Search

BackgroundDuring the COVID pandemic, residency program's social media presence increased to aid in residency recruitment by attempting to increase engagement and readily available information for applicants across specialties. However, little information exists on what characteristics and content on obstetrics and gynecology (OBGYN) residency program accounts attract more followers or engagement.ObjectivesTo identify social media trends in OBGYN residencies and determine which aspects of programs influence the number of followers and interaction with content posted.MethodsWe performed a retrospective review of ACGME accredited OBGYN programs and determined their presence on Instagram and X in the fall of 2021. Content from the thirty programs with the most followers was analyzed independently by two authors. Multivariate analysis and a linear mixed model were used to characterize and evaluate content on Instagram and X.ResultsMost programs utilized Instagram (88.5%, N = 262/296) and were managed solely by residents (84.4%, N = 108/128). Number of followers on Instagram positively correlated with features such as program size, Instagram profile duration, and Doximity rankings (p < 0.0x01). Programs on X had more followers if their profile had a longer duration, followed more individuals, or were ranked higher on Doximity. The most posted Instagram content was biographical and social in nature. Instagram posts with the highest engagement were awards and/or the Match.ConclusionsUnderstanding what social media content attracts more followers and increases engagement is crucial as it likely impacts OBGYN resident recruitment. Professional groups should establish guidelines for social media use in recruitment for the protection of both residents and applicants.

Short texts on social platforms often suffer from insufficient emotional semantic expressions, sparse features, and polysemy. To enhance the accuracy achieved by sentiment analysis for short texts, this paper proposes an emoji-based multifeature fusion sentiment analysis model (EMFSA). The model mines the sentiments of emojis, topics, and text features. Initially, a pretraining method for feature extraction is employed to enhance the semantic expressions of emotions in text by extracting contextual semantic information from emojis. Following this, a sentiment- and emoji-masked language model is designed to prioritize the masking of emojis and words with implicit sentiments, focusing on learning the emotional semantics contained in text. Additionally, we proposed a multifeature fusion method based on a cross-attention mechanism by determining the importance of each word in a text from a topic perspective. Next, this method is integrated with the original semantic information of emojis and the enhanced text features, attaining improved sentiment representation accuracy for short texts. Comparative experiments conducted with the state-of-the-art baseline methods on three public datasets demonstrate that the proposed model achieves accuracy improvements of 2.3%, 10.9%, and 2.7%, respectively, validating its effectiveness. [ABSTRACT FROM AUTHOR]

Background: Kidney function assessment is crucial in critical illness patients and is required before administering renally excreted medication, especially antibiotics and antiepileptics. Conventional clinical practice often focuses on renal impairment with low creatinine clearance (CrCl) and overlooks the augmented renal clearance (ARC), which is defined by (CrCl) more than 130 ml/min. This typical demonstration neglects individuals who experience hyperfunctioning kidneys. Among critically ill patients, the prevalence of (ARC) is approximately 20% to 65% of cases. This study aims to evaluate physicians' and clinical pharmacists' knowledge about ARC-associated risk factors, antibiotic regimen modification in ARC patients, and attitudes towards ARC workshops and guidelines in Al-Ain, UAE. Methods: A cross-sectional, online self-administered survey-based study was designed to achieve this study's aim. The questionnaire was constructed on profound literature analysis, validated, and piloted. The survey was emailed to physicians and pharmacists working in two hospitals, private and governmental, and distributed through different social media platforms over three months, December 2022—February 2023. Results: Of the 92 complete responses (32 clinical pharmacists, 60 physicians), 57 (61.9%), were aware of ARC, but 72 (78%) demonstrated poor knowledge overall. Clinical pharmacists had a higher mean rank of knowledge than the physician's group. Meanwhile, 70 (76.1%) participants were unaware of the eGFR threshold to determine ARC. There is a noticeable positive attitude toward seeking more information about antibiotic dose adjustment in ARC patients at 85 (92%) of the respondents. Remarkably, only 28 (30.4%) were directly involved with ARC patients' treatment plans. Conclusion: In conclusion, clinical pharmacists showed better knowledge than physicians. However, overall, the participating healthcare providers lacked knowledge about ARC, so a reliable source of information regarding ARC should be utilized. Future research could explore the implementation of professional development workshops for healthcare providers and national guidelines and then assess their impact on patient outcomes. [ABSTRACT FROM AUTHOR]

This study investigates the determinants of the obsessive view of mukbang autonomous sensory meridian response (Mukbang ASMR) and examines the moderation role of intertemporal choice and the mediating effect of mediated voyeurism among university students in Malaysia. A quantitative survey was conducted with 408 university students in Malaysia who viewed mukbang channel(s) often on social media. PLS-SEM is adopted to examine the associated paths and effects. The results demonstrate the significant impact of alienation and novelty, vicarious satisfaction, companionship and loneliness on mukbang ASMR obsession. Mediated voyeurism intervenes the effects of alienation and novelty on mukbang ASMR and intertemporal choice positively moderates the relationship between companionship and loneliness and mukbang ASMR. This study constructs a model to estimate Mukbang ASMR obsession by identifying specific motives and relationships among key factors, highlighting loneliness as the most effective determinant of mukbang ASMR among Malaysian younger generation. Research results provide an extended understanding of the mukbang ASMR, offering valuable insights in the areas of lifestyle, social well-being, and social media consumption. [ABSTRACT FROM AUTHOR]

Introduction: Children in boarding schools spend most of their time without their parents or caregivers, causing concerns about the suitability of such schools for children with asthma. This study assessed individuals' opinions regarding the suitability of boarding secondary schools for children with asthma. Methods: A qualitative design was adopted for this study using a focus group discussion held on a social media platform (WhatsApp®) of the Asthma Awareness and Care Group (AACG), The group comprised 150 registered members. The study was guided by a structured protocol and based on a vignette comprising three questions. Data were analysed via thematic analysis using framework principles. Results: Out of the 150 eligible members, there were responses from only 19 participants. Majority of the respondents were aged ≤ 30 years (n = 17, 89.5%). The three main themes generated from the thematic analysis include the appropriateness of boarding schools for children with asthma; facilities necessary for boarding schools to cater to children with asthma; and outright rejection of children with asthma by boarding schools. Respondents conceptualised the appropriateness of boarding schools for students with asthma in six distinct sub-themes: asthma severity and extent of control, child's self-efficacy and assertiveness, child equipped with tools (knowledge, inhalers, and asthma control diary), school awareness, facilities, and active support, availability of a guardian, and the knowledge and perception of teachers and schoolmates about asthma. The sub-themes associated with the themes were presented, alongside exemplar quotes from respondents. The majority of the respondents (61.5%) were in support of allowing children with asthma attend boarding schools but with some caveats such as without liability to the school, if facilities are unavailable. Conclusion: Children's age, autonomy, asthma management status, and the school's readiness were identified as important considerations for the safe attendance of children with asthma at boarding schools. [ABSTRACT FROM AUTHOR]

Immigration is one of the most salient topics in public debate. Social media heavily influences opinions on immigration, often sparking polarized debates and offline tensions. Studying 220,870 immigration-related tweets in the UK, we assessed the extent of polarization, key content creators and disseminators, and the speed of content dissemination. We identify a high degree of online polarization between pro and anti-immigration communities. We found that the anti-migration community is small but denser and more active than the pro-immigration community with the top 1% of users responsible for over 23% of anti-immigration tweets and 21% of retweets. We also discovered that anti-immigration content spreads also 1.66 times faster than pro-immigration messages and bots have minimal impact on content dissemination. Our findings suggest that identifying and tracking highly active users could curb anti-immigration sentiment, potentially easing social polarization and shaping broader societal attitudes toward migration. [ABSTRACT FROM AUTHOR]

