Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

The national museum of a country, as a cultural symbol of the nation, plays an important role in cultural communication at home and abroad. This study explores the online brand identity construction of two national museums—the British Museum and the National Museum of China—to inform cultural brands of the discursive strategies to distinguish themselves from others and communicate with their audiences effectively. Informed by multimodal critical discourse analysis, this paper analyzes the websites of the two museums and their social media posts, depicts their brand identity prisms, and evaluates the effectiveness of their online communication. The results show that both museums use multimodal and hypertextual resources to create unique and congruent brand images in website design and social media interaction with their target audiences, fulfilling the institutional functions of museums as the symbol of national culture or world civilization. They express differential personalities and cultural values to reinforce their brand identities in different sociocultural and political contexts. The findings may provide insight into the use of multimodality in online communication for cultural institutions to enhance their brand images and promote cultural exchanges.

Despite the pervasive nature of internet use among adolescents and young adults, there is not enough knowledge about whether and how involvement in social media influences substance use patterns and the risk of drug use-related problems. This study was conducted to examine the complex relationship between substance use-related social media engagement (viewing, liking, commenting, and posting the substance use-related social media content) and the drug use-related problem in adolescents from public perception. We surveyed to determine the perception of social media users regarding the association between substance use-related social media content and substance/drug abuse problems. An anonymous online questionnaire was conducted to collect the response from each participant. The response was generated after collecting the data from 126 users of mixed ages. The data was stringently analyzed, and the response was displayed in the form of bar charts. The primary findings indicated a significant relationship between drug/alcohol-related social media engagement and drug/alcohol-related problems. From public perception, a positive correlation was found between the engagement in the drug use-related content and drug use associated problems. However, further research is needed to determine the right direction of these associations that can provide substantiative solutions for numerous interventions aiming to prevent drug use-related adverse consequences.

Background: Electronic cigarette (e-cigarette) use and vaping tobacco have increased rapidly worldwide, raising concerns about their health effects, social acceptability, and regulatory challenges. In many countries, e-cigarettes are more commonly used by individuals from higher socioeconomic status (SES) backgrounds, who, in theory, should have greater knowledge about e-cigarettes and their associated risks. However, it remains unclear why a group with more knowledge about e-cigarette risks would also hold more positive attitudes toward vaping and exhibit higher usage rates — a phenomenon that may represent a knowledge-behavior paradox. Understanding this paradox, along with the complex relationships between e-cigarette knowledge, attitudes, and behaviors, is critical for informing effective public health interventions, campaigns, social media messaging, and regulatory policies. Objectives: This study aimed to evaluate the complex relationship between SES, e-cigarette knowledge, pro-vaping attitudes, and e-cigarette use. Methods: The SMOKES Study (Study of Measurement of Knowledge and Examination of Support for Tobacco Control Policies) used a multi-center, cross-sectional design, collecting data from 2,403 college and university students across 15 provinces in Iran (covering nearly half of the country's provinces). The survey measured family income, age, sex, ethnicity, e-cigarette use, knowledge, and attitudes. Structural Equation Modeling (SEM) was employed to examine the interrelations between SES, knowledge, attitudes, and behavior, while adjusting for age, sex, and ethnic minority status. Results: SEM analysis confirmed the hypothesized paradox. Although greater knowledge about e-cigarettes was linked to less favorable attitudes toward vaping and lower use, pro-vaping attitudes emerged as the strongest predictor of vaping behavior, while knowledge played a weaker protective role. Notably, individuals with higher SES simultaneously showed higher knowledge and, paradoxically, more pro-e-cigarette attitudes and greater usage. Female students and ethnic minority students reported higher correct knowledge and lower pro-vaping attitudes and use. Although age and higher family income were associated with more favorable attitudes, they did not directly predict vaping behavior. These results suggest that for higher SES individuals, poor knowledge is not the main driver of e-cigarette use; rather, their pro-e-cigarette attitudes, which seem to outweigh the influence of knowledge, play a key role. Conclusions: Although individuals from higher SES backgrounds report greater correct knowledge about e-cigarettes, this knowledge does not necessarily translate into reduced positive attitudes or lower usage. This study highlights the complexity of these paradoxical effects and suggests that public health strategies need to go beyond simple education and knowledge-based interventions. Targeted approaches should address industry messaging, challenge misconceptions, and strengthen regulatory efforts to reduce e-cigarette use among young adults, including those from higher SES backgrounds.

