Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action.

Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To reduce these negative effects of multimodal social media content, an important step is to understand creators' intent behind sharing content and to educate their social network of this intent. Towards this goal, we propose INTENT-O-METER, a perceived human intent prediction model for multimodal (image and text) social media posts. INTENT-O-METER models ideas from psychology and cognitive modeling literature, in addition to using the visual and textual features for an improved perceived intent prediction model. INTENT-O-METER leverages Theory of Reasoned Action (TRA) factoring in (i) the creator's attitude towards sharing a post, and (ii) the social norm or perception towards the multimodal post in determining the creator's intention. We also introduce INTENTGRAM, a dataset of 55K social media posts scraped from public Instagram profiles. We compare INTENT-O-METER with state-of-the-art intent prediction approaches on four perceived intent prediction datasets, Intentonomy, MDID, MET-Meme, and INTENTGRAM. We observe that leveraging TRA in addition to visual and textual features-as opposed to using only the latter-results in improved prediction accuracy by up to 7.5 % in Top-1 accuracy and 8 % in AUC on INTENTGRAM. In summary, we also develop a web browser application mimicking a popular social media platform and show users social media content overlaid with these intent labels. From our analysis, around 70 % users confirmed that tagging posts with intent labels helped them become more aware of the content consumed, and they would be open to experimenting with filtering content based on these labels. However, more extensive user evaluation is required to understand how adding such perceived intent labels mitigate the negative effects of social media.

Association between mukbang and cookbang viewing and body image perception and BMI in adolescents.

BACKGROUND: Adolescence is a critical period for establishing healthy eating habits and weight management, essential for preventing obesity and promoting overall health. This study investigates the impact of mukbang and cookbang-popular online broadcasts in Korea that feature excessive consumption of food-on the dietary habits and body image perception of Korean adolescents. With digital media, especially platforms like YouTube, becoming an integral part of daily life, these broadcasts have the potential to significantly influence adolescent health behaviors. METHODS: Employing data from the 18th Korea Youth Risk Behavior Web-based Survey (2022), this descriptive survey research explores the relationship between watching mukbang and cookbang and various health-related factors among adolescents. The survey's comprehensive dataset provided a unique opportunity to examine this association in a population that is increasingly exposed to digital media content. The analysis focused on the frequency of watching mukbang and cookbang, their impact on eating habits, body mass index (BMI), body shape perception, and body image distortion among adolescents. RESULTS: The results revealed a significant engagement with mukbang and cookbang among adolescents, with notable gender differences in viewing habits and effects. Increased frequency of viewing was associated with negative impacts on eating habits and body image perception. Furthermore, psychological factors such as stress levels and sleep quality emerged as significant predictors of the frequency of watching these broadcasts. CONCLUSIONS: This study highlights the need for further investigation into the causal relationships between mukbang and cookbang viewership and adolescent health outcomes. The findings suggest the importance of developing targeted interventions to mitigate the negative influences of such content on adolescents' eating habits and body perceptions. Given the widespread popularity of these broadcasts, it is crucial to address their potential health implications through public health strategies, educational content, and policy development aimed at promoting healthier lifestyles among adolescents.

The impact of social media on adolescent energy drink consumption.

This study aimed to investigate the effects of social media on energy drink consumption among adolescents in Saudi Arabia. An online survey including demographic characteristics (3 questions), consumption patterns of energy drinks (5 questions), and Social Media Effects Scale (4 questions) was completed by 860 Saudi Arabian adolescents. Data were analyzed using Statistical Package for the Social Science version 29, using descriptive statistics and correlation to measure the relationship between social media and energy drink consumption. The results showed that nearly 82% of the adolescent respondents consumed energy drinks once to twice a week. Social media was the most common source of information on energy drinks (42.0%). The major findings of this study showed a positive correlation (r = .592, P > .05) between social media use and consumption of energy drinks. The study found that the average score for the Social Media Effects Scale was 5.75 out of 8, or 71.87%, indicating that social media influences the energy drinks consumption of roughly two-thirds of the study participants. Tailored action plans are required to raise awareness of the negative effects of energy drinks and change consumption patterns among the adolescent population due to a lack of knowledge and poorly controlled legislation on energy drinks.

Exploring the application of CHATGPT in plastic surgery: a comprehensive systematic review.

OBJECTIVE: To determine the impact of ChatGPT in plastic surgery research and assess the authenticity of such contributions. METHODS: The study conducted a literature search in Sep'23 from databases like Pubmed, Google Scholar, SCOPUS, and OVID Medline.The following keywords 'ChatGPT', 'chatbot', 'reconstruction', 'aesthetic' and 'plastic surgery' were used. 32 papers were included from the initial 131 results of articles. English language articles from November 2022 to July 2023 discussing ChatGPT's role in plastic and aesthetic surgery were included whereas non-English documents, irrelevant content, and non-academic sources were excluded from the study. RESULTS: The manuscripts included in the systematic review had a diverse range, including original research articles, case reports, letters to the editor, and editorials. Among the included studies, there were 9 original research articles, 1 case report, 23 letters to the editor, and 2 editorials. Most publications originated from the United States (18) and Australia (7). Analysis suggested concerns, such as inaccuracies, plagiarism, outdated knowledge, and lack of personalized advice. Various authors recommend using ChatGPT as a supplementary tool rather than a replacement for human decision-making in medicine. CONCLUSIONS: ChatGPT shows potential in plastic surgery research, concerns about inaccuracies and outdated knowledge may provide deceiving information and it always requires human input and verification.

