 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. (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. 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. 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.

在臺灣現行制度下，性侵害加害者一旦離開監獄就必須進入社區治療，然而隨著性罪犯追蹤監控的期程拉長，其再犯率也隨之上升，也就顯示日常生活流轉匯聚各式各樣的境遇，進而影響性再犯的危險因子，然在性罪犯社區處遇歷程中如何評估性犯罪者的再犯企圖一直是臨床實務上值得關注的問題。本個案報告嘗試分析隨時間軌跡的滾動，如何由性犯罪者之日常語言使用找出反映個人內在心理狀態之線索，藉此觀察性侵害加害者之語言徵兆變化情形與再犯之關聯。本個案報告藉由分析一名資料庫匯出之性侵害加害人於社區治療期間所書寫之雙週誌，連續紀錄將近一年直至其再犯性犯罪，共21篇記載其社區治療參與個人生活狀況之自由書寫，將相關內容透過「語文探索與字詞計算」（LIWC）軟體進行五項具有心理意義之文字變項的電腦化文本分析。研究結果發現語言風格透過長時間紀錄後可以觀察到該性犯罪者之再犯可能徵兆，特別是在總字詞數由多到少之變化、正負向情緒詞彙相反的變動態勢，以及誠實性指標呈現V型變化上，符合Ekman、Vrij、DePaulo等三研究團隊有關欺瞞理論之推衍，也因此本研究認為具有心理意義之文字變項可反映潛藏犯罪意圖的細微變化，而與後續再犯結果有關，亦可作為性犯罪者心理評估之參考指標，值得未來透過性犯罪者之大樣本數據分析加以檢證。According to the current legal system in Taiwan, sex offenders must enter a community intervention program after being released from prison. However, recent research of sex offenders has suggested that over the longer term after offenders are released, they have a high rate of sexual re-offense. This shows that the daily struggles while returning to society might contribute to sexual re-offense in different ways, so as to impact the risk factor of sexual re-offense. Thus, assessing the re-offense attempts of sex offenders in the community intervention program is worthy of attention in clinical practice. However, considering the importance of monitoring linguistic behaviors, there is very little existing research that has empirically documented the language use of sex offenders and how that may impact recidivism of sexual offense. Therefore, the aim of this case report attempts to explore how sex offenders' state of minds are reflected in daily language use and how linguistic markers and sexual re-offense may be related. Pennebaker and his colleagues have provided excellent reviews of the psychological aspect in language use. They believe that either expressive writing or spoken language demonstrate cognitive reflective models. Instead of focusing on ＂what＂ is said by the individuals, paying attention to ＂how＂ the individuals say it is much more crucial. Furthermore, it opened a window for researchers to analyze the psychological vulnerabilities or risks of sex offenders as well as having further understanding of the underlining altered state of mind that is intertwined with the surroundings. Considering these concerns, this case study involved a biweekly log written by an ex-sex offender in a community intervention program. The time frame for the study lasted nearly one year until the re-offense occurred. A total of twenty-one free-writing diaries were analyzed with a computerized text analysis software, Linguistic Inquiry and Word Count (LIWC). Beyond examining several categories of words with psychological meanings, such as negative and positive affect, this case study also applies higher-order algorithms based on LIWC dictionaries, including the index of complex thinking, the index of honesty, and the index of social-emotional style. Considering months as the analysis unit, the one-year period is divided into the initial stage (April to July), incubation stage (August to December) and action stage (January to March). The results suggested that the language style could possibly indicate signs of recidivism for sex offenders over the long-term while recording their thoughts. The language used during the action stage demonstrated four valuable language shifts compared to other stages. First, the total number of words continually decreased over time. Second, positive affect words decreased and negative affect words increased. Third, the index of complex thinking decreased. Fourth, the index of honesty rose with a V-shape. This result is aligned with the inference of the deception theory of Ekman, Vrij, and DePaulo. To conclude, the study may be of importance in explaining language with psychological meaning that could reflect subtle changes in hidden intention of crimes. In addition, the findings of the study also have implications for observing signs of deception and the subsequent re-offense from the written text. Thus, language analysis is proving to be a useful source for conducting psychological evaluation and risk management for sex offenders who under treatment. That said, the generalization of the results to other populations with different backgrounds may be limited, so more research is called for to expand the understanding of the language style of sex offenders.

The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.

High-quality social contacts are important for older adult’s emotional well-being whereas narcissism, characterized by self-centeredness, may undermine the quality of personal relationships. Narcissism may be associated with poorer quality relationships because narcissistic adults are more self-centered in their daily social interactions. The current study examined the associations between linguistic features of conversation throughout the day and narcissism using a machine learning approach. Older adults aged 65–89 (N = 281, Mage = 74.07) wore an unobtrusive electronically activated recorder (EAR) throughout the day for 4 to 6 days. The EAR was activated 30 seconds every 7 minutes to capture conversations occurring in a natural setting. The sound files that contained participant’s speech (N = 28,243) were transcribed verbatim. We used Linguistic Inquiry and Word Count (LIWC) to extract linguistic features from the transcriptions (nfeature = 81; e.g., function words, affective processes). Linguistic features were analyzed using random forest algorithm which is a supervised machine learning method that can evaluate features’ performances in predicting older adult’s narcissism. The model reached an accuracy of 62% and results showed that total word count, more auxiliary verbs (e.g., will), more swear words (e.g., damn), and less assent (e.g., agree) were the most powerful predictors of narcissism. Drawing on sound files collected from real-life interactions, findings indicate higher usage of aggressive and disagreeable words among more narcissistic individuals, which probably leads to poorer quality relationships with social partners. Interventions aiming to improve contact quality and emotional well-being may consider individuals’ word use in daily interactions.

