Exploring gender differences in vacation preferences: a mixed-methods analysis

Expectancy value theory is often evoked by educational psychologists to explain gender differences in Science, Technology, Engineering, and Mathematics (STEM) variables. Yet gender does not operate in isolation. Nor are gender effects likely to be context free. In the current meta-analysis, we explore gender differences in STEM-related expectancy for success, and the task values of intrinsic, utility, attainment, and cost. We find that gender differences were generally small in size. Invoking the concept of intersectionality, we find that heterogeneity in gender effect sizes are large and gender differences are moderated, primarily, by socioeconomic status, ethnic diversity, and somewhat by national gender equality.

Organisations these days are actively using social media platforms to engage with potential and existing customers and monitor what they say about the organisation’s product or service. The most important area within social media monitoring lies in how to gain insight for sentiment analysis. Sentiment analysis helps in effective evaluation of customer’s sentiments in real time and takes on a special meaning in the context of online social networks like Twitter and Facebook, which collectively represent the largest online forum available for public opinion. Sentiment Analysis is not about retrieving and analyzing the analytics purely on the basis of positive, negative or neutral sentiment. It is imperative to assess the influencers of the sentiments in terms of Retweet and Share option used by them on Twitter and Facebook platform respectively. Measuring the intensity is other important aspect of sentiment analysis process. What kind of nouns, adjectives, verbs and adverbs are used in the opinion across the Twitter and Facebook platform matters as well since it exhibits the intensity of the underlying emotion in the text written. This study was conducted to propose a framework to identify and analyse the positive and negative sentiments present in Twitter and Facebook platforms and an algorithm was prepared to measure the intensity and influence of the positive, negative sentiment in particular using the document and sentence level analysis technique.

Sleep and depression are comorbid problems that contribute to the development of chronic medical conditions (CMC) over time. Although racial and gender differences in the bidirectional associations between sleep, depression, and CMC are known, very limited information exists on heterogeneity of the residual effects of sleep problems over depressive symptoms on CMC across race by gender groups. Using a life-course perspective, the present study compared race by gender groups for residual effects of restless sleep over depressive symptoms on CMC. We used data from waves 1 (year 1986), 4 (year 2001), and 5 (year 2011) of the Americans' Changing Lives Study (ACL). The study followed 294 White men, 108 Black men, 490 White women, and 237 Black women for 25years. Restless sleep, depressive symptoms (Center for Epidemiological Studies-Depression scale [CES-D]), and number of chronic medical conditions (hypertension, diabetes, chronic lung disease, heart disease, stroke, cancer, and arthritis) were measured in 1986, 2001, and 2011. We employed multi-group cross-lagged modeling, with chronic medical conditions as the outcome and race by gender as the groups. Major group differences were found in the residual effect of restless sleep on CMC over depressive symptoms across race by gender groups. Restless sleep in 2001 predicted CMC 10years later in 2011 among Black women (standardized adjusted B = .135, P < .05) and White men (standardized adjusted = .145, P < .01) and White women (standardized adjusted B = .171, P < .001) but not Black men (standardized adjusted B = .001, P > .05). Race by gender heterogeneity in the residual effect of restless sleep over depressive symptoms on CMC over 25years suggests that comorbid poor sleep and depressive symptoms differently contribute to development of multi-morbidity among subpopulations based on the intersection of race and gender. Thus, interventions that try to prevent comorbid sleep problems and depression as a strategy to prevent medical conditions may benefit from tailoring based on the intersection of race and gender.