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases. [ABSTRACT FROM AUTHOR]

This study assesses whether references to the ancient past in debates about political issues on social media over-represent negative and extreme views. Using precision-recall, we test the performance of three sentiment analysis methods (VADER, TextBlob and Flair Sentiment) on a corpus of 1,478,483 posts, comments and replies published on Brexit-themed Facebook pages between 2015 and 2017. Drawing on the results of VADER and manual coding, we demonstrate that: 1) texts not containing keywords relating to the Iron Age, Roman and medieval (IARM) past are mostly neutral and 2) texts with IARM keywords express more negative and extreme sentiment than those without keywords. Our findings show that mentions of the ancient past in political discourse on multi-sided issues on social media are likely to indicate the presence of hostile and polarised opinions. [ABSTRACT FROM AUTHOR]

Background: Rosemary (Rosmarinus officinalis) has a rich historical use for various reasons due to its beneficial characteristics including stimulating hair growth, providing antioxidant and antibacterial effects, and functioning as a skin conditioner and fragrance enhancer. The plant is cultivated in Jordan and using it is gaining popularity among the population. Aim of the study: To assess the knowledge, attitude and perception of rosemary use among Jordanians. Materials and methods: A cross-sectional study was conducted targeting at least 385 participants via social media platforms. Face and content validity of the questionnaire was performed by independent researchers. The questionnaire was divided into three main sections including the demographics section, the knowledge section and the attitude toward rosemary use. Statistical analysis was conducted using SPSS including descriptive statistics, chi-square tests, and multiple linear regression. Results: The study's participants (n = 407) had a mean age of 30.98 years old (SD = 12.76). The mean knowledge score for the participants ranged from -5 to 7, with a mean of 2.96 (SD = 2.61), with younger participants exhibiting higher knowledge scores regarding rosemary. Approximately half of the participants (48.2%) reported using rosemary for medical purposes. About three-quarters reported using rosemary oil, but a higher percentage reported using the leaves. Around three quarters administered it orally or applied it topically. Most participants obtained rosemary by growing it at home or from herbalists. Family and friends were the main influencers for rosemary users. Most of the study participants strongly agreed/agreed that plants possess a healing power (94.4%). The most reported reason for using rosemary was improving the hair condition (82.1%) followed by gastrointestinal problems (67.9%). Conclusions: The study highlights the widespread use of rosemary for different reasons through different methods of application. Addressing misconceptions and enhancing knowledge dissemination may allow informed decision-making and promote the safe and effective use of herbal therapies. [ABSTRACT FROM AUTHOR]

The study uses 31 semi-structured interviews to explore users' motivations for adopting and using blockchain-based social media (BSM) platforms. The objective of the study is twofold—to collect empirical data on early adopters of BSM and to test the applicability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model for explaining why some users are choosing BSM over mainstream social media (MSM) platforms. Manual content analysis of the interviews reveals that users are initially drawn to BSM due to social influence and financial incentives, but they continue to use it mainly because of the sense of community they experience. We also find that the steep learning curve, the absence of content moderation, as well as security and privacy concerns hinder the widespread adoption of these platforms. From the theoretical side, although the UTAUT model is generally suitable for examining why individuals use BSM, we suggest integrating two additional factors into the model: financial incentives and content moderation. [ABSTRACT FROM AUTHOR]

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective. [ABSTRACT FROM AUTHOR]

Raising young children has always been hard, but evidence suggests that it may be getting harder. The isolation of the pandemic, the pressures to fulfill exacting parenting standards, and the explosion of "expert" parenting advice on social media have fueled the rise of "gentle parenting," an approach that pivots away from older, discipline-heavy parenting typologies and which promises the development of happier, healthier children. Despite the popularity of gentle parenting, it has received no empirical scrutiny. The current study represents the first systematic investigation of what gentle parenting entails. Data were gathered from a sample (N = 100) of parents of at least one child between the ages of 2 and 7 from the Midwest, Southeast, and Southwest. Approximately half (n = 49) of the sample identified as "gentle parents." Inductive analyses identified this approach as one that emphasizes high levels of parental affection and parents' and children's emotion regulation. Gentle parenting appears to be distinct from other established measures of parenting approaches in its emphasis on boundaries, yet the enactment of those boundaries is not uniform. Overall, gentle parents reported high levels of parenting satisfaction and efficacy, but a subset of gentle parents who were highly critical of themselves reported significantly lower levels of efficacy than the rest of the sample. Statements of parenting uncertainty and burnout were present in over one-third of the gentle parent sample. Implications are discussed for future research and increased support for those who identify as gentle parents. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the impact of digital finance on the breakthrough innovation of enterprises with a sample of A-share listed companies in Shanghai and Shenzhen from 2011 to 2022. It is found that digital finance can promote corporate breakthrough innovation, and presents certain structural heterogeneity characteristics. The mechanism test shows that digital finance has the dual attributes of a financing platform and a social platform, which can promote breakthrough innovation by alleviating corporate financing constraints and expanding corporate social networks. Heterogeneity analysis reveals that the role of digital finance in promoting breakthrough innovation is characterized by regional heterogeneity, with digital finance playing a greater role in promoting breakthrough innovation in provinces with a low level of development of the banking sector, provinces with a high level of development of the capital market sector, and the central region. In addition, the degree of firms' external financing dependence and the degree of product market competition can strengthen the positive effect of digital finance on firms' breakthrough innovation. This paper enriches the related research on the impact of digital finance on enterprise innovation, and provides theoretical basis and policy insights on how digital finance can better assist the innovation-driven development strategy. [ABSTRACT FROM AUTHOR]

Introduction: Ehlers-Danlos syndromes (EDS) is an umbrella term for a group of hereditary connective tissue disorders usually featuring hyperextensible skin, easy bruising, poor healing, and joint hypermobility. According to international Ehlers-Danlos support groups, the average time to diagnosis of this rare genetic condition is 10–12 years. Consequently, the journey to diagnosis can potentially be traumatic. This pilot study aims to explore female patients' journey to a diagnosis of EDS while living in Australia. Materials and methods: Over six weeks, from January to February 2023, a survey was distributed on EDS social media platforms including Facebook and LinkedIn. Ethical approval was obtained through the Ulster University Institute of Nursing and Health Research Ethics Filter Committee (FCNUR). Thematic and statistical analysis of the anonymous data was completed during March 2023. Results: 152 women completed the survey. More than half of the respondents first noticed symptoms of EDS more than 15 years prior to diagnosis and more than three quarters of respondents received other diagnoses prior to their EDS diagnosis. Most respondents saw their general practitioner, a physiotherapist and/or a rheumatologist prior to being correctly diagnosed with the condition. While some respondents had positive experiences with these health professionals, many felt that they were not listened to and, after doing their own research, had to educate their health professional/s about EDS. Conclusion: This pilot survey demonstrated that the journey to diagnosis of EDS for women in Australia is frequently long and traumatic. Participants indicated that more EDS education and training is needed for health professionals, especially GPs, to improve the diagnostic process. Implications for practice: Participants called for their self-reported symptoms to be listened to by health professionals and to be met with empathy and an open mind instead of being dismissed. Additional education and training to health professionals about connective tissue disorders including EDS may aid earlier diagnosis in Australia. [ABSTRACT FROM AUTHOR]

