Word Count Research Articles

The utility of Chat Generative Pre-trained Transformer as a patient resource in paediatric otolaryngology.

This study aimed to determine the feasibility of using Chat Generative Pre-trained Transformer (ChatGPT) (https://chatgpt.com) as a patient resource for paediatric otolaryngology conditions and assess the quality of responses generated by ChatGPT when compared with information available on the internet. ChatGPT responses to common paediatric otolaryngology conditions were compared with top internet pages for readability (Flesch Reading Ease score, word count), expediency (time taken to generate response), validity (comparison of recommendations to the American Academy of Otolaryngology Head and Neck Surgery guidelines) and consistency (changes in recommendations based on question variation). ChatGPT was more expeditious in generating responses with fewer words, albeit with higher reading scores. When compared with accredited guidelines, there was no difference in validity between these sources (internet sources and ChatGPT). Consistent responses were obtained with question variation. ChatGPT may be a valuable source for patients and families in providing valid information comparable to internet materials.

Anonymous and Insecure Bullies are Less Depressed than Confident and Identifiable Ones, but Only if Remorseful: Cyberbullying Goals, Identifiability, and Depression.

The connection between bullying others and depression is clear. Less clear are the communicative paths through which being a bully leads to depression. Cyberbullying consists of communicative episodes that transcend modes of communication, contexts, and relationships wherein a social network of communicators pursues a subordinate goal of harming other(s) mentally, emotionally, and/or physically to achieve a hierarchically represented set of superordinate goals. Rooted in this conceptualization, we asked 739 undergraduate students to report on a memorable episode of which 374 met our criteria and reported on sending a series of hurtful messages. We employed close-ended self-report measures, as well as open-ended responses subjected to Linguistic Inquiry and Word Count (LIWC-22) sentiment analysis, and moderated mediation models. Data suggest four conclusions. First, the extent to which bullies attack for five cyberbullying goals (insecurity, past-harm, highlight-differences, upward-mobility, and revenge) depends on how identifiable a cyberbully feels during the bullying episodes. Second, whereas bivariate associations among the five goals and depression emerged, when considering the full theoretical model, only insecurity goals sustained as an effective predictor of increased levels of depression. Third, anonymous bullies who attack because they are insecure are less depressed than confident and identifiable bullies, but only if they experience negative emotions post-attack. Finally, message severity (assessed via LIWC-22) was (a) an ineffective mediator, (b) not associated with depression or identifiability, and (c) mostly not associated with goals.

Phenotypic and Genotypic Correlates of the NaHCO3-Responsive Phenotype Among Methicillin-Resistant Staphylococcus aureus (MRSA) Isolates from Skin and Soft Tissue Infections (SSTIs)

ObjectivesTo assess the frequency of the novel NaHCO3-responsive phenotype, wherein clinical methicillin-resistant Staphylococcus aureus (MRSA) isolates are rendered susceptible to standard-of-care β-lactams in the presence of NaHCO3, in a collection of 103 clinical US MRSA skin and soft tissue infection (SSTI) isolates and 22 clinical European SSTI isolates. Determined the correlation between specific phenotypic and genotypic metrics and the NaHCO3-responsive phenotype among US SSTI isolates. MethodsAntimicrobial susceptibility testing (AST) was performed to determine susceptibility phenotypes. Targeted and whole genome sequencing (WGS) with a genome-wide sequence analysis (GWAS) were conducted to identify specific and novel genotypes of interest that may be associated with the NaHCO3-responsive phenotype. Gene expression analysis and targeted gene deletion were performed to assess the role of a specific novel genetic locus in the NaHCO3-responsive phenotype. ResultsThe NaHCO3-responsive phenotype was identified in 78/103 US isolates and 4/22 UK isolates to cefazolin (CFZ), and in 17/103 US isolates and 1/22 UK isolates to oxacillin (OXA). In US isolates, a significant association was identified between NaHCO3-responsiveness to CFZ and: i) susceptibility to amoxicillin-clavulanate; ii) a specific mecA genotype; iii) clonal complex (CC) type 8; and iv) spa type t008. GWAS identified SNPs in an AraC family regulator (SAUSA300_RS00540) to be exclusively found in NaHCO3-non-responsive SSTI strains. In vitro HCO3 exposures of NaHCO3-responsive strains, but not -non-responsive strains, caused >2-fold up-regulated expression of this gene. Deletion of this gene rendered NaHCO3-responsive strain MRSA 11/11 no longer NaHCO3-responisve to CFZ; we have termed this gene the staphylococcal AraC bicarbonate-response regulator, sabR. ConclusionsNaHCO3-responsiveness is highly associated with CC8/spa t008, a commonly circulating genetic background in North America. The AraC bicarbonate-response regulator, sabR, appears to be associated with the mechanism of NaHCO3-responisveness, more work is needed to verify. Word count 288.

