Text Analysis Program Research Articles

Automated Assessment of Leadership Style and Professional Values of a Top-Manager and its Compliance with SDGS

Objective: the main objective was to use automated text analysis and assess the leadership styles and professional values of top managers, as well as to study how these parameters can influence the achievement of the SDG "Decent work and Economic growth". Theoretical Framework: The work is based on the idea that the leadership style and intelligence of a top manager determine the trajectory of a company’s development, the psychological working climate, the attitude towards employees, and the decision-making mechanism within and outside the team. Method: The authors used automated psychological text analysis using AI-based chatbots (ChatGPT, Gemini, Gigachat) and a specialized psychological text analysis program (LIWC). These methods were compared with psychological content analysis performed by a person. Results and Discussion: The obtained results showed that automated text analysis can be effectively used to understand the personality of the manager and the features of decision-making on his part in relation to employees and the organization. Research Implications: the approach proposed by the authors will allow for the development of measures to increase labor productivity and create decent jobs, taking into account the personality and values of the top manager, and will serve as a basis for the development of creativity and innovation in the company, thereby contributing to the achievement of the SDGs of “Decent Work and Economic Growth”. Originality/Value: the conducted research confirmed our hypothesis that text analysis allows us to understand important personality traits from the standpoint of intelligence, logic, thinking and speech, using artificial intelligence tools.

Examining the relationship between parents' self-reported mindfulness and observed language use in attachment-relevant communication.

Parents' language use is an important context for early socialization. We examined the relationship between parents' self-reported mindfulness and observed language use in two forms of attachment-relevant communication. Sixty-three parents of 6-18-month-old infants from Australia (n=32) and New Zealand (n=31) completed the five facets of mindfulness-short form (FFMQ-SF) questionnaire, the adult attachment interview (AAI), and a 10-min play session with their infant. We examined parents' frequency of word usage within the categories of the linguistic inquiry word count (LIWC) text analysis program to explore the relationship between mindfulness and language use. Mindfulness was associated with cognitive, affective, perceptual, and time orientation language use in the AAI. However, fewer associations were identified between mindfulness and language use in the parent-infant play session. Results are discussed in terms of their relevance to mindfulness and attachment.

Trends in Influencer Marketing: A Bibliometric Analysis and Future Directions

Influencer marketing is one of the trending research topics in marketing. In this context, this study aims to bibliometrically evaluate the work published by academics about influencer marketing. Bibliometric analysis was applied to 1584 English articles in the Scopus database containing the subject title of influencer marketing and its synonyms. The data were analyzed under four headings—information of published articles, citation analysis, word cloud and author information—through the R program version 4.2.2. Additionally, the Voyant Tools text analysis program was used in some analyses. The research results will provide a detailed report on English articles in the Scopus database regarding influencer marketing between 2002 and 2022. It is thought that the research results will provide a general guide for future studies on influencer marketing.

Letter of Recommendation Characteristics Associated with Interview Offer to a Vascular Surgery Residency Program

BackgroundLetters of recommendation (LORs) are considered by program directors (PDs) to be an integral part of the residency application. With the conversion of USMLE Step 1 to a binary pass/fail outcome, LORs will likely have higher important in the application process moving forward. However, their utility in securing an interview for a particular applicant remains undetermined. This study aims to identify the applicant and LOR characteristics associated with an interview invitation. MethodsLetter writer (n=977) characteristics were abstracted from applications (n=264) to an individual integrated vascular surgery residency program over 2 application cycles. A validated text analysis program, Linguistic Inquiry and Word Count, was used to characterize LOR content. Applicant, letter writer, and LOR characteristics associated with an interview invitation was determined using multivariable analysis. ResultsLetter writers were 70.9% vascular surgeons (VS), 23.7% PDs, and 45.4% professors. Applicants offered an interview were more likely to come from a top 50 medical school (35.2% vs 25.8%, p=0.013) and an institution with a home vascular program (45.5% vs 34.1%, p=0.006). Alpha Omega Alpha membership was significantly associated with interview offer (28.4%, p<0.001). A greater proportion of letters from VS was associated with an interview offer (p <0.001) compared with letter writers of other specialties. One or more PD letters was significantly associated with an interview offer (79.55% vs 20.45%, p=0.008), whereas number of letters from APDs was not significantly associated with interview offer. Letters written by away institution faculty were significantly associated with interview offer (75%, p<0.001), whereas nonclinical letters were not. Presence of one or more letters from a chair (57.95% vs 42.05%, p=0.015) or chief (67.05% vs 32.95%, p=0.028) was significantly associated with interview offer. Letters for applicants offered an interview had more references to research and teaching, which were more common in letters written by VS. Letters written by PDs were more likely to use assertive, advertising language in favor of applicants. There were no significant applicant, letter writer, or LOR characteristics associated with a top 20 rank. ConclusionSuccessful applicants were more likely to have LORs written by VS, PDs, and those of higher academic rank with references to research and teaching.