Background: According to the Seventh National Census, China's fertility rate is less than 1.5, marking a significant national issue with potential risks. To counter this low birth rate, the Chinese government has relaxed family planning policies and introduced supportive measures. Purpose: Changes in birth policy have attracted considerable attention from the people of China. This article aims to study the public's response to the three-child support policy using Weibo as a window. The goal is to provide a more balanced evaluation of current perspectives, enabling policymakers to formulate better fertility information, particularly when anticipating a poor public response to controversial policies. Methodology: This research uses a crawler to gather data from Sina Weibo. Through opinion mining of Weibo posts on the three-child policy, Weibo users' online opinions on the three-child policy are analyzed from two perspectives: their attention content and sentiment tendency. Using an interrupted time series, it examines changes in online views on the policy, matching policy documents to the time nodes of Weibo posts. Findings: The public has shown great interest in and provided short-term positive feedback on policies related to improving maternity insurance, birth rewards, and housing subsidies. In contrast, there has been a continuous negative response to policies such as extending maternity leave, which has particularly sparked concerns among women regarding future employment and marital rights protection. On social media, the public's attention to the three-child birth policy has focused mainly on the protection of women's rights, especially legal rights after childbirth, and issues related to physical and mental health. Child-rearing support and economic pressure are also hot topics, involving the daily expenses of multichild families, childcare services, and housing pressure. However, this study also revealed that infertile or single women express a strong desire to have children, but due to limitations in the personal medical insurance system, this desire has not been fully satisfied. Contributions: Our study demonstrates the feasibility of a rapid and flexible method for evaluating the public response to various three-child supportive policies in China using near real-time social media data. This information can help policy makers anticipate public responses to future pandemic three-child policies and ensure that adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed but controversial, restrictions. [ABSTRACT FROM AUTHOR]

The increased usage of social media in organizational settings has raised questions about its effect on job performance. This study explains the interplay between social media usage, transactive memory capability, employee creativity, and job performance within the Chinese organizational context. The rationale for this study stems from the need to understand how social media usage can enhance job performance through cognitive and creative processes within teams. On the basis of earlier literature, the survey questionnaire was designed to collect data and the survey included validated scales to measure these constructs. The data is collected from 816 employees in China via online resources like social media and emails and then Structural Equation Modeling is used for empirical analysis through AMOS software. The findings show the positive impact of social media on job performance while transactive memory capability and employee creativity mediates this relationship. These findings contribute to a better understanding of how social media can be leveraged to improve job performance. Practical implications highlight the importance of fostering transactive memory capability through social media platforms, facilitating employee creativity, and ultimately, bolstering job performance. Additionally, this study underscores the need for organizations to strategically harness the potential of social media as a catalyst for innovation and knowledge sharing, thus optimizing workforce performance in the digital era. [ABSTRACT FROM AUTHOR]

The Gender Inequality Index is a country-level measure of gender inequality based on women's levels of reproductive health, social and political empowerment, and labor-market representation. In two studies, we tested the validity of the GII-S, a state-level measure of gender inequality in the USA. In Study 1, the GII-S was associated with objective and subjective measures of wellness among women, including life satisfaction, financial well-being, and perceptions of safety. GII-S was not associated with the Gini coefficient, a well-established measure of economic inequality, suggesting that gender and economic disparities represent distinct aspects of social inequality. Study 2 tested the link between GII-S scores and collective action—specifically, participation in the #MeToo movement promoting awareness of sexual harassment and violence against women. Analysis of geo-localized messages on the Twitter social media platform reveals that higher GII-S scores were associated with fewer tweets containing the #MeToo hashtag. Moreover, GII-S was associated with state-level political orientation: the more conservative a state, the higher its level of gender inequality. Results are discussed in terms of possible socio-cognitive processes underpinning the association between gender inequality and sensitivity to violence against women. [ABSTRACT FROM AUTHOR]

Technological developments over the past few decades have changed the way people communicate, with platforms like social media and blogs becoming vital channels for international conversation. Even though hate speech is vigorously suppressed on social media, it is still a concern that needs to be constantly recognized and observed. The Arabic language poses particular difficulties in the detection of hate speech, despite the considerable efforts made in this area for English-language social media content. Arabic calls for particular consideration when it comes to hate speech detection because of its many dialects and linguistic nuances. Another degree of complication is added by the widespread practice of "code-mixing," in which users merge various languages smoothly. Recognizing this research vacuum, the study aims to close it by examining how well machine learning models containing variation features can detect hate speech, especially when it comes to Arabic tweets featuring code-mixing. Therefore, the objective of this study is to assess and compare the effectiveness of different features and machine learning models for hate speech detection on Arabic hate speech and code-mixing hate speech datasets. To achieve the objectives, the methodology used includes data collection, data pre-processing, feature extraction, the construction of classification models, and the evaluation of the constructed classification models. The findings from the analysis revealed that the TF-IDF feature, when employed with the SGD model, attained the highest accuracy, reaching 98.21%. Subsequently, these results were contrasted with outcomes from three existing studies, and the proposed method outperformed them, underscoring the significance of the proposed method. Consequently, our study carries practical implications and serves as a foundational exploration in the realm of automated hate speech detection in text. [ABSTRACT FROM AUTHOR]

This study explores the intricate relationship between social media usage and the mental health of young individuals by leveraging the insights of 492 UK school headteachers. It adopts a novel multidisciplinary approach, integrating perspectives from psychology, sociology, education studies, political science, philosophy, media studies, linguistics, social work, anthropology, and health sciences. The application of thematic analysis, powered by ChatGPT-4, identifies a predominantly negative perspective on the impact of social media on young people, focusing on key themes across various disciplines, including mental health, identity formation, social interaction and comparison, bullying, digital literacy, and governance policies. These findings culminated in the development of the five-factor Comprehensive Digital Influence Model, suggesting five key themes (Self-Identity and Perception Formation, Social Interaction Skills and Peer Communication, Mental and Emotional Well-Being, Digital Literacy, Critical Thinking, and Information Perception, and Governance, Policy, and Cultural Influence in Digital Spaces) to focus the impacts of social media on young peoples' mental health across primary and secondary educational stages. This study not only advances academic discourse across multiple disciplines but also provides practical insights for educators, policymakers, and mental health professionals, seeking to navigate the challenges and opportunities presented by social media in the digital era. [ABSTRACT FROM AUTHOR]

The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources and slowing inference times, making deployment difficult in resource-limited environments. In this paper, we process both structured and unstructured data, determining the polarity of text using the TextBlob scheme to determine the sentiment of news headlines. We propose a Stochastic Gradient Descent (SGD)-based Ridge classifier (RC) for blending SGDR with an advanced string processing technique to effectively classify news articles. Additionally, we explore existing supervised and unsupervised ML algorithms to gauge the effectiveness of our SGDR classifier. The scalability and generalization capability of SGD and L2 regularization techniques in RCs to handle overfitting and balance bias and variance provide the proposed SGDR with better classification capability. Experimental results highlight that our string processing pipeline significantly boosts the performance of all ML models. Notably, our ensemble SGDR classifier surpasses all state-of-the-art ML algorithms, achieving an impressive 98.12% accuracy. McNemar's significance tests reveal that our SGDR classifier achieves a 1% significance level improvement over K-Nearest Neighbor, Decision Tree, and AdaBoost and a 5% significance level improvement over other algorithms. These findings underscore the superior proficiency of linear models in news categorization compared to tree-based and nonlinear counterparts. This study contributes valuable insights into the efficacy of the proposed methodology, elucidating its potential for news categorization and sentiment analysis. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to revolutionize various domains by automating language-driven tasks. This study evaluates the effectiveness of an AI-assisted methodology, called the "POP Title AI Five-Step Optimization Method," in optimizing content titles on the RED social media platform. By leveraging advancements in natural language generation, this methodology aims to enhance the impact of titles by incorporating emotional sophistication and cultural proficiency, addressing existing gaps in AI capabilities. The methodology entails training generative models using human-authored examples that align with the aspirations of the target audience. By incorporating popular keywords derived from user searches, the relevance and discoverability of titles are enhanced. Audience-centric filtering is subsequently employed to further refine the generated outputs. Furthermore, human oversight is introduced to provide essential intuition that AI systems alone may lack. A total of one thousand titles, generated by AI, underwent linguistic and engagement analyses. Qualitatively, 65% of the titles exhibited intrigue and conveyed meaning comparable to those generated by humans. However, attaining full emotional sophistication remained a challenge. Quantitatively, titles emphasizing curiosity and contrast demonstrated positive correlations with user interactions, thus validating the efficacy of these techniques. Consequently, the machine-generated titles achieved coherence on par with 65% of human-generated titles, signifying significant progress and potential for further refinement. Nevertheless, achieving socio-cultural awareness is vital to match human understanding across diverse contexts, thus presenting a critical avenue for future improvement in the methodology. Continuous advancements in AI can enhance adaptability and reduce subjectivity by promoting flexibility instead of relying solely on manual reviews. As AI gains a deeper understanding of humanity, opportunities for its application across various industries through experiential reasoning abilities emerge. This case study exemplifies the nurturing of AI's potential by refining its skills through an evolutionary process. [ABSTRACT FROM AUTHOR]