Emotions and entrepreneurial finance: Analysis of venture capitalists’ and business angels’ digital footprints on Twitter

Emotions are a central concept in previous entrepreneurship research, but this is mainly related to entrepreneurs and their entrepreneurial journey. However, venture capitalists (VCs) and business angels (BAs), two critical investors in the entrepreneurial finance literature, are essential actors in the entrepreneurial process. Still, little is known about investor emotions in this context. Therefore, in this study, we ask how venture capitalists differ from business angels in terms of their expressed emotions. To this end, we use an increasingly familiar research approach by examining the digital footprints of these investors on Twitter. For this purpose, we identify 822 investors from Crunchbase and analyze their 994,969 Tweets with Linguistic Inquiry and Word Count (LIWC) as a text analysis tool. Our results show that venture capitalists display more positive emotions on Twitter than angel investors, meaning that we find an association between VCs and emotional tone. Furthermore, in our post-hoc analysis, we explore further explanations for the differences between VC and BA. In doing so, we show differences in their expressed cognitive processes as well as in their communicated drivers. In both concepts, we find positive associations with the investor type of VC. To conclude this paper, we develop implications for practice and further research based on the results.

Scalable Transformer Accelerator with Variable Systolic Array for Multiple Models in Voice Assistant Applications

Transformer model is a type of deep learning model that has quickly become fundamental in natural language processing (NLP) and other machine learning tasks. Transformer hardware accelerators are usually designed for specific models, such as Bidirectional Encoder Representations from Transformers (BERT), and vision Transformer models, like the ViT. In this study, we propose a Scalable Transformer Accelerator Unit (STAU) for multiple models, enabling efficient handling of various Transformer models used in voice assistant applications. Variable Systolic Array (VSA) centralized design, along with control and data preprocessing in embedded processors, enables matrix operations of varying sizes. In addition, we propose an efficient variable structure and a row-wise data input method for natural language processing where the word count changes. The proposed scalable Transformer accelerator accelerates text summarization, audio processing, image search, and generative AI used in voice assistance.

LCD benchmark: long clinical document benchmark on mortality prediction for language models.

The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent. To address this issue, we propose Long Clinical Document (LCD) benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of Medical Information Mart for Intensive Care IV and statewide death data. We evaluated this benchmark dataset using baseline models, from bag-of-words and convolutional neural network to instruction-tuned large language models. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations. Baseline models showed 28.9% for best-performing supervised models and 32.2% for GPT-4 in F1 metrics. Notes in our dataset have a median word count of 1687. Our analysis of the model outputs showed that our dataset is challenging for both models and human experts, but the models can find meaningful signals from the text. We expect our LCD benchmark to be a resource for the development of advanced supervised models, or prompting methods, tailored for clinical text.

Similarities and differences in diurnal salivary adrenal hormones in monozygotic twins with discordant birthweight.