What's in a Name? Experimental Evidence of Gender Bias in Recommendation Letters Generated by ChatGPT.

Artificial intelligence chatbots such as ChatGPT (OpenAI) have garnered excitement about their potential for delegating writing tasks ordinarily performed by humans. Many of these tasks (eg, writing recommendation letters) have social and professional ramifications, making the potential social biases in ChatGPT's underlying language model a serious concern. Three preregistered studies used the text analysis program Linguistic Inquiry and Word Count to investigate gender bias in recommendation letters written by ChatGPT in human-use sessions (N=1400 total letters). We conducted analyses using 22 existing Linguistic Inquiry and Word Count dictionaries, as well as 6 newly created dictionaries based on systematic reviews of gender bias in recommendation letters, to compare recommendation letters generated for the 200 most historically popular "male" and "female" names in the United States. Study 1 used 3 different letter-writing prompts intended to accentuate professional accomplishments associated with male stereotypes, female stereotypes, or neither. Study 2 examined whether lengthening each of the 3 prompts while holding the between-prompt word count constant modified the extent of bias. Study 3 examined the variability within letters generated for the same name and prompts. We hypothesized that when prompted with gender-stereotyped professional accomplishments, ChatGPT would evidence gender-based language differences replicating those found in systematic reviews of human-written recommendation letters (eg, more affiliative, social, and communal language for female names; more agentic and skill-based language for male names). Significant differences in language between letters generated for female versus male names were observed across all prompts, including the prompt hypothesized to be neutral, and across nearly all language categories tested. Historically female names received significantly more social referents (5/6, 83% of prompts), communal or doubt-raising language (4/6, 67% of prompts), personal pronouns (4/6, 67% of prompts), and clout language (5/6, 83% of prompts). Contradicting the study hypotheses, some gender differences (eg, achievement language and agentic language) were significant in both the hypothesized and nonhypothesized directions, depending on the prompt. Heteroscedasticity between male and female names was observed in multiple linguistic categories, with greater variance for historically female names than for historically male names. ChatGPT reproduces many gender-based language biases that have been reliably identified in investigations of human-written reference letters, although these differences vary across prompts and language categories. Caution should be taken when using ChatGPT for tasks that have social consequences, such as reference letter writing. The methods developed in this study may be useful for ongoing bias testing among progressive generations of chatbots across a range of real-world scenarios. OSF Registries osf.io/ztv96; https://osf.io/ztv96.

Automated text analysis methods to identify the individual structure of motivation for sports and a healthy lifestyle

In this study, we set two goals: develop and test a method for assessing a person’s motivational profile, based on psychological and content analysis of the text and applicable to motivation for sports and a healthy lifestyle and test automated text analysis programs based on artificial intelligence and compare their performance with each other and with traditional expert content analysis. The selection of programs was made from 65 text analysis programs and 12 artificial intelligence-based chatbots. A total of 12 texts were used for the study, including three long interviews and speeches, and 9 short (2-6 sentences) posts on social media and online media. The evaluated texts contain 5,787 words, of which 91% are originally in Chinese and 9% in English. The content analysis methodology was substantiated in detail in our previous publications.