Background: Recent research has identified structural racism—systemic policies and practices that perpetuate racial inequalities—as a significant social determinant of population health. Studies utilizing data from the Adolescent Brain Cognitive Development (ABCD) study have shown an association between higher levels of state-level structural racism and increased tobacco use among youth in the United States. However, there has been limited exploration of the psychosocial mediators of this relationship, particularly in the context of youth aged 10-16 years. Objective: This study aimed to assess the roles of socioeconomic status (SES), tobacco susceptibility, and perceived discrimination as potential mediators in the relationship between state-level structural racism and youth tobacco initiation rates. Methods: We analyzed data from the ABCD study, a nationally representative longitudinal survey of 11,698 youth followed from ages 9/10 to 15/16. These data were combined with state-level indicators of structural racism. We employed structural equation modeling (SEM) to investigate the mediators of the association between structural racism and self-reported initiation of tobacco use, while controlling for individual and state-level covariates. Results: Our findings indicate that higher levels of structural racism were associated with increased rates of tobacco initiation among youth. This relationship was partially mediated by lower SES, but not by perceived discrimination or tobacco susceptibility. Conclusion: The association between structural racism and youth tobacco initiation appears to be influenced in part by the lower SES prevalent in states with higher levels of racism. These results highlight the need for addressing both racism and SES inequalities as key strategies for reducing tobacco disparities among youth.

This study is aimed at examining the prevalence of premarital sex among adolescents between 13-19 years within Chorkor, Korle Gonno and Mamprobi communities in the Accra Metropolis. The study with a sample size of 268 used a descriptive approach and a combination of convenient and quota sampling methods. The study recommends the establishment of a school guidance and counselling unit to sensitize and advise students and parental monitoring of adolescent lives and the use of social media on mobile devices.

The world of the stock market is an intricately complex financial ecosystem that demands years of dedicated study to comprehend fully. It relies on risk mitigation practices and fundamental theoretical techniques to engage in speculation regarding stock and cryptocurrency fluctuations. However, this realm is progressively becoming more inclusive, with accessibility expanding beyond traditional educational barriers. Technological advancements, coupled with the ease of entry into this domain and the information-disseminating power of social networks, contribute to a rising number of individuals participating in this financial movement. What makes this evolution disruptive is that the same tools facilitating accessibility also exert influence on the way market trends unfold. This paper delves into the escalating impact of social media within the financial sphere, emphasizing the heightened accessibility to information and market involvement facilitated by platforms like Twitter and Reddit. It sheds light on how social media plays a pivotal role in market manipulation, as evidenced by phenomena such as the r/wallstreetbets subreddit, where meme-based strategies were employed to inflate the prices of stocks like GameStop. The study explores the utilization of social media by influential figures, exemplified by Elon Musk, who leverage their platforms to sway market movements. Additionally, this paper addresses instances of misinformation, such as the confusion surrounding Virgin Galactic's shares following a SpaceX failure and the introduction of "AGUA" in the Mexican stock market, leading to widespread misunderstandings. The paper extends its examination to the effects of social media on cryptocurrencies, highlighting how comments from public figures can significantly impact the prices of Bitcoin and Dogecoin. Overall, it underscores the imperative need for adaptation to these changes in the digital financial paradigm.

Quantitatively, the study adopted a descriptive research design. The population of this study comprised two thousand (2000) students in the four (4) senior high schools (Edinaman Senior High, Eguafo Senior High, Peter Hold Book Senior High and Komenda Senior Technical Institute) in Komenda Edina Eguafo Abirem municipality. Purposive, simple random and stratified sampling techniques were used to select two hundred students from the four for this study. A questionnaire was the main instrument for data collection. There are more adverse effects of social media network participation on academic performance than positive effects. Social media network sites serve as a useful medium for enhancing students’ academic performance if properly used. Therefore, SHS students should be guided to use social media properly to enhance their academic performance. It is recommended that regular counselling by school authorities and parents for students who participate in social media networks should be done to prevent improper use of social media and avoid addiction and its consequences. It is also recommended that teachers should encourage students to use the right grammar and correct spelling of words when participating in social networks to help stop the negative effect it has on students’ academic performance. It is once again recommended that all stakeholders should be involved in educating students on the proper use of social media networks for their academic work as well as the dangers of improper use on their academic performance and social well-being.