Background: The likelihood of COVID-19 spreading from one individual to another is impacted by personal factors, preventive actions taken, and the quantity and length of exposure. Social media instantly shares health information with the public so medical professionals can interact with them. Methods: The review used Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist. We retrieve articles by using keywords on Pub Med, Cochrane Library, and grey literatures. The Joanna Briggs Institute (JBI) Meta-Analysis of Statistics Assessment and Review Instrument was used to check the quality of articles. The main qualitative synthesis was including comments related to the characteristics of the studied populations, the study period, and the main results obtained from the social media platforms. Result: This study includes 32 studies conducted in 20 countries globally. Most of the studies discussed that during the pandemics different types of social medias were utilized to provide knowledge to change the attitude of the people and helps to practices COVID-19 preventive behaviors. By enabling people to seek and share knowledge, socialize, and find pleasure, social media use encourages people to adopt preventive behaviors. This review demonstrated a substantial relationship between higher levels of precautionary behaviors and factors such as educational status, knowledge, fear, and reading medical pages on social media. According to the studies, watching videos is a reliable means to get accurate information, and watching animated films posted on social media can help people learn more about COVID-19 preventive strategies. Conclusion: In these urgent times, social media could even help with quick information availability; misinformation or inadequate understanding can cause misunderstandings within the community. This analysis revealed that following medical pages on social media, having knowledge, feeling afraid, and having an educational background were all strongly correlated with greater levels of aware preventive behavior. Therefore, it is advised for policy makers to develop social behavioral change health communication strategies, guidelines on COVID-19 prevention behaviors. Health care providers should prepare health learning materials, and provide accurate, updated and timely information using all forms of social media to correct mis- information, misperceptions, depression, and anxiety for better control of the pandemics. Future research shall focus on evaluating effectiveness of each social media platform interventions during such emergency crisis. [ABSTRACT FROM AUTHOR]

Social media platforms serve as communication tools where users freely share information regardless of its accuracy. Propaganda on these platforms refers to the dissemination of biased or deceptive information aimed at influencing public opinion, encompassing various forms such as political campaigns, fake news, and conspiracy theories. This study introduces a Hybrid Feature Engineering Approach for Propaganda Identification (HAPI), designed to detect propaganda in text-based content like news articles and social media posts. HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). Hybrid feature engineering entails the amalgamation of various features, including Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), Sentimental features, and tweet length, among others. Multiple Machine Learning classifiers undergo training and evaluation utilizing the proposed methodology, leveraging a selection of 40 pertinent features identified through the hybrid feature selection technique. All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. The SVM-based HaPi (SVM-HaPi) exhibits superior performance among traditional algorithms, achieving precision, recall, F-Measure, and overall accuracy of 0.69, 0.69, 0.69, and 69.2%, respectively. Furthermore, the proposed approach is compared to well-known existing approaches where it overperformed most of the studies on several evaluation metrics. This research contributes to the development of a comprehensive system tailored for propaganda identification in textual content. Nonetheless, the purview of propaganda detection transcends textual data alone. Deep learning algorithms like Artificial Neural Networks (ANN) offer the capability to manage multimodal data, incorporating text, images, audio, and video, thereby considering not only the content itself but also its presentation and contextual nuances during dissemination. [ABSTRACT FROM AUTHOR]

In March 2020, the outbreak of COVID-19 precipitated one of the most significant stock market downturns in recent history. This paper explores the relationship between public sentiment related to COVID-19 and stock market fluctuations during the different phases of the pandemic. Utilizing natural language processing and sentiment analysis, we examine Twitter data for pandemic-related keywords to assess whether these sentiments can predict changes in stock market trends. Our analysis extends to additional datasets: one annotated by market experts to integrate professional financial sentiment with market dynamics, and another comprising long-term social media sentiment data to observe changes in public sentiment from the pandemic phase to the endemic phase. Our findings indicate a strong correlation between the sentiments expressed on social media and market volatility, particularly sentiments directly associated with stocks. These insights validate the effectiveness of our Sentiment(S)-LSTM model, which helps to understand the evolving dynamics between public sentiment and stock market trends from 2020 through 2023, as the situation shifts from pandemic to endemic and approaches new normalcy. [ABSTRACT FROM AUTHOR]

Cryptocurrency is a digital asset secured by cryptography that has become a popular medium of exchange and investment known for its anonymous transactions, unregulated markets, and volatile prices. Given the popular subculture of traders it has created, and its implications for financial markets and monetary policy, scholars have recently begun to examine the political, psychological, and social characteristics of cryptocurrency investors. A review of the existing literature suggests that cryptocurrency owners may possess higher-than-average levels of nonnormative psychological traits and exhibit a range of non-mainstream political identities. However, this extant literature typically employs small nonrepresentative samples of respondents and examines only a small number of independent variables in each given study. This presents the opportunity for both further testing of previous findings as well as broader exploratory analyses including more expansive descriptive investigations of cryptocurrency owners. To that end, we polled 2,001 American adults in 2022 to examine the associations between cryptocurrency ownership and individual level political, psychological, and social characteristics. Analyses revealed that 30% of our sample have owned some form of cryptocurrency and that these individuals exhibit a diversity of political allegiances and identities. We also found that crypto ownership was associated with belief in conspiracy theories, "dark" personality characteristics (e.g., the "Dark Tetrad" of narcissism, Machiavellianism, psychopathy, and sadism), and more frequent use of alternative and fringe social media platforms. When examining a more comprehensive multivariate model, the variables that most strongly predict cryptocurrency ownership are being male, relying on alternative/fringe social media as one's primary news source, argumentativeness, and an aversion to authoritarianism. These findings highlight numerous avenues for future research into the people who buy and trade cryptocurrencies and speak to broader global trends in anti-establishment attitudes and nonnormative behaviors. [ABSTRACT FROM AUTHOR]

Highlighting minorities and crime survivors through public discourse is essential for their support and protection. However, advocating for minorities is challenging due to the fear of potential isolation from one's social circles. This reluctance contributes to the societal phenomenon known as the "spiral of silence," significantly impeding efforts to support socially vulnerable individuals. This study centers on a pivotal instance where the silence surrounding sexual abuse in the Japanese entertainment industry was disrupted, in which the late company president had allegedly abused idol trainees of the company for decades. Utilizing extensive data from news media and social media, the study probes the engagement dynamics of public attention to this scandal. Results indicate that users on social media provided earlier and greater coverage for this scandal compared to news media outlets. Furthermore, television demonstrated a significant delay in addressing this issue compared to other news media, such as tabloids, magazines, and online news. Regarding social media engagement, idol fans exhibited a more subdued response to the issue compared to the general public. Notably, fans more loyal to the company tended to be slower to mention the issue, with a higher likelihood of standing in defense of the perpetrators. Moreover, conflicting attitudes were observed within the fan communities, culminating in an observable "echo chamber" phenomenon. This paper presents a novel examination of the process of disruption of social silence and offers critical insights for aiding vulnerable individuals in environments dominated by an unacknowledged spiral of silence. This study is novel in that it suggests a reinterpretation of the "spiral of silence theory" in the age of social media, through a comprehensive analysis of relevant social media data and news media data. This contributes to the body of research that has analyzed the spiral of silence theory online. [ABSTRACT FROM AUTHOR]