95 Delving Beyond the Test Score: Linguistic Markers of Cognitive Impairment on Paragraph Recall

Objective:Cognitive tests requiring spoken responses, such as paragraph recall, are rich in cognitive-related information that is not captured using traditional scoring methods. This study aimed to determine if linguistic features embedded in spoken responses may differentiate between individuals who are and are not cognitively impaired.Participants and Methods:Participants in the Long Life Family Study completed a neuropsychological assessment which included the WMS-R Logical Memory I paragraph recall. For a subset of participants (N=709), test responses were digitally recorded and manually transcribed. We used Linguistic Inquiry Word Count, a text analysis program, to quantify word counts, grammatical features (e.g, prepositions, verb tenses), and the use of content words related to specific semantic categories (e.g., work-related, numbers) for immediate (IR) and delayed recall (DR). We used regression models with Generalized Estimating Equations adjusted by age, sex, education, and within-family correlation to select features associated with cognitive status (normal cognition [NC] versus cognitive impairment [CI]; Bonferroni-corrected threshold p&lt;0.001). Next, we developed a “polyfeature score” (PFS) for both immediate and delayed recall, each calculated as a weighted sum of the selected linguistic features. We then built a logistic regression model to evaluate the predictive value of each PFS for identifying cognitively impaired individuals. In secondary analyses, we used regression models as above to identify features associated with mild cognitive impairment subtype (amnestic [aMCI] versus nonamnestic [naMCI]; threshold p&lt; .05).Results:The sample included 599 participants with NC and 110 with CI (mean age = 72.3 ± 11.0 years, 54% female). The regression identified 8 linguistic features for IR and 7 for DR that significantly predicted cognitive status. Decreased use of content words related to work (e.g., employed, school, police) and biological processes (e.g., cook, cafeteria, eat) and the use of negations (e.g., no, not, can’t) were predictive of cognitive impairment in both recall conditions. In contrast, the use of other content word categories were predictive of cognitive status in only one recall condition (IR: leisure, cognitive processes, space; DR: drives, number). The use of fewer prepositions in IR, more first-person pronouns in DR, and fewer words in the past tense in DR were each associated with cognitive impairment. Word count was not predictive of cognitive status. Both PFSs were highly associated with cognitive status (PFS_IR ß= 0.74, p&lt; 0.001; PFS_DR ß= 0.86, p= 0.001) with high discriminative value (PFS_IR AUC= 0.93, sensitivity = 0.81, specificity= 0.91; PFS_DR AUC= 0.95, sensitivity= 0.77, specificity= 0.88). In the CI subset, linguistic features differed between those classified as aMCI (n= 24) and naMCI (n= 40). Two function word categories predicted aMCI in IR whereas decreased word count, two function word categories, and two content word categories predicted aMCI in DR (all p&lt; .05)Conclusions:Linguistic features from paragraph recall provide high predictive value for classifying cognitive status increasing its potential as a cognitive screener in clinical settings. Additionally, each recall condition identified unique linguistic features associated with cognitive impairment which may aid differentiation of cognitive impairment subtypes and elucidate processes underlying deficits in learning and recall.

Analyzing Leadership Messaging Styles and Team Performance in the NFL: Insights from Post-First Loss Press Conferences

This study investigates the impact of leadership messaging styles used by NFL head coaches on team performance, specifically focusing on messaging following a team's initial loss of the season. The research builds upon previous findings that indicate certain leadership styles, such as Deliberativeness messaging, influence performance throughout the season. However, this study examines the messaging patterns specifically after the first loss. Qualitative data, consisting of press conference transcripts from NFL head coaches after their teams' initial loss between 2020 and 2022, were collected and analyzed using DICTION, a computer-aided text analysis program. This software generated numerical scores indicating the prevalence of different messaging styles: Activity, Optimism, Certainty, Realism, and Commonality. These scores facilitated quantitative tests and correlation analyses. The results reveal that most messaging styles exhibit weak associations with team performance. Nonetheless, the analysis identifies distinct tendencies among coaches, with a propensity for high levels of Optimism, low levels of Certainty and Realism, and moderately high levels of Commonality. These findings suggest that coaches may adopt these messaging patterns due to their effectiveness. Understanding these tendencies can assist amateur coaches in enhancing team performance following the first loss of the season. By leveraging the messaging tendencies identified in this study, coaches can potentially improve team outcomes.

Linguistic Differences by Gender in Letters of Recommendation for Maternal-Fetal Medicine Fellowship Applicants.