Overview: The rise of e-commercehas created a more interactive and competitive environment for both sellers and buyers. In recent times, a heavy reliance on online product reviews posted on the e-commerce website by existing and verified users has been witnessed. From the extant literature, it is evident that the impact of non–textual or quantitative aspects of online product reviews (OPR), such as volume, valence, star rating, helpful votes, etc., on buying behaviour, purchase intention, sales, etc. has been vastly studied. But since the past decade, the importance of analysing and understanding the textual data of OPR has been strongly recommended and explored.Aim: Reviewers express their opinions through non-structured and unfiltered reviewswhere the information presented goes beyond mere words.The review text has embedded emotions and sentiments also. The current research aims to investigate and comprehend the implicit information conveyed by consumers in the unstructured and heartfelt reviews on Amazon.in. The study seeks to identify key semantic aspects affecting OPRs' assessment by highlighting latent topics, sentiments and intricacies within the textual content of the reviews. Methodology: Amazon is the most popular e-commerce site in India, and its review system is widely seen as reliable, clear, and standardised. Its multi-dimensional format—textual reviews(review statements and title statements), aggregate star ratings which guarantee consistency across many product categories and offers a solid foundation forqualitative research. This study employed a multistage sampling technique to extract 5,900 reviews from 59 top-selling products across three best-selling categories: beauty, fashion, and electronics from Amazon India. Web scraping has been used to extract the review data using Python as a programming language. A qualitative content analysis, topic modelling, sentiment analysis and sentiment score analysis have been employed using various packages and functions in R Studio.Findings: The research reveals that both negative and positive sentiments have significant effects on product ratings. The word count analysis indicated a predominant use of positive words such as ‘good’, ‘product’, ‘quality’, ‘nice’, and ' price’. The three distinct topics identified through topic modelling demonstrate that reviews are shaped by a combination of functional utility, sensory experience, and detailed product attributes. The sentiment analysis revealed that positive sentiment is more common than negative sentiment. Additionally, emotions like trust, anticipation and joy were predominant, while negative emotions such as disgust and anger were less frequently observed. A key finding is the asymmetrical effect, where negative sentiment has a notably stronger negative impact than positive sentiment.Although statistically significant, the relatively low R-squared value suggests that sentiment scores alone account for only a small part of the variance in product ratings, indicating that ratings are a complex outcome influenced by multiple factors beyond expressed sentiment. Implications: The study highlights the multifaceted role of online product reviews in shaping consumer behaviour on e-commerce platforms. The predominance of positive emotions emphasises the value of building strong consumer confidence. At the same time, the asymmetrical effect of negative sentiment urges the need for businesses to address the dissatisfaction promptly. For practitioners, the findingssuggest that effective product management involves not only mitigating negative reviews but also actively leveraging positive consumer emotions and experiences to strengthen brand loyalty. Strategically, e-commerce businesses should adopt a dual approach—proactively managing negative feedback while amplifying positive narratives—to enhance customer satisfaction and trust. Academically, the research contributes by evidencing the interplay of emotional, semantic, and many other factors in review analysis, offering pathways for future studies to incorporate richer variables and advanced text mining techniques.

Treatment for alcohol use disorder (AUD) has the potential to improve health and quality of life. Little is known about disparities in AUD treatment utilization at the intersection of race and gender. We examined disparities in AUD treatment utilization among those diagnosed with AUD in a community sample, by race, ethnicity, and gender, and whether disparities varied by insurance. We also examined whether criminal legal history and socioeconomic status moderated disparities in treatment. We used data from the nationally representative 2017 to 2019 National Survey on Drug Use and Health, the most recent 3-year period available. The analytic sample included noninstitutionalized adults aged 18 to 64 who met criteria for past year AUD and identified as White, Black, or Latinx (n = 7782). We examined disparities in AUD treatment utilization by race, ethnicity, and gender subgroup and by insurance status, estimating weighted logistic regressions, and adjusting for indicators of clinical need in concordance with the Institute of Medicine definition of healthcare disparity. Only 5.4% of adults with AUD in the United States utilized AUD treatment in the past year. AUD treatment utilization did not significantly differ between White males and other racial, ethnic, and gender groups; however, we did identify disparities among Medicaid enrollees and those who were uninsured. Among Medicaid enrollees, Latinx females (3.2%) had lower treatment utilization than White males (9.3%, P < .05). Among uninsured individuals, Latinx males (1.8%) had lower treatment utilization than White males (6.2%, P < .05). AUD treatment utilization was extremely low among adults in the United States aged 18 to 64 who met criteria for AUD. Ethnic and gender disparities in treatment utilization were revealed when examining differences in AUD treatment utilization by insurance status. Strategies for improving access to AUD treatment that address structural barriers to care are needed and should consider targeted approaches for Medicaid enrollees and those uninsured.