False political information–misinformation or disinformation—is widely spread on social media. Individual social media users play a large part in this. However, only a minority actively share false material. It is important to establish what sets these individuals apart from those who do not, and why they do it. Motivations for sharing may vary and are likely to differ between people who share false material unknowingly and on purpose. In this paper we consider the extent to which individual differences in personality and other variables, and motivations for sharing, are associated with the likelihood of people sharing false political information both accidentally and deliberately. In a series of four studies (Ns = 614, 563, 627, 113) we examined predictors of sharing false political information using different methodological approaches. Across the four studies, a key finding was that positive schizotypy is associated with measures of sharing false information both accidentally and deliberately. Motivations for sharing political information online were also relevant, with sharing for reasons of 'raising awareness' appearing particularly important. Implications for research and practice are discussed. [ABSTRACT FROM AUTHOR]

THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. Different types of abusive content such as offensive language, hate speech, aggression, etc. have become prevalent in social media and many efforts have been dedicated to automatically detect this phenomenon in different resource-rich languages such as English. This is mainly due to the comparative lack of annotated data related to offensive language in low-resource languages, especially the ones spoken in Asian countries. To reduce the vulnerability among social media users from these regions, it is crucial to address the problem of offensive language in such low-resource languages. Hence, we present a new corpus of Persian offensive language consisting of 6,000 out of 520,000 randomly sampled micro-blog posts from X (Twitter) to deal with offensive language detection in Persian as a low-resource language in this area. We introduce a method for creating the corpus and annotating it according to the annotation practices of recent efforts for some benchmark datasets in other languages which results in categorizing offensive language and the target of offense as well. We perform extensive experiments with three classifiers in different levels of annotation with a number of classical Machine Learning (ML), Deep learning (DL), and transformer-based neural networks including monolingual and multilingual pre-trained language models. Furthermore, we propose an ensemble model integrating the aforementioned models to boost the performance of our offensive language detection task. Initial results on single models indicate that SVM trained on character or word n-grams are the best performing models accompanying monolingual transformer-based pre-trained language model ParsBERT in identifying offensive vs non-offensive content, targeted vs untargeted offense, and offensive towards individual or group. In addition, the stacking ensemble model outperforms the single models by a substantial margin, obtaining 5% respective macro F1-score improvement for three levels of annotation. [ABSTRACT FROM AUTHOR]

Introduction: Recruitment of care home staff to research studies is recognised as challenging. This was further exacerbated by the COVID-19 pandemic and the associated negative media portrayal of care home workers. Social media use has surged since the onset of COVID-19 lockdowns, offering a plausible approach to understanding the barriers to care home research recruitment and gaining insight into public perceptions of care home workers. Aim: To utilise comments from two Facebook recruitment posts to: 1) gain an understanding of potential barriers to recruitment of healthcare workers (HCWs) in UK care homes, and 2) explore public sentiment towards care home research and care homes in the context of the COVID-19 pandemic. Methods: This cross-sectional study analysed comments from two Facebook posts (available June-October 2021) advertising a separate study on psychological support for care staff during the pandemic. This study was situated within a larger investigation into the mental health and wellbeing of care home staff and employed both qualitative analysis and quantitative methods (word count and correlations between words used and between posts). Results: Three themes were identified from the qualitative analysis: support, mistrust and blame. There was a greater use of words associated with support and negative emotive words in post 2. Post 2 comments featured significantly more choice words and first-person singular pronouns than post 1 which indicated a resentful sentiment from those who advocate freedom of choice and control. Discussion of mistrust towards researchers was most prominent in post 1 indicating the importance of relationship building between researchers and HCWs in UK care homes. With attribution to blame, there was a larger range of negative emotion words than positive emotion words. Discussion and conclusion: Taken together our findings offer novel insights into why recruitment to care home research during the pandemic including the use of social media might be problematic. Social media is a useful tool for recruitment but should not be considered as a one-time input. Researchers should pro-actively engage with the study population from the start using co-design with resident and public groups to support recruitment and ensure these populations are accurately represented within research. [ABSTRACT FROM AUTHOR]

Touch offers important non-verbal possibilities for socioaffective communication. Yet most digital communications lack capabilities regarding exchanging affective tactile messages (tactile emoticons). Additionally, previous studies on tactile emoticons have not capitalised on knowledge about the affective effects of certain mechanoreceptors in the human skin, e.g., the C-Tactile (CT) system. Here, we examined whether gentle manual stroking delivered in velocities known to optimally activate the CT system (defined as 'tactile emoticons'), during lab-simulated social media communications could convey increased feelings of social support and other prosocial intentions compared to (1) either stroking touch at CT sub-optimal velocities, or (2) standard visual emoticons. Participants (N = 36) felt more social intent with CT-optimal compared to sub-optimal velocities, or visual emoticons. In a second, preregistered study (N = 52), we investigated whether combining visual emoticons with tactile emoticons, this time delivered at CT-optimal velocities by a soft robotic device, could enhance the perception of prosocial intentions and affect participants' physiological measures (e.g., skin conductance rate) in comparison to visual emoticons alone. Visuotactile emoticons conveyed more social intent overall and in anxious participants affected physiological measures more than visual emoticons. The results suggest that emotional social media communications can be meaningfully enhanced by tactile emoticons. [ABSTRACT FROM AUTHOR]

Research interest in information sharing behavior on social media has significantly increased over the past decade. However, empirical studies on the relationship between Big Five personality traits and information sharing behavior have yielded contradictory conclusions. We aimed to investigate how Big Five personality influences information sharing behavior on social media. This meta-analysis systematically reviewed high-quality studies indexed by web of science and CNKI from the past decade (n = 27, with 31969 samples) and performed a meta-analysis to examine the association between Big Five personality traits and information sharing behavior. The literature search was performed in September 2023. The meta-analysis results showed that extraversion (β = 0.05**) had a positive relationship with information sharing behavior on social media. Agreeableness (β = −0.06**), conscientiousness (β = −0.03**), and neuroticism (β = −0.03**) had negative relationships with information sharing behavior on social media. However, the relationship between openness and information sharing behavior was not clearly observed due to insufficient research. The meta-analysis results are made available to the scientific community to enhance research, comprehension, and utilization of social media. [ABSTRACT FROM AUTHOR]

The proliferation of misinformation on social media platforms has given rise to growing demands for effective intervention strategies that increase sharing discernment (i.e. increase the difference in the probability of sharing true posts relative to the probability of sharing false posts). One suggested method is to encourage users to deliberate on the veracity of the information prior to sharing. However, this strategy is undermined by individuals' propensity to share posts they acknowledge as false. In our study, across three experiments, in a simulated social media environment, participants were shown social media posts and asked whether they wished to share them and, sometimes, whether they believed the posts to be truthful. We observe that requiring users to verify their belief in a news post's truthfulness before sharing it markedly curtails the dissemination of false information. Thus, requiring self-certification increased sharing discernment. Importantly, requiring self-certification didn't hinder users from sharing content they genuinely believed to be true because participants were allowed to share any posts that they indicated were true. We propose self-certification as a method that substantially curbs the spread of misleading content on social media without infringing upon the principle of free speech. [ABSTRACT FROM AUTHOR]