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.

Differences in Ethical Decision making between experts and novices: A Comparative StudyEthics is a pivotal feature in developing professionalism in engineers. The production of moral behavioris intricate and demands at least four different psychological processes: moral sensitivity, moraljudgment, moral motivation and moral character and implementation. Moral motivation plays a criticalrole in predicting an individual’s ethical behavior but it is not easy to assess. According to literaturemany variables such as gender, age, work experience, personality, nationality and cultural backgroundplay a role in ethical decisions.Work experience plays a critical role in professional engineers and graduate students by exposing themto more life challenging situations than the undergraduate students (novices). The goal of this study is tocompare the differences in ethical decisions by measuring the individual differences and responses toethical case studies of three different groups: professional engineers, graduate students, andundergraduate students. Individual differences in moral motivation are important for understandinghow people deal with ethical dilemmas. This study was conducted at a large university in the UnitedStates in the Fall 2013 and Fall 2014 semesters. Our sample consists of 60 people of three differentgroups: 20 professional engineers, 20 graduate students, and 20 undergraduate students.To compare the differences in decision making, a structured questionnaire was administered to eachparticipant. The questionnaire consisted of some demographic questions followed by three personalitymeasure tests (Person and Thing Orientations, Pauhlus Spheres of control, and Big Five Personality Test)and five ethical cases adopted from Lockhead Martin Ethics Challenge Games. Guided by the literature,we hypothesized that undergraduate students who are novices are ethically challenged in decisionmaking when compared to professional engineers and graduate students. Our study is at the stage ofanalyzing the data. The findings and applications for engineering instruction will be shared in the draftpaper.

Recently due to the development of Internet communication problems of author identification, author profiling (i.e. text-based demographics and personality prediction), deception detection in text, etc. have become burning issues. However, the choice of linguistic parameters for addressing these problems is still an open question. We argue that the level of the stability of these parameters in the idiolect of the individual has to be taken into account while choosing them for the particular task. In this study we are investigating the level of stability of linguistic parameters in an idiolect that are extracted from Russian texts using the Linguistic Inquiry and Word Count (LIWC) software. LIWC is the most commonly used text analysis program in studies related to authorship profiling, deception detection, mental health diagnostics, etc. However, up until recently the stability of LIWC variables in texts by the same individual on different topics etc. has not been addressed. For the first time the parameters of three groups have been identified based on their stability in idiolect computed using a coefficient of variation. The study was performed on the material of unedited informal Russian written texts. The principles of choosing the linguistic parameters for a range of text analysis tasks depending on the level of variation of the parameters in the idiolect have also been set forth.

This chapter reports on an effort to use a well-regarded language analysis software program, Linguistic Inquiry and Word Count (LIWC), to investigate the differences in writing samples of students from the Purdue University Rising Scholars program between their matriculation point and an advanced level following guided experiential experiences. The LIWC program identifies words within a sample text and compares the resultant counts to defined dictionaries associated with certain characteristics. This effort utilized 16 of the original LIWC psychological model categories and created 16 new professional skills categories. The writing samples were examined from an aggregated perspective at each level and then additionally in a paired format to determine individual differences across time. Although the original LIWC categories produced more word “hits” than the new professional categories, statistically significant results were inferred for both sets of terms. In general, advanced students provided more clinical and less personal writing samples, as would be expected. Several of the professional skills that could be identified with the program’s selection criteria were present in both the matriculating and advanced-level texts. Based on their writing samples, the Rising Scholars students at the advanced level exhibited improved analytical thinking, an increased use of larger descriptive words, an enhanced perception of how their work would be perceived by others, improved mental flexibility, an increased level of patience, and a greater exhibition of resourcefulness in problem-solving.

Language and psychology have much in common in the sense that each of these two disciplines can be used to study thefeatures of the other one. Words that people use every day carry a lot of explicit linguistic features which signal some implicit psychological traits, personal characteristics, social relations and cognitive processes. Since more attention has been recently paidto such an area of research, this study investigates linguistic and psychological features used as indicators of veracity or deception. The study examines selected speeches of the last five Egyptian and American presidents using a computerized content analysis tool called LIWC (Linguistic Inquiry and Word Count). Drawing on previous researches that examine the psychometrics of the language, this study follows an eclectic approach adopting Newman et al.’s model combined with other cues to deception concluded by other studies. The analysis detects the frequency of using pronouns, negation, exclusive words, details, conjunctions and big words in the speeches in order to reveal instances of deception.

Although many college courses adopt online tools such as Q&A online discussion boards, there is no easy way to measure or evaluate their effect on learning. As a part of supporting instructional assessment of online discussions, we investigate a predictive relation between characteristics of discussion contributions and student performance. Inspired by existing work on dialogue acts, project-based learning, and instructional analysis of student-generated text in generating predictive models, we make use of dialogue roles, linguistic features, and work patterns. In particular, we model the Q&A dialog roles that participants play, emotional features covered by LIWC (Linguistic Inquiry and Word Count), cohesiveness of the dialogue, the coherence captured by Coh-Metrix, and temporal patterns of participation. We use a discussion corpus from eight semesters of a computer science course, covering conversations of 173 student groups (370 students). We first remove various noises in student discussion data and normalize the discussion data. We then apply machine learning techniques and text analysis tools for classifying dialogue features efficiently. The extracted dialogue and participation features are used as predictive variables for project grades. The correlation and regression analyses indicate that the number of answers provided to others, the number of positive emotion expressions, and how early students communicate their problems before the deadline correlate with project grades. This finding confirms the argument that in assessing student online activities, we need to capture how they interact, not just how often they participate.

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.