Social Trends Research Articles

Critical Evaluation of the Impact of Training and Development Programs on the Performance of Employees in the UK Food Retail Sector

This research is a quantitative evaluation of the impact of training and development programs on the performance of employees in the UK food retail industry. The study assesses the impact of various training programs in the UK food retail businesses on the performance of employees. In essence, the study evaluated the context of the training programs, the most effective training programs, the environmental specific factors that influence training, and the impact these training programs have on the performance of employees. The study utilized the simple random sampling method under probability sampling in order to give a chance to all persons in the target population. As such a questionnaire was designed with a five-point Likert scale and 384 participants responded to these questions which were administered online with Google forms. In analyzing the data, the descriptive analysis was done using the SPSS, and a PLS-SEM analysis was made using SmartPLS. The findings of the study revealed that coaching and mentoring have no statistically significant relationship with employee performance, contrary to initial hypotheses. However, E-training has a significant positive relationship with employee performance, aligning with existing literature. Conversely, on-the-job training showed a significant negative relationship with employee performance, suggesting potential issues with its effectiveness or perception among employees. The study also revealed that environmental specific factors like political stability, sustainability, technological advancement, legal compliance, social trends and economic fluctuations influences training programs which in turn affects performance of employees. The results of this study provide valuable knowledge to managements of food retail businesses in the United Kingdom, it also in practice identifies the various training programs effective for performance in the food retail sector.

Research on the Impact of Social Media on Consumer Shopping Behavior:Based on Case Analysis of the Adolescent Population

Starting from the underlying logic of the impact of social media on consumers, this article focuses on analyzing the shopping habits of young consumers and the influence and shaping of social media on their consumption behavior. It analyzes the positive and negative effects of social media and puts forward relevant opinions and suggestions. Compared to the adult consumer group, the adolescent consumer group has not yet formed a relatively fixed consumption value system, and their consumption behavior is more susceptible to various external factors. Adolescents are in a critical period of character development and self-value establishment, so they will pay attention to social trends and seek the unique value of their own existence during this stage. Social media, as a fast channel for transmitting information, is also the main communication platform for adolescents. It provides a huge amount of information, including product purchasing channels, consumer evaluations, etc. These pieces of information constantly influence adolescents’ thoughts and subtly shape their shopping behavior. This article advocates for the correct guidance and shaping of the values of young people, exercising and cultivating their independent thinking ability, to enable them to have healthy and good consumption behavior.

"Beezin": Analysis of TikTok Content Related to a Viral Social Media Trend.

To evaluate "beezin," a social media trend that involves applying Burt's Bees Lip Balm to the eyelids to induce intoxication, which has the potential to cause ocular problems. Researchers identified the top TikTok videos about beezin using the search terms "beezing," "beezin," "burtsbees," "Burt's Bees on eyelids," and "burtsbeeschallenge." Researchers watched each video and recorded key information. Descriptive statistics were calculated. Sixty top videos were included in the analysis; they had more than 158 million views and more than 11 million likes. More than half (n = 37, 61%) portrayed the trend positively, 11 (18%) portrayed the trend negatively, and 12 (20%) took a neutral stance on the trend. None of the videos were created by a health care provider. Beezin is a viral social media trend that may cause ocular problems. There is a need for factual TikTok content to combat the trend. Health care providers should consider beezin when adolescent patients present with contact dermatitis, conjunctivitis, or other possible sequelae of beezin. [J Pediatr Ophthalmol Strabismus. 20XX;XX(X):XXX-XXX.].

Features of Worldview Perception Among Speakers of Yakut and Russian: Associative Field Analysis of Stimulus Words DYOL — SCHASTYE

This article analyzes the associative fields of the stimulus words DYOL and SCHASTYE, which are central to the linguistic consciousness of Yakut and Russian speakers. Investigating the underlying processes within language enables us to uncover the ethnocultural specificity of a community’s linguistic consciousness — a snapshot of the real-world image at a particular period in the life of an ethnic group. The focus is on identifying both commonalities and distinctions in the perception of happiness among speakers of the two languages, as well as the ethnocultural nuances in their worldviews. A comparative analysis of the associative fields of the stimulus words DYOL — SCHASTYE is conducted using Y.N. Karaulov’s “semantic gestalt” methodology, which includes delineating semantic zones, determining types of connections between stimuli and responses, and employing a quantitative assessment method to measure the overlap of associative fields. The analysis reveals significant shifts in the value priorities of Yakut speakers in recent years. While happiness was previously associated with family, children, and material wealth, con-temporary perspectives increasingly emphasize aspects such as fulfilling work, travel, career advancement, and self-care. This reflects modern social trends where happiness is linked not only to material factors but also to personal growth and self-actualization.