Social media has become an increasingly important way to seek and share experiences, support, knowledge, and advice during the COVID-19 pandemic. Reddit, a pseudonymous social media platform, was one way that young people interacted during the pandemic. Our study goals were two-fold: (1) to categorize information sought and provided by users of r/saplings, a subreddit devoted to cannabis use and is often used by young people, and (2) to examine if conversations changed during the COVID-19 pandemic. We extracted 213 randomly selected posts and 2,546 related comments across four time periods (before the pandemic, during the first wave, summer, and next fall). We assessed the volume of posts and comments throughout our study period and conducted a qualitative content analysis. Quantitatively, the findings demonstrated an increase in the number of posts and comments throughout the study period. Given the substantial growth in subreddit activity throughout our study period, Reddit may play an increasingly important role in youth socialization related to cannabis. From the content analysis, we identified three major themes: how to acquire cannabis, how to use cannabis, and associated consequences. Reddit-users prioritized certain content in their posts at different stages of the pandemic. 'Places to acquire' and 'future use' were most common at the beginning of the pandemic, while the theme of 'consequences' and the topic of 'tolerance' became more prominent during the summer months. The comments to these posts were generally thorough and responsive to the post. Nearly all the information came from opinions or personal experiences. Firstly, our findings suggest that young people viewed Reddit as a viable outlet for conversations about cannabis. Secondly, due to the nature of the peer comments and lack of verifiable information being exchanged, misinformation may still circulate and inadvertently worsen the efforts to reduce cannabis-related harm. Interventions that provide understandable and accurate cannabis-related information in accessible formats may increase young people's ability to access and practice harm reduction. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources. [ABSTRACT FROM AUTHOR]

The Urdu language is spoken and written on different social media platforms like Twitter, WhatsApp, Facebook, and YouTube. However, due to the lack of Urdu Language Processing (ULP) libraries, it is quite challenging to identify threats from textual and sequential data on the social media provided in Urdu. Therefore, it is required to preprocess the Urdu data as efficiently as English by creating different stemming and data cleaning libraries for Urdu data. Different lexical and machine learning-based techniques are introduced in the literature, but all of these are limited to the unavailability of online Urdu vocabulary. This research has introduced Urdu language vocabulary, including a stop words list and a stemming dictionary to preprocess Urdu data as efficiently as English. This reduced the input size of the Urdu language sentences and removed redundant and noisy information. Finally, a deep sequential model based on Long Short-Term Memory (LSTM) units is trained on the efficiently preprocessed, evaluated, and tested. Our proposed methodology resulted in good prediction performance, i.e., an accuracy of 82%, which is greater than the existing methods. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to assess the confidence, attitude, and scientific research practices of undergraduate students of different health profession specialties. Methods: In this cross-sectional study, an online-based questionnaire was distributed as a Google Form via groups and pages of medical universities available on social media sites such as Facebook, WhatsApp, and Twitter to the second- to sixth-year students of different health profession specialties in different universities across the United Arab Emirates (UAE) in the period from October through December 2023 using the convenience sampling technique. The questionnaire included four parts that assessed socio-demographics and custom-designed research-related questions (6 items), perceived confidence (8 items), attitudes (14 items), and the practice in the context of scientific research and its implementation (9 items). Multivariate logistic regression analyses were conducted to explore the variables associated with the study outcomes, including confidence, attitudes, and practice levels. Results: The study included 522 undergraduate students. The participants reported low confidence, a negative attitude, and low scientific research practice. Regression results revealed that individuals without prior research experiences were less likely to have high confidence and practice compared to those with previous research experience (OR = 0.634, 95% CI: 0.426–0.945, p = 0.025; and OR = 0.139, 95%Cl: 0.090–0.216, P<0.001, respectively). Additionally, participants who reported difficulty in differentiating between various literature resources were less likely to have high confidence and practice compared to those who reported the ability to differentiate (OR = 0.627, 95% CI: 0.42–0.935, p = 0.022, and OR = 0.370, 95%Cl: 0.237–0.579, p<0.001, respectively). Furthermore, individuals who had not taught research methods in their undergraduate studies were less likely to have high practice (OR = 0.505, 95%Cl: 0.309–0.823, p = 0.06). Conclusions: Undergraduates of different medical specialties in the UAE demonstrated acceptable levels of confidence and attitude toward scientific research, with several areas for practice improvement. Education and training courses focusing on various aspects of scientific research should be incorporated into the medical curricula in order to enhance students' confidence and practice of scientific research. [ABSTRACT FROM AUTHOR]

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users. [ABSTRACT FROM AUTHOR]

Social media platform's lack of control over its content made way to the fundamental problem of misinformation. As users struggle with determining the truth, social media platforms should strive to empower users to make more accurate credibility judgements. A good starting point is a more accurate perception of the credibility of the message's source. Two pre-registered online experiments (N = 525;N = 590) were conducted to investigate how verified source information affects perceptions of Tweets (study 1) and generic social media posts (study 2). In both studies, participants reviewed posts by an unknown author and rated source and message credibility, as well as likelihood of sharing. Posts varied by the information provided about the account holder: (1) none, (2) the popular method of verified source identity, or (3) verified credential of the account holder (e.g., employer, role), a novel approach. The credential was either relevant to the content of the post or not. Study 1 presented the credential as a badge, whereas study 2 included the credential as both a badge and a signature. During an initial intuitive response, the effects of these cues were generally unpredictable. Yet, after explanation how to interpret the different source cues, two prevalent reasoning errors surfaced. First, participants conflated source authenticity and message credibility. Second, messages from sources with a verified credential were perceived as more credible, regardless of whether this credential was context relevant (i.e., virtual lab coat effect). These reasoning errors are particularly concerning in the context of misinformation. In sum, credential verification as tested in this paper seems ineffective in empowering users to make more accurate credibility judgements. Yet, future research could investigate alternative implementations of this promising technology. [ABSTRACT FROM AUTHOR]

Since the conception of social media, research on political communication has pointed toward the risk that the social media environment can foster political echo chambers. However, this has recently been contradicted by some studies demonstrating "cross-cutting discussions" on social media. The current study extends this literature by particularly focusing on communication on political vlogger videos and having mainstream news outlet videos as a reference point. Specifically, this study addresses five points: (1) to what extent cross-partisan comments occupy conservative and liberal vloggers' comment threads and if there is a significant difference between the two, (2) the possibility that comments from vlogger videos can be utilized to predict the political leanings of comments on mainstream news outlet videos, (3) if the proportion of cross-cutting discussions on mainstream news outlet videos significantly varies by the news outlet's political leaning, (4) if a neutral news outlet channel can work as a venue for cross-cutting discussions, and (5) if the proportion of cross-cutting comments in mainstream news outlet comment threads is significantly different from that in vlogger comment threads. Both manual and computational analyses were employed; the political leanings of vlogger comments were analyzed by manual content analysis, and based on the results, the political leanings of mainstream news outlet comments were analyzed by NLP classifiers using three different algorithms—logistic regression, SVM, and random forest. As a result, we found that the proportion of cross-cutting discussions significantly varies by both the channel's political leaning and media type. In addition, our results suggest the possibility of neutral news outlets as a place for cross-cutting discussions. [ABSTRACT FROM AUTHOR]