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

Academic AbstractGender differences in sexuality are often attributed to evolved biological differences organized before adolescence or experiential ones learned afterward-neglecting learning that endures because it is evolutionarily expected, and we are biologically sensitized to it. Here, we present the Biodevelopmental Learning Opportunities and Outcomes Model (BLOOM) of gender differences in sexuality, arguing women's lower interest in sex originates not from unequal capacities to want/desire it, but unequal opportunities to like/enjoy sex when biospsychosocially primed to learn from it. We synthesize evidence indicating sex is least equal in adolescence, offering the greatest costs and fewest rewards to women/girls who debut with men/boys (WDM). Concomitantly, it is most teachable in adolescence, when a window of opportunity for sexual incentive learning may open, particularly among individuals with heightened sexual plasticity/learning aptitude (i.e., women/girls). Implications for distinguishing gender differences in sexuality from experience-contingent similarities, and realizing equal sexual rights, education, and health are discussed.Public AbstractGender differences in sexual enjoyment are among the largest in psychology and have remained so over decades despite other advances in gender equality. The gender gap in sexual pleasure, for example, has gained widespread attention and is increasingly discussed as an explanation for gender differences in sexuality. Here, we spotlight the largest, but least discussed, gender gap in sexual enjoyment: the developmental gap. We review evidence that adolescence is not simply a vulnerable period for sexual health, but a window of opportunity for learning to have healthy, enjoyable, and desirable sex-and one wherein equal opportunity is lacking. We propose women get the least equitable sex during this window, when they are primed to learn from it, and this learning informs sexual interest thereafter, generating acquired differences that are often mistaken for inborn ones. We close with recommendations for ensuring equal opportunities for healthy sex and sexual health across genders.

This study was carried out to examine the Islamic credit card within the context of Islamic economics in terms of scientific research produced in response to the introduction of Islamic credit cards. Descriptive bibliometric statistical analysis and sentiment analysis are applied to secondary data in the form of 134 papers published over the last nine years and then processed with VOSViewer and SentiStrength. The results demonstrated that bibliometric mapping is divided into two clusters, with cluster 1 containing 33 keywords and cluster 2 containing 26. The results of sentiment analysis of the literature indicate 34% positive sentiment, 25% negative sentiment, 41% neutral sentiment, and 0% high negative and high positive sentiment.

Background: Previous research has explored gender differences in visuo-spatial intelligence and gaze behaviour; however, no comprehensive review has synthesised findings across parameters or examined their underlying causes. Aim: This review critically evaluates literature on gender differences in gaze behaviour and visuo-spatial intelligence. It integrates findings from visual software (cognitive and perceptual processes) and visual hardware (biological and physiological capacities), identifies methodological inconsistencies, and examines sociocultural moderators influencing performance. A dual-framework approach is proposed to explain the interaction between these domains. Method: Electronic searches were carried out across multiple databases, including Current Contents, ScienceDirect, EBM Reviews, CISTI Source (1990–February 2025), Google Scholar, SportDiscus (1990–February 2025), PubMed (1990–February 2025), the Cochrane Database of Systematic Reviews, and various international e-catalogues. Results: Seventy-seven full-text articles were reviewed. Consistent gender differences were observed in both biological capacities and cognitive strategies, although methodological variations contributed to inconsistencies. Sociocultural factors such as play experience and training exposure were identified as influential moderators. Conclusion: Gender differences in visio-spatial skills reflect biological, cognitive and social influences. An interactionist framework is recommended for future research. Contribution: This review advances understanding by introducing the distinction between ‘visual hardware’ and ‘visual software’, critically evaluating eye-tracking research and proposing a more nuanced interactionist framework for future investigations.