As in many realms of academia and medicine, in obstetrics and gynecology, women experience gender bias in residency evaluations and academic promotions. More specifically, women in Maternal-Fetal Medicine (MFM) are underrepresented within departmental leadership positions. As a means of identifying spaces where bias may exist, multiple investigators have previously reported on gender bias in letters of recommendation (LORs) for residency and subspecialty training programs. We aimed to determine if linguistic differences exist in LORs for self-identified male and female applicants to MFM fellowship at an academic institution. This was a retrospective single-site cohort study from 2019 to 2021. Data collected included applicant's age, self-reported race/ethnicity and gender, geographic region of residency, step 1 and 2 scores, scholarly and volunteer activities, and number of LORs. The Linguistic Inquiry and Word Count (LIWC) software, a validated text analysis program, was used to characterize LOR linguistic content. Multivariable analysis was used to compare letter characteristics to applicant demographics. A total of 212 applications were reviewed, including 808 LORs. Women comprised 76.9% of applicants, and men 23.1%. Most applicants identified as non-Hispanic White (52.8%). Men were more likely to be international medical graduates (20 vs. 6%, p ≤ 0.01), and women reported more volunteer activities (7.1 ± 5.1 vs. 5.5 ± 4.3, p = 0.04). There were no differences in step scores, number of research projects, or number of LORs. Multivariable analysis controlling for applicant race, step 1 score, and gender of letter writer revealed that letters written for males contained significantly more references to the word category cognitive processes (7.4 ± 0.2 vs. 7.1 ± 0.1, p = 0.046), specifically in reference to the subcategories of certainty and differentiation. We identified linguistic differences in LORs written for MFM applicants, suggesting potential bias in the style of writing for male and female physicians applying to this field. · Gender bias exists in the evaluation and promotion of women in medicine.. · We sought to determine whether it also exists in letters of recommendation for MFM fellowship.. · Previous studies have examined gender bias in letters of recommendation for other specialties.. · Linguistic bias was detected in letters of recommendation for MFM fellowship from 2019 through 2021.. · We found evidence of linguistic differences based on gender of applicant and letter writers..

Detecting ulterior motives from verbal cues in group deliberations.

Forensic interviewing entails practitioners interviewing suspects to secure valid information and elicit confessions. Such interviews are often conducted in police stations but may also occur in field settings such as border crossings, security checkpoints, bus terminals, and sports venues. Because these real-world interviews often lack experimental control and ground truth, this investigation explored whether results of non-forensic interviews generalize to forensic ones. Organizational espionage was simulated to determine (1) what verbal signals distinguish truth from deception, (2) whether deception in groups aligns with deception in dyads, and (3) whether non-forensic venues can be generalized to forensic ones. Engaging in a mock hiring deliberation, participants (4-5 strangers) reviewed and discussed resumes of potential candidates. Surreptitiously, two group members assigned the role of "organizational spies" attempted to persuade the group to hire an inferior candidate. Each group member presented notes from an interview of "their" candidate, followed by a discussion of all candidates. Spies were to use any means possible, including deception, to persuade others to select their candidate. A financial incentive was offered for having one's candidate chosen. The interview reports and discussions were transcribed and analyzed with SPLICE, an automated text analysis program. Deceivers were perceived as less trustworthy than truth-tellers, especially when the naïve players won but overall, deceivers were difficult for non-spies to detect even though they were seen as less trustworthy than the naïve participants. Deceivers' language was more complex and exhibited an "echoing" strategy of repeating others' opinions. This collusion evolved naturally, without pre-planning. No other verbal differences were evident, which suggests that the difference between spies and non-spies was subtle and difficult for truth-tellers to spot. Whether deception can be successfully detected hinges on a variety of factors including the deceiver's skill to disguise and the detector's ability to sense and process information. Furthermore, the group dynamics and communication context subtly moderate how deception manifests and influence the accuracy of detecting ulterior motives. Our future investigations could encompass non-verbal communication channels and verbal patterns rooted in content, thereby providing a more comprehensive understanding of deception detection.