Introduction: Individuals with type 1 diabetes (T1D) experience a complex set of alterations to skeletal muscle metabolic, neuromuscular, and vascular health; collectively referred to as diabetic myopathy. While the full scope of diabetic myopathy is still being elucidated, evidence suggests that even when individuals with T1D are physically active, indices of myopathy still exist. As such, there is a question if adherence to current physical activity guidelines elicits improvements in skeletal muscle health indices similarly between individuals with and without T1D. The objectives of this trial are to: 1) compare baseline differences in skeletal muscle health between adults with and without T1D, 2) examine the association between participation in a home-based exercise program, detraining, and retraining, with changes in skeletal muscle health, and 3) examine the roles of age and sex on these associations. Methods and analysis: This will be a prospective interventional trial. Younger (18–30 years) and older (45–65 years) males and females with T1D and matched individuals without T1D will engage in a four-phase, 18-week study sequentially consisting of a one-week lead-in period, 12-week exercise training program, one-week detraining period, and four-week retraining period. The exercise program will consist of aerobic and resistance exercise based on current guidelines set by Diabetes Canada. Metabolic, neuromuscular, and vascular outcome measures will be assessed four times: at baseline, post-exercise program, post-detraining, and post-retraining. Differences in baseline metrics between those with and without T1D will be examined with independent sample t-tests, and with two-way analyses of variance for age- and sex-stratified analyses. Changes across the duration of the study will be examined using mixed-model analyses. Dissemination: Findings from this research will be shared locally and internationally with research participants, clinicians, diabetes educators, and patient advocacy organizations via in-person presentations, social media, and scientific fora. Trial registration number: NCT05740514. [ABSTRACT FROM AUTHOR]

Objectives: The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the mechanisms underpinning associations between social media and smartphone use and self-harm in a clinical youth sample. We present here a comprehensive description of the cohort from baseline data and an overview of data available from baseline and follow-up assessments. Methods: Young people aged 13–25 years were recruited from a mental health trust in England and followed up for 6 months. Self-report data was collected at baseline and monthly during follow-up and linked with electronic health records (EHR) and user-generated data. Findings: A total of 362 young people enrolled and provided baseline questionnaire data. Most participants had a history of self-harm according to clinical (n = 295, 81.5%) and broader definitions (n = 296, 81.8%). At baseline, there were high levels of current moderate/severe anxiety (n = 244; 67.4%), depression (n = 255; 70.4%) and sleep disturbance (n = 171; 47.2%). Over half used social media and smartphones after midnight on weekdays (n = 197, 54.4%; n = 215, 59.4%) and weekends (n = 241, 66.6%; n = 263, 72.7%), and half met the cut-off for problematic smartphone use (n = 177; 48.9%). Of the cohort, we have questionnaire data at month 6 from 230 (63.5%), EHR data from 345 (95.3%), social media data from 110 (30.4%) and smartphone data from 48 (13.3%). Conclusion: The 3S-YP study is the first prospective study with a clinical youth sample, for whom to investigate the impact of digital technology on youth mental health using novel data linkages. Baseline findings indicate self-harm, anxiety, depression, sleep disturbance and digital technology overuse are prevalent among clinical youth. Future analyses will explore associations between outcomes and exposures over time and compare self-report with user-generated data in this cohort. [ABSTRACT FROM AUTHOR]

Introduction: Hyperinflation is a common procedure to clear secretion, increase lung compliance and enhance oxygenation in mechanically ventilated patients. Hyperinflation can be provided as manual hyperinflation (MHI) or ventilator hyperinflation (VHI), where outcomes depend upon the methods of application. Hence it is crucial to assess the application of techniques employed in Sri Lanka due to observed variations from recommended practices. Objective: This study is aimed to evaluate the application and parameters used for MHI and VHI by physiotherapists in intensive care units (ICUs) in Sri Lanka. Methodology: An online survey was conducted among physiotherapists who are working in ICUs in Sri Lanka using WhatsApp groups and other social media platforms. Results: A total of 96 physiotherapists responded. The survey comprised of three sections to obtain information about socio-demographic data, MHI practices and VHI practices. Most of the respondents (47%) worked in general hospitals and 74% of participants had a bachelor's degree in physiotherapy; 31.3% had 3–6 years of experience; 93.8% used hyperinflation, and 78.9% used MHI. MHI was performed routinely and as needed to treat low oxygen levels, abnormal breath sounds, and per physician orders while avoiding contraindications. Self-inflation bags are frequently used for MHI (40.6%). Only a few participants (26%) used a manometer or tracked PIP. In addition to the supine position, some participants (37.5%) used the side-lying position. Most physiotherapists followed the recommended MHI technique: slow squeeze (57.3%), inspiratory pause (45.8%), and quick release (70.8%). VHI was practised by 19.8%, with medical approval and it was frequently performed by medical staff compared to physiotherapists. Treatment time, number of breaths, and patient positioning varied, and parameters were not well-defined. Conclusion: The study found that MHI was not applied with the recommended PIP, and VHI parameters were not identified. The study indicates a need to educate physiotherapists about current VHI and MHI practice guidelines. [ABSTRACT FROM AUTHOR]

ChatGPT has demonstrated impressive abilities and impacted various aspects of human society since its creation, gaining widespread attention from different social spheres. This study aims to comprehensively assess public perception of ChatGPT on Reddit. The dataset was collected via Reddit, a social media platform, and includes 23,733 posts and comments related to ChatGPT. Firstly, to examine public attitudes, this study conducts content analysis utilizing topic modeling with the Latent Dirichlet Allocation (LDA) algorithm to extract pertinent topics. Furthermore, sentiment analysis categorizes user posts and comments as positive, negative, or neutral using Textblob and Vader in natural language processing. The result of topic modeling shows that seven topics regarding ChatGPT are identified, which can be grouped into three themes: user perception, technical methods, and impacts on society. Results from the sentiment analysis show that 61.6% of the posts and comments hold favorable opinions on ChatGPT. They emphasize ChatGPT's ability to prompt and engage in natural conversations with users, without relying on complex natural language processing. It provides suggestions for ChatGPT developers to enhance its usability design and functionality. Meanwhile, stakeholders, including users, should comprehend the advantages and disadvantages of ChatGPT in human society to promote ethical and regulated implementation of the system. [ABSTRACT FROM AUTHOR]

Introduction: Reversing the upward trajectory of obesity requires responding by including the multiple influences on weight control. Research has focused on individual behaviours, overlooking the environments where individuals spend their lives and shape lifestyles. Thus, there is a need for lay understandings of the impact of environments as a cause and solution to obesity. This research aimed to understand the influence of environments on the adoption of health practices in adults with obesity and to identify lay strategies with which to address environmental barriers to behaviour change. Methods: Nineteen adults with a history of obesity living in the United Kingdom were interviewed through video conferencing between May 2020 and March 2021. Semi-structured interviews and socio-demographic questionnaires were used, and data analysed through hermeneutic phenomenology informed reflexive thematic analysis. Results: Three main themes were created: living with convenience and normalcy: the increased accessibility of unhealthy food, people interacting with digital media for positive practice change, and the need to prioritise prevention in schools, the National Health Service and the food industry. Conclusions: The food environment was the major barrier, while interactions with social media was the most important opportunity to adopt healthy practices. The National Health Service was considered an obesogenic environment, something relevant since it has been traditionally recognised as an obesity management system. The perceptions from individuals with a history of obesity provide new suggestions on the influence of previously overlooked environments to design more adequate and effective interventions and policies that consider, more than in the past, the environments where people spend their lives. [ABSTRACT FROM AUTHOR]