Political-RAG: using generative AI to extract political information from media content

ABSTRACT In the digital era, media content is crucial for political analysis, providing valuable insights through news articles, social media posts, speeches, and reports. Natural Language Processing (NLP) has transformed Political Information Extraction (IE), automating tasks such as event extraction and sentiment analysis. Traditional NLP methods, while effective, are often task-specific and require specialized expertise. In contrast, Large Language Models (LLMs) powered by Generative Artificial Intelligence (GenAI) offer a more integrated solution. However, domain-specific challenges persist, which led to the development of the Retrieval-Augmented Generation (RAG) framework. RAG enhances LLMs by incorporating external data retrieval, addressing issues related to data availability. To demonstrate RAG’s capabilities, we introduce the Political-RAG system, designed to extract political event information from media content, including Twitter data and news articles. Initially developed for event extraction, the Political-RAG system lays the foundation for developing various complex Political IE tasks. These include detecting hate speech, analyzing conflicts, assessing political bias, and evaluating social trends, sentiment, and opinions.

Reduce, reinforce, and replenish: safeguarding the early-life microbiota to reduce intergenerational health disparities

Socioeconomic (SE) disparity and health inequity are closely intertwined and associated with cross-generational increases in the rates of multiple chronic non-communicable diseases (NCDs) in North America and beyond. Coinciding with this social trend is an observed loss of biodiversity within the community of colonizing microbes that live in and on our bodies. Researchers have rightfully pointed to the microbiota as a key modifiable factor with the potential to ease existing health inequities. Although a number of studies have connected the adult microbiome to socioeconomic determinants and health outcomes, few studies have investigated the role of the infant microbiome in perpetuating these outcomes across generations. It is an essential and important question as the infant microbiota is highly sensitive to external forces, and observed shifts during this critical window often portend long-term outcomes of health and disease. While this is often studied in the context of direct modulators, such as delivery mode, family size, antibiotic exposure, and breastfeeding, many of these factors are tied to underlying socioeconomic and/or cross-generational factors. Exploring cross-generational socioeconomic and health inequities through the lens of the infant microbiome may provide valuable avenues to break these intergenerational cycles. In this review, we will focus on the impact of social inequality in infant microbiome development and discuss the benefits of prioritizing and restoring early-life microbiota maturation for reducing intergenerational health disparities.

Corporate Governance: Exploring Key Concepts, Mechanisms, and Historical Evolution

Corporate governance involves several key concepts that help define its scope and purpose. Though review of key corporate governance concepts and historical evolution is widespread in the extant literature, detailed review across the globe is limited. Motivated by this dearth, this study reviewed these concepts and explored how firms rely on them to implement effective corporate governance. The study employed documentary and descriptive research methods to review concepts of corporate governance, systems, mechanisms and historical perspectives around the world. Our study discovered that understanding internal controls helps ensure compliance, minimize risks and safeguard the firm’s assets. Similarly, the study discovered that executive compensation structures help align management interests with shareholder value and external oversight through auditing and regulation which provides an unbiased assessment of the firm’s financial reports. Our historical perspective of corporate governance highlighted significant milestones that have shaped its evolution. Importantly, these include reports and acts aimed at addressing corporate scandals and improving governance practices. We also discovered that to improve corporate governance standards worldwide, multilateral organizations like the Organisation for Economic Cooperation and Development (OECD) among others have played a number of roles in developing international guidelines and codes of best practice. Further, the study explored the dynamic nature of corporate governance in response to evolving economic, social and technological trends. The study concluded that understanding the concepts, systems, mechanisms and historical perspectives of corporate governance is essential for firms and stakeholders to maintain transparency, accountability and sustainable performance.

A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets

Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model’s focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model’s consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model’s predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.

Female genital cosmetic surgery: a social trend driving surgical practice.

Female genital cosmetic surgery: a social trend driving surgical practice.

디지털 활용 역량에 관한 예비 영유아 교사의 은유 분석

Objectives The purpose of this study is to explore the perceptions of early childhood pre-service teachers regarding their digital utilization capabilities. Methods This study used a metaphor analysis method. metaphor analysis is suitable as a research method to explore individual beliefs and perceptions. for the study, we collected metaphors regarding the digital utilization capabilities of 107 early childhood pre-service teachers. and collected metaphors analyzed according to an 8-step metaphor analysis procedure. Results As a result of this study, early childhood teacher’s perception about digital utilization capabilities were characterized into three categories and nine subcategories. the three categories are: ‘digital utilization capabilities viewed from the perspective of learning and cognition’, ‘early childhood education field and digital utilization capabilities’, ‘Social trends and digital utilization capabilities’. the nine subcategories are as follows: ‘capabilities required for early childhood teachers’, ‘capabilities that are difficult and awkward for me’, ‘capabilities essential for modern and future society’, ‘capabilities that must be continuously learned’, ‘capabilities that are helpful for life and learning’, ‘Beneficial capabilities when utilized appropriately in early childhood education settings’, ‘capabilities that must be newly learned according to social changes’, ‘capabilities that feel the difficulties and questions of applying early childhood education’. Conclusions This study is valuable in that it suggests the direction of a program to digital utilization capabilities of early childhood pre-service teachers to be developed in the future.