Analyzing the Extent to which Gender Bias Exists in News Articles Using Natural Language Processing Techniques

Prior studies have shown the existence of gender bias in job postings, performance reviews, and letters of recommendation. However, very little research has been done on the presence of gender biases in mainstream news sources and how they vary across publications. Human editing, given the rapid pace of news dissemination, is not effective enough to address biases. Even computer programs that parse the news articles for specific words and references still fall short of identifying and detecting the undertones and implicit references, which is why sophisticated techniques like Artificial Intelligence (AI) are necessary. In this study, I used Natural Language Processing (NLP) methods, a series of Python-programs to further analyze how biases vary in new information, along the metrics of type, variety, and intensity. I used over 500,000 news articles from 15 publications, spanning over 4 years to build and train my algorithm. Using Word2Vec, a popular NLP method, I was able to conclude that more right leaning publications are more likely to exhibit misogynistic content that is biased against women. However, the method fell short of identifying many forms of objectification like Benevolent Sexism. Similarly, using VADER, a python-code of sentiment analysis tool, I was able to determine that mere metrics of positive, negative, and neutral sentiment are not sufficient to detect occurrences of gender bias. To gauge the breadth of sexist language effectively, I used the LIWC text analysis program which calculates the percentage of words in a given text that fall into one or more of over 80 linguistic, psychological and topical categories indicating various social, cognitive, and affective processes. As a result, with statistical evidence my study was able to conclude the presence of implicit gender bias occurs all across publications but is more prevalent in right-leaning publications.

Digital ethics challengers in Russian and English media texts: Migrant discourse case study

With digital technologies penetrating all spheres of life, the problem of digital ethics has become one of the most perspective and widely researched tasks of modern science and linguistics in particular. Digitalization influences the processes of media communication, which requires development of certain ethical standards in virtual environment. The given study aims at analysing and comparing digital ethics in Russian and English media texts. Within the framework of this study, digital etiquette is seen as a continuation of speech etiquette aimed at neutralizing aggression towards vulnerable members of society in the online environment. Using a combination of methods, namely sentiment analysis and analysis of lexical associations, messages about migrants in social networks, forums, blogs, instant messengers and microblogs were analyzed. The sentiment orientation of the text was determined using an automatic text analysis program and subsequent linguistic expertise. For this study we collected social media data of 8,478,629 characters (VKontakte, Odnoklassniki, YouTube, etc.). For the analysis of English-language media texts, iWeb data on the input word migrant was collected. The total volume of the analyzed English-language Internet messages is 1,185,200 characters. Migrant discourse in Russian and English social media texts is polythematic. The results show that negative and neutral tone prevail in English media texts while in Russian there are neutral, positive and negative texts. In English media texts, there are messages expressing solidarity with migrants. At the same time, the problems of ethnic intolerance and religious stereotyping still require consideration and further development of regulatory tools.

Качественное тематическое исследование динамики уровня тревожности пациента в отношениях с родными и близкими: анализ единичного случая психоаналитического консультирования

Introduction. The model of studying a single case and considering this version of the study of psychotherapy were implemented for the first time within the boundaries of the psychoanalytic paradigm. Today, this type of research is recognized and has promising developments in various areas and schools of psychotherapeutic practice. While there is some disagreement among academic psychologists and representatives of psychotherapy practice regarding the effectiveness of this method, it has a rather strong argument in favor of its application. The advantages of this method include: close attention to the acquisition of concrete, practical, contextual knowledge, as opposed to universal theo-retical knowledge without taking into account the context; the use of more flexible re-search standards and obtaining detailed data on the problem being solved. Methodology and methods: a qualitative case study of a group of isolated cases was conducted in 2018-2022 and was part of a pilot study to assess the effectiveness of psychoanalytic counseling of patients with high levels of anxiety. Patient indicators were collected us-ing psychodiagnostic techniques and recording material for sessions of 5 patients. To implement a qualitative thematic analysis of the dynamics of changes in the level of anx-iety, diagnostic tools have been developed to assess the manifestation of anxiety indica-tors in the speech of patients, based on the theoretical provisions of psychodynamic psy-chodiagnostics of anxiety. Results and conclusions. The results of a qualitative case study of the dynamics of anxiety and its impact on patients' relations with relatives and friends in the course of psychoanalytic counseling are presented. Analysis of the mani-festation of anxiety in the speech of patients showed that its decrease is reflected in less pronounced references to excitement, tension and negative feelings towards relatives and friends, and the semantic analysis of patients' speech with the help of the text anal-ysis program showed subtle movements in the semantic structure of their statements. Changes in the semantic structure of patients' speech reflect the individual way of their awareness of the problems under discussion and are reflected in the emergence of new semantic connections of the central

Framing Requests for Help: Language and Gender in Parental Advice-Seeking Letters