Introduction DHEAS is increased in formerly small-for-gestational-age singletons and in smaller twins compared to their normal-weight co-twin. Less is known concerning other adrenal hormones. We compared diurnal salivary profiles in monozygotic twins with intra-twin birthweight(bw)-differences to analyse the long-term impact of bw and persistent intra-twin auxological differences on various adrenal hormones. Methods 46 monozygotic pre-pubertal twin-pairs with bw-difference of <1SDS (concordant; n=29, 13 female) and ≥1SDS (discordant; n=17, 6 female) were recruited. At a mean age of 6.9 years (yrs) saliva samples were collected (7 am/waking, 1 pm, 6 pm, and 9 pm) and analysed with liquid chromatography-tandem mass spectrometry. Results Diurnal salivary concentrations showed significant intra-twin correlations in all twin-pairs for nearly all time-points: progesterone and androstenedione (4/4 time-points), 17-hydroxyprogesterone (2/4). However, in the discordant group mean progesterone differed significantly (p=0.018) between the smaller (3.27nmol/L, SD 3.87) and the larger twin (0.72nmol/L, SD 0.42) at 7 am and intra-twin differences were observed at 3/4 time-points. Regression analyses of intra-twin differences confirmed that actual hormonal parameters were explained mainly by the co-twin parameter. However, at 6 pm progesterone concentration of the smaller twin was explained by bw (Β -1.102; SE 0.563; p=0.05) and actual weight (Β 0.799; SE 0.327; p=0.019). Conclusion The significant correlations within monozygotic twin-pairs in all measured hormones at nearly all time-points suggest a strong genetic influence on the adrenal axis. Among the discordant twin pairs, we found significant variations in progesterone, suggesting additional long-lasting influence from intrauterine conditions on adrenal hormones with possible metabolic consequences. Word count 249.

A Study on Patents Invalidation Reexamination Decisions for Discussing Variance between Strong Utility Models and Weak Utility Models

Abstract 19,082 China utility model patents were retrieved from invalidation reexaminations decisions. A thorough analysis using ANOVA was conducted across nine technology areas for discussing the variances between weak utility models, in any of which all claims were invalid through the reexaminations, and strong utility models, in any of which at least one claim was remaining valid. Four high value indicators for classifying utility models were found, including description word count, examination duration, figure count and claim count, to respectively show significance in five technology areas; wherein the strong patents showed significantly higher means of indicators in every technology areas of significance. Two fair value indicators for classification were found, including IPC count and abstract word count, to respectively show significance in three technology areas. Two low value indicators for classification were found, including inventor count and applicant count, showing significance in two or less technology areas. Technology distinction was shown. The overall technology and technology G (physics) were respectively provided with the most number of five valuable indicators, while technology C (chemistry and metallurgy) and D (textiles; paper) were respectively provided with the least number of three valuable indicators. The technologies provided with more valuable indicators were more applicable for classifying strong/weak utility models. The strong utility models were shown to be provided with more claim terms, more figures, richer description content and longer examination duration. The criteria for classification was therefore obtained. JEL classification: C38, C46, G11, G12. Keywords: Patent, ANOVA, Utility Model, Reexamination, Invalidation.

Exploring Valuable Indicators for Classifying Strong and Weak Patents Based on Invalidation Reexamination Decisions

Abstract Based on 8,666 invalidation reexamined China invention grant patents of decision dates from 2000 to 2021, the effect and value of eight indicators for classifying strong and weak patents in nine technology areas, including overall technology and eight technology sections, was thoroughly analyzed via ANOVA. Two high valuable indicators including abstract word count and examination duration for classification were found, which respectively showed significance in five technology areas. Four less valuable indicators, including claim count, figure count, inventor count and IPC count, were found to respectively show significance in one or two technology areas. A valueless indicator of applicant count was found to show none of significance in any technology areas. The strong patents did not always show higher indicator means of significance. Especially for the high valuable indicator of examination duration, the strong patents showed lower means in any of five technology areas of significance. The finding of this research would contribute the state of art in evaluating patents and help patent owners improve their patent asset management strategy. JEL classification numbers: C38, C46, G11, G12.

Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.

Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the perspectives of older adults on an integrated digital brain health platform have not been investigated. This study aims to understand the perspectives of older adults on a digital brain health platform by conducting semistructured interviews and analyzing their transcriptions by natural language processing. The study included 28 participants from the Boston University Alzheimer's Disease Research Center, all of whom engaged with a digital brain health platform over an initial assessment period of 14 days. Semistructured interviews were conducted to collect data on participants' experiences with the digital brain health platform. The transcripts generated from these interviews were analyzed using natural language processing techniques. The frequency of positive and negative terms was evaluated through word count analysis. A sentiment analysis was used to measure the emotional tone and subjective perceptions of the participants toward the digital platform. Word count analysis revealed a generally positive sentiment toward the digital platform, with "like," "well," and "good" being the most frequently mentioned positive terms. However, terms such as "problem" and "hard" indicated certain challenges faced by participants. Sentiment analysis showed a slightly positive attitude with a median polarity score of 0.13 (IQR 0.08-0.15), ranging from -1 (completely negative) to 1 (completely positive), and a median subjectivity score of 0.51 (IQR 0.47-0.53), ranging from 0 (completely objective) to 1 (completely subjective). These results suggested an overall positive attitude among the study cohort. The study highlights the importance of understanding older adults' attitudes toward digital health platforms amid the comprehensive evolution of the digitalization era. Future research should focus on refining digital solutions to meet the specific needs of older adults, fostering a more personalized approach to brain health.