A COMPUTATIONAL ANALYSIS OF IDEOLOGICAL POSITIONS, EMOTIONAL STANCE, AND SUPPORT FOR PRESIDENTIAL CANDIDATES IN TURKEY

Using artificial intelligence, this article explores the intricate dynamics between ideologies, emotions, and political preferences of the electorate in Turkey. Utilizing a dataset of one billion posts from X (formerly Twitter), the study maps out political opinions, focusing on support for presidential candidates, ideological stances, and collective emotions around the pivotal 2023 Turkish presidential elections. We discuss the limitations of conventional survey techniques and introduce an ERC‐funded Politus project that processes digital trace data to offer timely insights into social and political trends. The study's findings, particularly around the “prayer rug (seccade) crisis,” underscore the complexity of electoral politics and the potential of digital trace data in capturing the evolving sentiments and ideological orientations of voters. Through this computational approach, the research provides a granular depiction of Turkey's ideological map and electoral behavior, contributing significantly to the discourse on political analysis in the digital era.

A systematic review of the nexus between climate change and social media: present status, trends, and future challenges

Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.

Elements to Social Media Success: Analyzing Fashion Trends and Performance Factors on Instagram

Trends are the catapult to success for accounts on social media. And though many creators and businesses follow similar trends, only a select number reach their desired results. Previous research has only gone as far as explaining the significance of social media in the fashion industry, as well as the power of trends in the digital realm. This study evaluates 10 Instagram posts under the #sweatervest trend, categorizing them into successful and unsuccessful based on engagement metrics to assess which factors—relevance, interactiveness, use of familiar concepts, innovation, and entertainment—most contribute to performance. Findings conclude a generally positive relationship between each factor and the performance of the content. These elements together serve as a "formula" or "recipe" on how to make successful content when following a social media trend.

Social status, chaste widowhood, and trends in marital age gaps in post-Imjin Korea, 1606–1630

Social status, chaste widowhood, and trends in marital age gaps in post-Imjin Korea, 1606–1630

Unveiling Digital Mirrors: Decoding Gendered Body Poses in Instagram Imagery

Social media platforms have had a significant impact on people's everyday lives worldwide and imagery on social media has become a vital means of practicing views of the self and communicating them to others. Albeit a significant proportion of people frequently upload self-representations on social media, this source of information on individuals, groups, and societies remains under-examined within the social sciences and neighboring disciplines. In our study, we focus on gender-stereotypical body-posing in self-portraits on social media. While sociology has examined gender stereotypes for decades, research lacks empirical evidence on representations in digital contexts as well as novel forms of stereotyping. We present a scalable and transferable methodology for analyzing body poses in images on the social media platform Instagram by combining neural network pose detection with an unsupervised learning approach. Based on a clustering algorithm applied to gender-annotated imagery, we identify 150 body posing clusters. Our results reveal significant gender differences in 20 percent of clusters, many of which represent gender-stereotypical body poses addressed in sociological literature. Moreover, we can identify new stereotypical poses related to smartphone technology and social media trends. This study represents a novel approach to utilizing large-scale image data for social science research and contributes to a better understanding of the consolidation and reproduction of gender stereotypes in digital realms.

Eight Decades of Educational Assortative Mating: A Research Note

Recent social and economic trends in the United States, including increasing economic inequality, women's growing educational advantage, and the rise of online dating, have ambiguous implications for patterns of educational homogamy. In this research note, we examine changes in educational assortative mating in the United States over the last eight decades (1940 to 2020) using the U.S. decennial censuses and the American Community Survey, extending and expanding earlier work by Schwartz and Mare. We find that the rise in educational homogamy noted by Schwartz and Mare has not continued. Increases in educational homogamy stalled around 1990 and began reversing in the 2000s. We find a growing tendency for marriages to cross educational boundaries, but a college degree remains the strongest dividing line to intermarriage. A key trend explaining this new pattern is women's increasing tendency to marry men with less education than themselves. If not for this trend, homogamy would have continued increasing until the early 2010s. We also show substantial heterogeneity by race, ethnicity, and nativity and among same- versus different-sex couples.