Atopic Dermatitis Disease Education.

There has been an influx of new educational resources for atopic dermatitis (AD) patients in recent years. The two primary organizations in the United States offering educational materials, online resources, and other forms of support include the National Eczema Association (NEA) and the American Academy of Dermatology (AAD). Educational workshops and interventions have emerged as tools that can deliver comprehensive information on AD, such as symptoms, treatments, and disease management. In regard to these workshops, studies have proven longer interventions to be more effective. Studies have also found multidisciplinary teams, including psychologists, dietitians, and AD specialists, to be more effective in AD treatment and education. Additionally, video-based education was found to be the most effective delivery medium compared to various written modes of education. Given the psychosocial impacts of AD, support groups have been found to improve life quality and decrease disease severity, with age-specific groups offering the greatest benefits. Technology such as social media and smartphones has also improved education. Social media has allowed the rapid exchange of information to wider audiences, but due to its unregulated nature, false information has also been disseminated. Despite this, web-based interventions have still been found to be satisfying, convenient, and effective in increasing treatment awareness. The advent of smartphone applications has provided patients with access to information on AD symptoms and treatment on demand. While the effectiveness of these promising applications hasn't been confirmed by studies, patient provider interactions via smartphone (teledermatology) have been found to be as effective as in-person appointments. This chapter will discuss these different types of emerging resources available to AD patients including educational materials, interventions, support groups, organizational support, and technological resources and their effectiveness.

Association between health-related social media use and E-cigarette use among current cigarette users: the roles of anti-tobacco messages and harm perception.

BACKGROUND: The popularity of e-cigarettes is on the rise among current cigarette users. Therefore, there are concerns about their health implications. This study examined the impact of health-related social media use on e-cigarette use among current cigarette users. It assesses the mediating influence of online anti-tobacco messages and the moderating role of the harm perception of e-cigarettes. METHODS: This study was focused on 563 current cigarette users from the 2022 Health Information National Trends Survey (HINTS). Three tasks were performed: (1) assessing the direct and indirect impacts of health-related social media use on e-cigarette use among current cigarette users, (2) exploring the mediating role of exposure to online anti-tobacco messages, and (3) examining the moderating influence of e-cigarette harm perception on the path from anti-tobacco messages to e-cigarette use. RESULTS: Health-related social media use was positively associated with current cigarette users' e-cigarette use directly (bp = 0.183, p < .01) and indirectly through exposure to online anti-tobacco messages (bp = 0.023, 95% CI: [0.001, 0.051]). Harm perception of e-cigarettes moderated the relationship between online exposure to anti-tobacco messages and e-cigarette use (bp=-0.467, p < .01). The relationship appeared weaker for individuals who perceived greater harm from e-cigarettes. CONCLUSIONS: Health-related social media use positively correlates with e-cigarette use among current cigarette users through exposure to online anti-tobacco messages. The perceived harm of e-cigarettes moderates this indirect path. These findings have implications for public health interventions aimed at smoking cessation.

Digital media use, depressive symptoms and support for violent radicalization among young Canadians: a latent profile analysis.

BACKGROUND: Despite the prominent role that digital media play in the lives and mental health of young people as well as in violent radicalization (VR) processes, empirical research aimed to investigate the association between Internet use, depressive symptoms and support for VR among young people is scant. We adopt a person-centered approach to investigate patterns of digital media use and their association with depressive symptoms and support for VR. METHODS: A sample of 2,324 Canadian young people (Mage = 30.10; SDage = 5.44 ; 59% women) responded to an online questionnaire. We used latent profile analysis to identify patterns of digital media use and linear regression to estimate the associations between class membership, depressive symptoms and support for VR. RESULTS: We identified four classes of individuals with regards to digital media use, named Average Internet Use/Institutional trust, Average internet use/Undifferentiated Trust, Limited Internet Use/Low Trust and Online Relational and Political Engagement/Social Media Trust. Linear regression indicated that individuals in the Online Relational and Political Engagement/Social Media Trust and Average Internet Use/Institutional trust profiles reported the highest and lowest scores of both depression and support for VR, respectively. CONCLUSIONS: It is essential to tailor prevention and intervention efforts to mitigate risks of VR to the specific needs and experiences of different groups in society, within a socio-ecological perspective. Prevention should consider both strengths and risks of digital media use and simulteaneously target both online and offline experiences and networks, with a focus on the sociopolitical and relational/emotional components of Internet use.

Advocating for Older Adults in the Age of Social Media: Strategies to Achieve Peak Engagement on Twitter.