Interpersonal Goals and Language Use During the Disclosure of a Concealable Stigmatized Identity: A LIWC Analysis

Previous theorizing suggests that the first stage of self-disclosure is the pursuit of an interpersonal goal. However, among individuals disclosing a concealable stigmatized identity, little is known about how their goals impact downstream processes such as language use and post-disclosure affect. Using Linguistic Inquiry and Word Count (LIWC), we examined the extent to which primed self-image or compassionate goals influence the linguistic content and style of disclosure, as well as dyad members’ affective states. Self-image disclosures were high in negative self-focused attention, whereas compassionate disclosures were more inclusive and positive in valence. Moreover, among disclosers, negative emotion words predicted their own negative affect. Among confidants, first-person singular and second-person pronouns predicted negative affect, however, this outcome was inversely related to disclosers’ use of social words. Compassionate goals that seek mutually beneficial outcomes may help disclosers communicate information about their stigma more effectively, and attenuate dyad members’ feelings of emotional distress.

Emerging Adults With a Suicide Attempt History Text More Emotionally With Peers than Family Members

Emotional communication is central to strong interpersonal relationships and mental health. For emerging adults, much of their interpersonal communication happens via texting, but we know little about how and with whom their emotional texting occurs. Understanding these patterns may be especially critical for emerging adults experiencing mental health crises (e.g., suicidality). This study examined how the emotional tone of texts (sent and received) varies based on texting partner (i.e., peers, which includes friends and significant others, vs. family) and within-person suicide risk. Participants were 27 emerging adults with prior, non-lethal suicide attempt(s). Linguistic Inquiry Word Count was used to label 75,928 texts. While there was variation in the specific interactions, results overall indicated that participants exchanged texts containing all emotion category words at greater rates with peers (vs. family), and this pattern generally held across within-person suicide risk level. Implications for suicide prevention/intervention efforts are discussed.

Readable and neutral? Reliability of crowdsourced misinformation debunking through linguistic and psycholinguistic cues.

In the face of the proliferation of misinformation during the COVID-19 pandemic, crowdsourced debunking has surfaced as a counter-infodemic measure to complement efforts from professionals and regular individuals. In 2021, X (formerly Twitter) initiated its community-driven fact-checking program, named Community Notes (formerly Birdwatch). This program allows users to create contextual and corrective notes for misleading posts and rate the helpfulness of others' contributions. The effectiveness of the platform has been preliminarily verified, but mixed findings on reliability indicate the need for further research. The study aims to assess the reliability of Community Notes by comparing the readability and language neutrality of helpful and unhelpful notes. A total of 7,705 helpful notes and 2,091 unhelpful notes spanning from January 20, 2021, to May 30, 2023 were collected. Measures of reading ease, analytical thinking, affect and authenticity were derived by means of Wordless and Linguistic Inquiry and Word Count (LIWC). Subsequently, the non-parametric Mann-Whitney U-test was employed to evaluate the differences between the helpful and unhelpful groups. Both groups of notes are easy to read with no notable difference. Helpful notes show significantly greater logical thinking, authenticity, and emotional restraint than unhelpful ones. As such, the reliability of Community Notes is validated in terms of readability and neutrality. Nevertheless, the prevalence of prepared, negative and swear language in unhelpful notes indicates the manipulative and abusive attempts on the platform. The wide value range in the unhelpful group and overall limited consensus on note helpfulness also suggest the complex information ecology within the crowdsourced platform, highlighting the necessity of further guidance and management. Based on the statistical analysis of the linguistic and psycholinguistic characteristics, the study validated the reliability of Community Notes and identified room for improvement. Future endeavors could explore the psychological motivations underlying volunteering, gaming, or even manipulative behaviors, enhance the crowdsourced debunking system and integrate it with broader efforts in infodemic management.