Confirming the importance of language to not just relay thoughts but also construct hierarchies, 251 parental advice-seeking letters written at the dawn of the parent education movement in the 1920s and 1930s were coded with the Linguistic Inquiry and Word Count computerized text analysis program. An examination of language use patterns shows how parents framed their requests and how they manifested signs of “doing gender” in the process. The fathers’ and mothers’ letters differed in length, common verb use, negation word use, pronoun use, preposition use, and locus on three summary variables (analytic, authenticity, and clout). They did not differ in the use of words associated with general affect, positive emotion, negative emotion, anger, and anxiety, thus exemplifying how the practice of “doing gender” can be circumstantial. The findings underscore the importance of studying the rhetorical aspects of parental advice-seeking communications and illustrate how language and gender influence these communications.

Goal language is associated with attrition and weight loss on a digital program: Observational study.

Behavioral weight loss reduces risk of weight-related health complications. Outcomes of behavioral weight loss programs include attrition and weight loss. There is reason to believe that individuals' written language on a weight management program may be associated with outcomes. Exploring associations between written language and these outcomes could potentially inform future efforts towards real-time automated identification of moments or individuals at high risk of suboptimal outcomes. Thus, in the first study of its kind, we explored whether individuals' written language in actual use of a program (i.e., outside of a controlled trial) is associated with attrition and weight loss. We examined two types of language: goal setting (i.e., language used in setting a goal at the start of the program) and goal striving (i.e., language used in conversations with a coach about the process of striving for goals) and whether they are associated with attrition and weight loss on a mobile weight management program. We used the most established automated text analysis program, Linguistic Inquiry Word Count (LIWC), to retrospectively analyze transcripts extracted from the program database. The strongest effects emerged for goal striving language. In striving for goals, psychologically distanced language was associated with more weight loss and less attrition, while psychologically immediate language was associated with less weight loss and higher attrition. Our results highlight the potential importance of distanced and immediate language in understanding outcomes like attrition and weight loss. These results, generated from real-world language, attrition, and weight loss (i.e., from individuals' natural usage of the program), have important implications for how future work can better understand outcomes, especially in real-world settings.

Linguistic Differences in Letters of Recommendation for Maternal Fetal Medicine Fellowship Applicants [A49

INTRODUCTION: Multiple medical specialties have previously reported on gender bias in letters of recommendation (LORs). We aimed to determine if linguistic differences exist in LORs for male and female applicants to a maternal fetal medicine (MFM) fellowship at an academic institution. METHODS: This was an institutional review board (IRB)-approved retrospective single-site cohort study conducted from 2019-2021. Data collected included applicant’s age, race, self-reported gender, geographic region of residency, Step 1 and 2 scores, scholarly and volunteer activities, and number of LORs. The Linguistic Inquiry and Word Count (LIWC) software, a validated text analysis program, was used to characterize LORs’ linguistic content. Multivariable analysis was used to compare letter characteristics to applicant demographics. RESULTS: A total of 212 applications were reviewed, including 808 LORs. Women comprised 76.9% of applicants and men 23.1%. Most applicants identified as non-Hispanic White (52.8%). Men were more likely to be international medical graduates (20% vs. 6%, P=&lt;.01), and women reported more volunteer activities (7.1 ± 5.1 vs. 5.5 ± 4.3, P=.04). There were no significant differences in Step scores, number of research projects, or number of LORs. Multivariable analysis controlling for applicant race, Step 1 score, and gender of the letter writer revealed that letters written for males contained significantly more references to the word category cognitive processes (7.4 ± 0.2 vs. 7.1 ± 0.1, P=.046), specifically in reference to the subcategories of certainty and differentiation. CONCLUSION: We identified linguistic differences in LORs written for MFM applicants, suggesting potential bias in the style of writing for male and female physicians applying to this field.

Cognitive-Affective Styles of Biden and Trump Supporters: An Automated Text Analysis Study

Are conservatives more simple-minded and happier than liberals? To revisit this question, 1,518 demographically diverse participants (52% females) were recruited from an online participant-sourcing platform and asked to write a narrative about the upcoming 2020 U.S. Presidential Election as well as complete self and candidates’ ratings of personality. The narratives were analyzed using three well-validated text analysis programs. As expected, extremely enthusiastic Trump supporters used less cognitively complex and more confident language than both their less enthusiastic counterparts and Biden supporters. Trump supporters also used more positive affective language than Biden supporters. More simplistic and categorical modes of thinking as well as positive emotional tone were also associated with positive perceptions of Trump’s, but not Biden’s personality. Dialectical complexity and positive emotional tone accounted for significant unique variance in predicting appraisals of Trump’s trustworthiness/integrity even after controlling for demographic variables, self-ratings of conscientiousness and openness, and political affiliation.