Brain fog is a phenomenon that is frequently reported by persons with chronic pain. Difficulties with cognition including memory impairments, attentional issues, and cloudiness are commonly described. The current medical literature demonstrates a similar cloudiness: there is no clear taxonomy or nomenclature, no well-validated evaluations and a dearth of effective interventions. To focus our understanding of this complex phenomenon, we will perform a discourse analysis to explore how brain fog is described in public posts on social media. Discursive methodology will generate insights regarding the societal understanding and meanings attributed to brain fog, by sampling perspectives of persons with lived experience, currently underrepresented in the medical literature. It is anticipated that the results of the proposed study will 1) help healthcare professionals better understand the experience of chronic pain-related brain fog and 2) generate hypotheses for future research. To conclude, by incorporating innovative and contemporary methods, this proposed discourse analysis of social media sources will generate nuanced insights, bridging the gap between researchers, health care providers, and persons with lived experience. [ABSTRACT FROM AUTHOR]

Introduction: The Electronic Nicotine Delivery Systems (ENDS) industry recently introduced non-tobacco nicotine (NTN), which is not tobacco-derived and is often marketed as "tobacco-free nicotine." Given its novelty, it is important to understand where young adults learn about NTN ENDS. This study examined sources of exposure to NTN ENDS and relationships with NTN ENDS use and susceptibility. Methods: We analyzed online survey data collected in Fall 2021 from 642 young adults (18–25 years) who had heard of NTN ENDS. We assessed 9 sources of NTN ENDS exposure (e.g., retail stores, social media) and examined associations between sources of exposure and NTN current (past-month) use, lifetime (non-current) use, and susceptibility to use, adjusting for demographics and other tobacco product use. Results: Participants reported current NTN ENDS use (37.4%), lifetime use (12.0%), susceptibility (18.5%), or no susceptibility to use (32.1%). The most common sources of NTN ENDS exposure were retail stores (87.7%) and social media (81.0%). Exposure to NTN ENDS via social media was associated with greater odds of current NTN ENDS use (vs. no susceptibility) (aOR = 1.83, 95%CI: 1.02–3.28). Exposure via online streaming platforms was associated with greater odds of current (aOR = 1.75, 95%CI: 1.08–2.82) and lifetime NTN ENDS use (aOR = 2.42, 95%CI: 1.25–4.68). Conclusions: Young adults were exposed to and learned about NTN ENDS from diverse sources, primarily retail shops and social media. Further, exposure via social media and streaming platforms were associated with NTN ENDS use. Future studies should explore the content of NTN information from various sources to inform prevention efforts. [ABSTRACT FROM AUTHOR]

In Tanzania, the One by One: Target COVID-19 campaign was launched nationally in July 2022 to address the prevalent vaccine hesitancy and lack of confidence in COVID-19 vaccines. The campaign mobilized social media influencers and viral content with the ultimate goal of increasing COVID-19 vaccine uptake in the country. The objective of this study was to empirically assess the impact of the campaign on three outcomes: vaccine confidence, vaccine hesitancy, and vaccination status. Using programmatic data collected through an online survey before and after the campaign, we conducted a difference-in-difference (DiD) analysis and performed a crude, adjusted, and propensity score-matched analysis for each study outcome. Lastly, to observe whether there was any differential impact of the campaign across age groups, we repeated the analyses on age-stratified subgroups. Data included 5,804 survey responses, with 3,442 and 2,362 responses collected before and after the campaign, respectively. Although there was only weak evidence of increased COVID-19 vaccine confidence in the campaign-exposed group compared to the control group across all age groups, we observed a differential impact among different age groups. While no significant change was observed among young adults aged 18–24 years, the campaign exposure led to a statistically significant increase in vaccine confidence (weighted/adjusted DiD coefficient = 0.76; 95% CI: 0.06, 1.5; p-value = 0.034) and vaccination uptake (weighted/adjusted DiD coefficient = 1.69.; 95% CI: 1.02, 2.81; p-value = 0.023) among young adults aged 25–34 years. Among adults aged 35 years and above, the campaign exposure led to a significant decrease in vaccine hesitancy (weighted/adjusted DiD coefficient = -15; 95% CI: -21, -8.3; p-value<0.001). The social media campaign successfully improved vaccine hesitancy, confidence, and uptake in the Tanzanian population, albeit to varying degrees across age groups. Our study provides valuable insights for the planning and evaluation of similar social media communication campaigns aiming to bolster vaccination efforts. [ABSTRACT FROM AUTHOR]

Background: Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in women of childbearing age, associated with increased incidence of emotional disorders, anxiety and depression. Objective: The aim was of this study was to investigate whether those women reporting PCOS differed to women without PCOS in measures of psychological well-being including body-image satisfaction and self-esteem across a Middle Eastern population. Materials and methods: An online survey link of 21 questions was shared and distributed across social media platforms (Instagram and WhatsApp). The main outcome measured was levels of self-esteem and body image satisfaction in association with symptoms experienced by the participants. Results: 12,199 female subjects completed the survey of whom 3,329 respondents (27.3%) self-reported a diagnosis of PCOS. PCOS respondents felt less attractive compared to non-PCOS respondents (73.9% vs 80.5%, p<0.0001). More respondents with PCOS reported avoidance of their reflection in the mirror (61.7% vs 49.8%, p<0.001) and avoidance of social interactions (22.3% vs 32.3%, p<0.0001). More PCOS respondents wanted to lose weight (75.2% vs 68.5%, p<0.001) with increasing weight being associated with being less attractive (p<0.001). Fewer PCOS respondents felt satisfied/confident compared non-PCOS respondents (38.6% vs 50.7%, p<0.001). Conclusion: PCOS respondents reported significantly lower measures of self-esteem and body image satisfaction compared to non-PCOS respondents in this population. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has highlighted an era in hearing health care that necessitates a comprehensive rethinking of audiology service delivery. There has been a significant increase in the number of individuals with hearing loss who seek information online. An estimated 430 million individuals worldwide suffer from hearing loss, including 11 million in the United Kingdom. The objective of this study was to identify National Health Service (NHS) audiology service social media posts and understand how they were used to communicate service changes within audiology departments at the onset of the Covid-19 pandemic. Facebook and Twitter posts relating to audiology were extracted over a six week period (March 23 to April 30 2020) from the United Kingdom. We manually filtered the posts to remove those not directly linked to NHS audiology service communication. The extracted data was then geospatially mapped, and themes of interest were identified via a manual review. We also calculated interactions (likes, shares, comments) per post to determine the posts' efficacy. A total of 981 Facebook and 291 Twitter posts were initially mined using our keywords, and following filtration, 174 posts related to NHS audiology change of service were included for analysis. The results were then analysed geographically, along with an assessment of the interactions and sentiment analysis within the included posts. NHS Trusts and Boards should consider incorporating and promoting social media to communicate service changes. Users would be notified of service modifications in real-time, and different modalities could be used (e.g. videos), resulting in a more efficient service. [ABSTRACT FROM AUTHOR]

It is becoming harder to tell rumors from non-rumors as social media becomes a key news source, which invites malicious manipulation that could do harm to the public's health or cause financial loss. When faced with situations when the session structure of comment sections is deliberately disrupted, traditional models do not handle them adequately. In order to do this, we provide a novel rumor detection architecture that combines dual comparison learning, adversarial training, and attention filters. We suggest the attention filter module to achieve the filtering of some dangerous comments as well as the filtering of some useless comments, allowing the nodes to enter the GAT graph neural network with greater structural information. The adversarial training module (ADV) simulates the occurrence of malicious comments through perturbation, giving the comments some defense against malicious comments. It also serves as a hard negative sample to aid double contrast learning (DCL), which aims to learn the differences between various comments, and incorporates the final loss in the form of a loss function to strengthen the model. According to experimental findings, our AGAD (Attention Graph Adversarial Dual Contrast Learning) model outperforms other cutting-edge algorithms on a number of rumor detection tasks. The code is available at https://github.com/icezhangGG/AGAD.git. [ABSTRACT FROM AUTHOR]