AnxietyNet: social media trends with fuzzy logic trees

In the digital age, social media platforms have become integral parts of daily life, facilitating communication, information sharing, and community building on a global scale. However, alongside the benefits of connectivity, concerns regarding public anxiety within these virtual communities have emerged. This research paper delves into the phenomenon of public anxiety on social media and proposes a novel approach utilizing fuzzy tree logic to analyze and address this issue. Through a comprehensive review of literature, theoretical frameworks, and practical applications, this paper aims to provide insights into the complex dynamics of public anxiety and offer potential solutions leveraging machine learning techniques. The proposed methodology involves the development of a cascading model to dynamically compute individual anxiety scores within social media networks. By analyzing structural influences and user interactions, this model aims to provide a nuanced understanding of anxiety dynamics within online communities. Additionally, a probabilistic model is designed to measure anxiety scores of social network messages, utilizing a generalized user profile. The integration of fuzzy tree logic enables the construction of a tree structure to effectively compute anxiety scores, offering a more flexible and human-like decision-making process. Preliminary experiments demonstrate the effectiveness of the proposed approach in capturing and analyzing public anxiety on social media platforms. The fuzzy tree model shows promise in accurately assessing anxiety levels and identifying key factors contributing to social anxiety within online communities. Moreover, the integration of machine learning techniques enhances the scalability and adaptability of the model, allowing for real-time analysis and proactive intervention strategies. This research underscores the importance of addressing public anxiety on social media through innovative analytical frameworks. By leveraging fuzzy tree logic and machine learning techniques, we can gain deeper insights into the dynamics of anxiety within online communities and develop targeted interventions to support individuals experiencing distress. Collaborative efforts in this endeavor hold the potential to foster societal well-being and resilience in the digital age.

Research on Social Interaction Trends Between Tourists and Children in The Northern Mountain Tourist Areas

Tourism is currently very developed in Vietnam, opening up opportunities to promote culture, sceneries and lives in remote areas to domestic and foreign tourists. Tourists coming to the mountain tourist areas in Northern Vietnam, in addition of the desire to enjoy the majestic and poetic view of the place, also aim to interact with people, especially children, who are said to be innocent and happy. In this research article, the authors studied the current trend of social interactions between tourists and children in northern mountain tourist areas after reviewing the theory of social interaction. In addition, the article also provides assessments and solutions to promote positive trends and limit negative trends of these interactions for authorities and children as well as families and schools in this area

Chiefdom, Vassalage and Empire: The Political Structures of Arabia from the First CE to the Advent of Islam

No political system could ever evolve in isolation fr om the environment in which it emerged. Similarly, no particular system can be understood as stagnant. It is actually the culmination or an improved form of a long historical growth. Keeping in view this synopsis, Islamic political institutions could be placed in the time and space in which the salient political features and social trends of the Jahiliyah Arabia and the neighboring political cultures contributed to their foundation and evolution. In addition to the compact political structures of Aksum, Rome and Persia, and then Byzantine and Sassanid on the borders, internally the pre-Islamic Arabs had a diverse mechanism for managing their affairs. The Arabian South experienced organized governance as compared to the north and central regions. This diversity can be attributed to the geo-strategic location and ecological environment of the peninsula. The fertile South was more supportive to the growth of a political mechanism than the arid, geopolitically more vulnerable north and central regions. In a nutshell, a proper study of the less discussed Jahiliyah politics could minimize assumptions about the complete lawlessness and political unawareness of the Arabs in politics and governance.

관공서 스마트 오피스 구축에 관한 연구 : 사용자 조사 및 국내·외 사례조사 분석을 중심으로

In recent years, the social trend of emphasizing work-life balance (Work-Life Balance) and the introduction of the 52-hour workweek system have highlighted the need for smart offices focused on work efficiency. Public institutions are also actively pursuing the implementation of smart offices. This paper aims to provide guidelines for establishing smart offices in public institutions, based on field surveys and user studies of a public office currently undergoing relocation planning. Through a spatial usage survey of Public Office A, which is in the process of relocation planning, and user surveys and interviews with staff, this study identifies office space improvement needs and requirements. Furthermore, by analyzing domestic and international case studies of smart office implementations in public institutions, this paper offers practical directions for the establishment of smart offices in public institutions. Based on the surveys and interviews conducted, the study addresses the specific requirements of each department, ensures a clear understanding of and engagement with spatial needs, and incorporates insights from successful international examples. This paper highlights the importance of applying innovative spatial designs that can improve the current organizational culture, facilitating active communication and collaboration.