With numerous unique physical, mental, psychological, and emotional challenges associated with being a university-level student-athlete, it can be assumed that student-athletes may be at great risk of mental health conditions such as burnout. Burnout results from the interaction of three different elements: physical or emotional exhaustion, sport devaluation, and reduced athletic accomplishment. This study sought to answer the following research questions: (1) Why do student-athletes experience burnout in their sport?, and (2) What can be done to avoid burnout? using standard qualitative interviewing techniques of a Division I Women’s Soccer coach. There were two prominent themes answering research question 1: Perfection and Performative Social Media and It’s a Job and Coach is the Boss. There were three prominent themes identified answering research question 2: Parental Influence, De-Professionalize Amateur Sport, and Value Athletes’ Time. Where this work differs from other qualitative studies regarding athlete burnout, is that it explores the perceptions and opinions of a coach. The coach interviewed for this study clearly expressed concerns for his student-athletes and their holistic well-being. In addition to a continuation of scholarship through the student-athletes lens, more research conducted exploring the perspectives of coaches, parents, and institutional administrators in pursuit of solutions to mitigating the effect of burnout on student-athletes is of value.

A survey on food choices with a randomized sample population of individuals using various social media in Nigeria was conducted during the COVID1-19 pandemic. The data generated was subjected to basic standard statistical analysis. The parameters indicated that 94% of the population is young adults, 58.9 % percent are city dwellers, 63.6% are students, 23.4 % are into business, 86.9% are graduates; 73.8% consume various diets, 23.4% are vegetarians and only 2.8% fed only on proteins, 30.8% of them go on two meals per day. The most choices on influence on food purchases decision are hunger (26.2%), mood (26.2%), past experience (45.8%), quality of the food products (66.7%), cost of the food products (50.5%) and government approval (28%). Also,other most preferred choices are for self-prepared food (40.21%), enhanced local diets (36 %), and a blend of foreign and local diets purchases (24%). Other highest choices include: easy preparation (37.4%), shelf life (29%); cute packaging (23.4%), swelling property preference (20.6%), minimal cooking time and energy preference (37.4%). The weighted sum, index and rank on factors influencing food choices showed that the influence of quality of food product ranked highest, followed by influence on cost. Also preference for enhanced local healthy diets to foreign ranked highest, minimal cooking time and energy costs ranked highest. These nutritional adaptations have implications to individuals, food scientists, manufacturers in the food industry, food regulatory agencies, government and other decision bodies.

Online shopping is a growing phenomenon and social media namely Facebook has radically changed consumers online purchase behaviour; especially the interactive information system of consumers, peers, and vendors. Similar to other online purchasing products or services, e-cigarette marketing in Facebook has potential. This study aims to determine the association between individual’s online interaction and e-cigarette purchase intention from Facebook vape groups. A cross-sectional study was conducted among 214 respondents using the Facebook platform from September to December 2019. Data were collected via a self-administered questionnaire. The Chi-square test was used for data analyses using SPSS (version 25.0). Most of the participants were male; the mean age of the respondents was 27.41 ± 2.1. Total 93.9% (201 of 214) and 56.1% (120 of 214) respondents were vaper and smoker respectively. Among the non-smokers, most of the respondents were vaper (84 of 94, 89.4%). Consumers smoking and vaping status were associated with each other (χ2 = 6.11, P=0.01). There was an association between vaping status and purchase intention of e-cigarettes from the Facebook vape group (χ2 = 5.06, P=0.02). Online interactions, namely like (χ2 = 5.88, P <0.01), post (χ2 = 7.16, P <0.01) and sharing (χ2 = 10.14, P <0.01) of the e-cigarette related information or advertisements in social media were associated with purchase intention of e-cigarettes from Facebook vape group. This study revealed individuals vaping and online interactions were associated with an online purchase the intention of e-cigarette in Facebook. Public health researchers and policymakers might contemplate the regulatory environment of online marketing of e-cigarettes to curtail the uptake of e-cigarettes among non-smokers.

The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is done through a bag of words method, and 80-20 data is split using training and testing. The experimental findings demonstrate that the LSTM model outperforms the conventional models, such as SVM and Naïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of 99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, it performs well the model consumes heavy computational resources and longer training time. In summary, the results show that deep learning performs well in sentiment analysis and can be used to social media monitoring, customer feedback evaluation, market sentiment analysis, etc.