How Useful are Current Chatbots Regarding Urology Patient Information? Comparison of the Ten Most Popular Chatbots' Responses About Female Urinary Incontinence.

This research evaluates the readability and quality of patient information material about female urinary incontinence (fUI) in ten popular artificial intelligence (AI) supported chatbots. We used the most recent versions of 10 widely-used chatbots, including OpenAI's GPT-4, Claude-3 Sonnet, Grok 1.5, Mistral Large 2, Google Palm 2, Meta's Llama 3, HuggingChat v0.8.4, Microsoft's Copilot, Gemini Advanced, and Perplexity. Prompts were created to generate texts about UI, stress type UI, urge type UI, and mix type UI. The modified Ensuring Quality Information for Patients (EQIP) technique and QUEST (Quality Evaluating Scoring Tool) were used to assess the quality, and the average of 8 well-known readability formulas, which is Average Reading Level Consensus (ARLC), were used to evaluate readability. When comparing the average scores, there were significant differences in the mean mQEIP and QUEST scores across ten chatbots (p = 0.049 and p = 0.018). Gemini received the greatest mean scores for mEQIP and QUEST, whereas Grok had the lowest values. The chatbots exhibited significant differences in mean ARLC, word count, and sentence count (p = 0.047, p = 0.001, and p = 0.001, respectively). For readability, Grok is the easiest to read, while Mistral is highly complex to understand. AI-supported chatbot technology needs to be improved in terms of readability and quality of patient information regarding female UI.

ChatGPT versus Google Gemini: a comparison to evaluate patient education guide created on common neurological disorders

IntroductionThis study assesses the effectiveness of AI tools, ChatGPT and Google Gemini, in educating the public about neurological conditions such as Bell’s palsy, Tetanus, and Headaches, and assess chatbot-generated patient guides based on readability and ease of understanding.MethodologyIn March 2024, the authors conducted a cross-sectional study to develop patient education brochures for Bell’s palsy, Tetanus vaccination, and Persistent Headache, leveraging AI models ChatGPT and Google Gemini. The brochures’ quality was assessed through readability, similarity, and a modified DISCERN score for reliability. Statistical analysis, performed in R software, compared responses from both AI models using unpaired T-tests. The correlation between ease score and reliability was explored using Pearson’s Coefficient of Correlation.ResultsThe study revealed no significant variances in word count, sentence count, or average words per sentence between the two AI tools. However, materials generated by ChatGPT exhibited significantly higher ease scores, highlighting its proficiency in creating more understandable content (p < 0.05).ConclusionsThis study finds ChatGPT outperforms Google Gemini in readability despite similar metrics. This suggests ChatGPT's potential superiority in creating more understandable patient education materials. As AI advances, it’s essential to research more tools and medical conditions to ensure they meet diverse patient education needs.

Evaluating the visual literacy of the Kuwaiti government’s Arabic infographics during the COVID-19 pandemic: an automated visual analysis and AI approach

PurposeWith infographics emerging as key communication tools on social media platforms, this study explores the visual literacy of governments in creating effective infographics, especially during crises. Using the Kuwaiti government during the COVID-19 pandemic as a case study, the investigation evaluates the government’s visual competency and strategy in its infographics on X.Design/methodology/approachFor competence, AI-based techniques were employed to analyze the proportion of text region size to total infographic size, word count per infographic and the most prominent colors used. Regarding strategy, the study utilized the crisis and emergency risk communication (CERC) model as a framework to examine how the Kuwaiti government integrated crisis communication response strategies into infographics.FindingsWhen communicating complex messages, the government resorted to text-heavy infographics instead of creative visualizations, casting doubt on its visual competence. The inconsistent use of colors further undermined a recognizable visual identity. Regarding strategy, infographics on crisis updates were most frequent, supporting CERC’s emphasis on reducing uncertainty. Yet, prioritizing bolstering strategies above empathy and action steps goes against the advice of existing literature.Originality/valueWhile crisis communication research is widely based on textual analysis, this study extends the literature by examining visuals, specifically infographics. Additionally, focusing on Arabic infographics from Kuwait, the study expands the crisis communication literature, which has mainly concentrated on Western countries and the English language. Given the lack of consensus on the best methods to measure visual literacy, this study’s AI approaches contribute to the literature.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0172