Linguistic Differences by Gender in Letters of Recommendation for Minimally Invasive Gynecologic Surgery Fellowship Applicants

Various surgical specialties have reported gender bias in letters of recommendation (LOR). We aimed to determine if linguistic differences exist in LOR for female and male physicians applying to Fellowship in Minimally Invasive Gynecologic Surgery (FMIGS). Retrospective cohort study including application cycles 2019 and 2020. Academic university hospital. FMIGS applicants. Applicant demographic and baseline data included age, race, gender, geographical region of residency training, Step 1 and 2 scores, number of research and volunteer activities, Alpha Omega Alpha and Gold Humanism status and number of LOR, as well as the gender and academic rank of the letter writer. The Linguistic Inquiry and Word Count software, a validated text analysis program, was used to characterize LOR linguistic content. A total of 118 applications, including 391 letters, were analyzed. Seventy-six (64.4%) applicants were female and 42 (35.6%) were male. Most female applicants were white (46% vs. 36%, p = 0.04), had Alpha Omega Alpha status (13% vs. 0%, p = 0.01), higher Step 2 scores (239.7 vs. 230.4, p < 0.01), and more service activities (7.7 vs. 4.7, p < 0.01), compared to male applicants. Male applicants were more likely to graduate from international medical schools (45% vs. 16%, p < 0.01). Female authors wrote 159 LOR, and male authors wrote 232. Following multivariable analysis controlling for race, Step 1 score and letter writer gender, no significant differences in average LOR word count for female and male applicants (406.7 ± 24.2 words vs. 340.1 ± 35.4 words), or differences in Linguistic Inquiry and Word Count linguistic categories existed. Although Baseline differences were noted between female and male FMIGS applicants, no differences in LOR length or linguistic categories were noted. These results likely reflect the impact of female predominance and increased efforts to address gender bias within Obstetrics and Gynecology.

Cybersecurity Risk in U.S. Critical Infrastructure: An Analysis of Publicly Available U.S. Government Alerts and Advisories

As threat actor operations become increasingly sophisticated and emphasize the targeting of critical infrastructure and services, the need for cybersecurity information sharing will continue to grow. Escalating demand for cyber threat intelligence and information sharing across the cybersecurity community has resulted in the need to better understand the information produced by reputable sources such as U.S. CISA Alerts and ICS-CERT advisories. The text analysis program, Profiler Plus, is used to extract information from 1,574 U.S. government alerts and advisories to develop visualizations and generate enhanced insights into different cyber threat actor types, the tactics which can be used for cyber operations, and sectors of critical infrastructure at risk of an attack. The findings of this study enhance cyber threat intelligence activities by enabling an understanding of the trends in public information sharing as well as identifying gaps in open-source reporting on cyber-threat information.

Technologies of deep text analysis in musicology

The article considers the possibility of improving the mechanism of work of the author-musicologist with the texts of historical musicology. The purpose of the article is to consider the mechanisms of detecting internal relationships between the elements of a musicological text. The subject of the study is verbal texts of musicology. The object of this article is analytical tools of modern digital technologies for working with texts. The author examines in detail such aspects as the selection and formation of a set of keywords, the definition of the structure of the search for information necessary for research. Particular attention is paid to the internal processes of the musicology laboratory, within which it is not always possible to form a set of key verbal meanings of a prepared study using traditional methods. The author for the first time proposed a methodology for using computer tools for deep text analysis programs in the daily activities of a musicologist-researcher. The scope of the methodology is not limited to the field of music science, it can find solutions in other fields of knowledge. In the course of the research, the practical side of the product implementation is highlighted, the stages of post-processing of a text array are presented in a concise form. At the same time, the problematic "slice" stated in the study turns out to be saturated with other information content, which allows us to further find hidden patterns of development of certain processes, discover new relationships and determine new vectors of scientific research.