In this longitudinal study, we examined whether certain types of stressful events and how individuals respond to these events would explain gender differences in depressive symptoms among adolescents. We hypothesized that certain stressful events would mediate the relationship between gender and depressive symptoms. We also hypothesized that individual differences in emotional reactivity would impact part of this relationship. Lastly, we examined whether gender differences in early childhood temperament might explain gender differences in emotional reactivity in adolescence. We examined these hypotheses in a sample of 315 adolescents (51% females; 93% Caucasian; 3% African-American; and 1% each Hispanic, Asian-American, and Native American) participating in a longitudinal study of child development since birth. We used multiple regression and constrained nonlinear regression to analyze the data. Results indicated that stressful events significantly mediated gender differences in depression, and that individual differences in emotional reactivity to these stressors significantly moderated the relationship between stress and depression. We also observed significant gender differences in emotional reactivity to these stressors; temperamental differences in withdrawal negativity in infancy were marginally significant in mediating gender differences in emotional reactivity to stress in adolescence.

This study aims to analyze the sentiment of the Hindu community toward religious issues on social media, with a specific focus on Kapuas Regency, Central Kalimantan. The development of social media as a digital public sphere has positioned platforms such as Instagram and Twitter as primary spaces for the spontaneous and open expression of opinions, perceptions, and religious attitudes. This study is important because religious issues in digital spaces often influence social harmony and shape religious discourse within society. The research employs a quantitative approach using text mining and sentiment analysis based on Natural Language Processing (NLP). Primary data were collected through web scraping techniques, utilizing Apify for Instagram and asynchronous Python scripts for Twitter, with relevant keywords, hashtags, and geographic indicators. The analysis process includes text preprocessing (cleaning, tokenization, stopword removal, and stemming), followed by sentiment classification using a lexicon-based approach with the InSet dictionary into three categories: positive, negative, and neutral. The analysis results were evaluated using a confusion matrix, along with precision, recall, and F1-score metrics to assess model reliability. The findings indicate that positive sentiment predominates on both Instagram and Twitter, followed by neutral sentiment, while negative sentiment appears in only a small proportion. Positive sentiment is generally associated with expressions of prayer, gratitude, tolerance, and calls for togetherness, whereas negative sentiment tends to emerge in discussions related to ritual differences or responses to socio-religious controversies. The sentiment analysis model achieved an accuracy of 100% on Instagram data (self-evaluation) and 74.4% on Twitter data (manual evaluation), with relatively high precision and recall values, indicating that the results are statistically reliable.

The estimation of class prevalence, i.e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc. For example, in sentiment analysis, the objective is often not to estimate whether a specific text conveys a positive or a negative sentiment, but rather estimate the overall distribution of positive and negative sentiments during an event window. A popular way of performing the above task, often dubbed quantification, is to use supervised learning to train a prevalence estimator from labeled data. Contemporary literature cites several performance measures used to measure the success of such prevalence estimators. In this paper we propose the first online stochastic algorithms for directly optimizing these quantification-specific performance measures. We also provide algorithms that optimize hybrid performance measures that seek to balance quantification and classification performance. Our algorithms present a significant advancement in the theory of multivariate optimization and we show, by a rigorous theoretical analysis, that they exhibit optimal convergence. We also report extensive experiments on benchmark and real data sets which demonstrate that our methods significantly outperform existing optimization techniques used for these performance measures.

Sentiment Analysis Applied to Digital Conversations Among Warm Autoimmune Hemolytic Anemia (wAIHA) Patients Receiving Rituximab and/or Blood Transfusion

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

BackgroundA key component of the initial public health response to the COVID-19 pandemic involved the use of mass media briefings led by public health officials to communicate updates during a time of great uncertainty and rapidly changing information. This study aims to examine the consistency of communications expressed during the public health briefings to generate novel insights about the type, direction, and strength of public health messages. The data source included 131 readily accessible public health briefings alongside the provincial and national new confirmed case counts during the first two waves of rapidly increasing cases during the pandemic in Alberta, Canada. We employed sentiment analysis as a text mining technique to explore the types and frequency of words in public health briefings conveying positive and negative sentiments. Using statistical analyses and data visualizations, we examined how public health messaging shifted with case trends.ResultsOur findings indicate consistent public health messaging in terms of sentiments regardless of case count fluctuations, an association of specific words with conveying positive and negative sentiments, and a focus on particular message patterns at different points during the first two waves of the COVID-19 pandemic.ConclusionOur findings demonstrate the practical implications and methodological advantages of using sentiment analysis as a data analytics tool for rapidly and objectively assessing the consistency of health communications during a public health crisis.