Using Textual Analysis to Examine Student Engagement in Online Undergraduate Science Education

This study investigates the relationship between student engagement and peer review performance in an online data science course. Employing a mixed-methods approach, we analyzed quantitative data on peer review metrics (word count, sentiment, and complexity) and qualitative insights from student comments from 2019 to 2021. Our findings indicate a positive correlation between higher student grades and more detailed, positive peer reviews, as measured by word count, sentiment, and keyword matches. While the overall changes in engagement metrics were not statistically significant, trends suggest increased engagement over time. These findings contribute to a growing body of research on the effectiveness of peer review in online learning environments and highlight its potential to foster student development in data science. Future research should address limitations such as sample size and explore broader applications across diverse online courses. Supplementary materials for this article are available online.

Study of U.S. Democratic and Republican Senators' Political Framing Strategies on Abortion Rights Based on Social Identity Theory and Framing Effect

Political framing significantly shapes public opinions and legislative outcomes in the United States, making the understanding of Democratic and Republican senators' strategies concerning abortion rights necessary. This study employs Social Identity Theory and Framing Effect to analyze cognitive and affective language patterns, as well as frequent references to specific word count in senators' speeches, using the Linguistic Inquiry and Word Count (LIWC) program. The findings reveal distinct partisan differences: Democrats frequently emphasize women's rights and personal freedoms, while Republicans focus on morality and the sanctity of unborn life. These divergent framing strategies highlight how party affiliation influences the discourse on this contentious issue. The research, using both qualitative and quantitative approaches, underscores the complexity of political communication and the need for nuanced studies to explore how language reflects and shapes political identities and ideologies.

Analysing loneliness forum posts, the comments they elicit, and the responses to these comments

In May 2023, the United States surgeon general put out an advisory suggesting that loneliness and social isolation should be tackled and prioritized like health conditions such as substance abuse. Online loneliness forums provide a platform for users experiencing loneliness to engage with, share experiences/concerns, and seek support from others. On these forums, users (posters) publish support seeking posts and other users respond by either reacting to these posts and/or writing comments in which they provide support. In some cases, the posters respond to and engage with the comments that their posts elicit; understanding these interactions between posters and the comments their posts elicit can inform helpful communication strategies on online loneliness forums and online health forums, in general. Prior work on analyzing data from online loneliness forums did not study the interactions between posters and the comments their posts elicit. To address this, we present a dataset of posts and comments published in a 4 year time period i.e. from January 1 2019 to December 31 2022 on an online loneliness forum on Reddit. This dataset consists of (a) posts, (b) all the comments associated with these posts, (c) the comments that elicited responses from the posters, and (d) the responses of the posters to these comments. With this dataset, we conduct analysis using a topic modeling algorithm called BERTopic and a psycholinguistic dictionary called Linguistic Inquiry and word count (LIWC) to gain insights and elucidate the language markers associated with comments (to posts) that elicit responses from the posters. We find that as it relates to comments that received responses from posters, the following topic themes were associated with these comments i.e. relationships, empathy, and mental health concerns and the LIWC categories on second person pronoun, social processes, and present focus, were associated with these comments. We also find that the topic themes on appreciation was associated with the responses by posters to comments their posts received. We discuss these findings in the discussion section.

How dark traits and regulatory focus manifest in online hotel reviews: a textual content analysis

ABSTRACT This study seeks to explore how Dark Triad of psychopathy, Machiavellianism, and narcissism interact with consumers’ regulatory focus (prevention-focus and promotion-focus) to influence online consumer reviews in the hotel industry. We conducted an empirical analysis of 1,185,940 hotel reviews from TripAdvisor.com, utilizing the textual content of online reviews and employing Language Inquiry and Word Count analytical tools. The research reveals a multifaceted link between regulatory focus and Dark Triad in online reviews. The results showed that individuals with a promotion focus tend to give higher ratings than those with a prevention focus. Dark Triad significantly influences how regulatory focus impacts online reviews. This study initiates a new academic dialogue on the interaction between regulatory focus and Dark Triad traits within online consumer behavior studies. It provides practical guidance for the hotel industry to refine their management and response strategies by acknowledging the impact of consumer personality traits on online reviews.