Background: Over the last decade, many organizations dedicated to serving the needs and interests of older adults have turned to social media platforms, such as Twitter, subsequently rebranded X, to improve the visibility of age-related issues. However, notwithstanding their growing digital presence and participation, minimal attention has been paid to the use of social media among these advocacy groups. To achieve policy change, advocacy organizations must first be able to engage and mobilize audiences. Objective: Our study aims to elucidate how different tweet features affect the time it takes for posts uploaded by age advocacy organizations to reach peak engagement. Methods: We collated 204,905 tweets from 53 age advocacy organizations posted over a 12-year period. The engagement score of each tweet was calculated by combining well-established metrics, namely likes, retweets, quote tweets, and replies. We ran Cox models with tweet features as predictors and time-to-peak engagement as the outcome. "Peak engagement" (event) refers to engagement scores above the 75th percentile, and "time" refers to months taken to reach peak engagement per tweet. Results: Approximately 1 in 2 tweets (n=103,068, 50.3%) had either no hashtags or just 1 hashtag. Around two-thirds (n=131,220, 64%) of the tweets included a URL. Visual information was highly underused, with most tweets not including GIFs (n=204,202, 99.7%), videos (n=199,800, 97.5%), or photos (n=143,844, 70.2%). Roughly half (n=101,470, 49.5%) of the tweets contained mentions and 9.3% (n=19,009) of tweets were replies. Only 4.5% (n=9285) of tweets were quote tweets. Most tweets were uploaded in the afternoon (n=86,004, 42%) and on a weekday (n=180,499, 88.1%). As hypothesized, features associated with peak engagement were the inclusion of visual elements like photos, which increased peak engagement by 3 times (P<.001), and the use of 3 or more hashtags (P<.001). Quote tweets increased engagement by 3 times (P<.001), as compared to regular tweets, controlling for account-level covariates. Tweets from organizations with a higher tweet volume were 40% less likely to reach peak engagement (P<.001). Conclusions: Social media as a networked platform has the potential to reach users on a global scale and at an exponential speed. Having uncovered the features that are more likely to reach peak engagement on Twitter, our study serves as an invaluable resource for age advocacy organizations in their movement to create a more age-inclusive world.

Database comments on Telegram channels related to cryptocurrencies with sentiments.

OBJECTIVES: Due to the limitations of Twitter, the expansion of Telegram channels, and the Telegram API's easy use, Telegram comments have become prevalent. Telegram is one of the most popular social networks, unlike Twitter, which has no restrictions on sending messages, and experts can share their opinions and media. Some of these channels, managed by influencers of large companies, are very influential in the behavior of the market on various stocks, including cryptocurrencies. In this research, the opinion collection of 10 famous Telegram channels regarding the analysis of cryptocurrencies has been extracted. The sentiments of these opinions have been analyzed using the HDRB model. HDRB is a hybrid model of RoBERTa deep neural network, BiGRU, and attention layer used for sentiment analysis (SA). Analyzing the sentiments of these opinions is very important for understanding the future behavior of the market and managing the stock portfolio. The opinions of this dataset, published by experts in the field of cryptocurrencies, are precious, unlike the opinions that are extracted only by using the hashtag of the names of cryptocurrencies. On the other hand, the dataset related to cryptocurrencies, which has the opinions of experts and the polarity of their feelings, is very rare. DATA DESCRIPTION: The dataset of this research is the sentiments of more than ten popular Telegram channels regarding a wide range of cryptocurrencies. These comments were collected through the Telegram API from December 2023 to March 2024. This data set contains an Excel file containing the text of the comments, the date of comment creation, the number of views, the compound score, the sentiment score, and the type of sentiment polarity. These opinions cover influencer analysis on a wide range of cryptocurrencies. Also, two Word files, one containing the description of the dataset columns and the other Python code for extracting comments from Telegram channels, are included in this dataset.

Use of digital media by adolescents for sexual and reproductive health and rights communication in sub-Saharan Africa: a protocol for systematic review.

BACKGROUND: Within the sub-Saharan African region, there is a growing concern for sexual and reproductive health and rights communication, and more particularly, for adolescents. Given the existing barriers associated with face-to-face access, the need to use digital media to access information and services has become desirable and imperative, especially so due to the COVID-19 pandemic. However, in sub-Saharan Africa, a synthesis of evidence that informs adolescents' digital media engagements for sexual and reproductive health and rights (SRHR) communication is limited. This systematic review therefore aims to examine and synthesize evidence on use of digital media for sexual and reproductive health and rights communication by adolescents in sub-Saharan Africa. METHODS: A search for peer-reviewed articles will be conducted in PubMed, ScienceDirect, Scopus, Embase, Web of Science, PsychINFO and Google Scholar with emphasis on those published between 2000 and 2023. Only observational and qualitative studies will be included. Quality assessment of included articles will be done using standardized checklists from the Joanna Briggs Institute. Both descriptive and narrative summaries will be used to appraise evidence from included studies. DISCUSSION: This review will be essential in providing information on the types of digital media adolescents use, the various SRHR issues they use this platform to address and their reasons for using it and associated challenges. It will also contribute to the advocacy for the inclusion of these technologies in the teaching and learning, provision of and access to SRHR information and services by teachers, public health providers and peer educators in the subregion. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020211491. This protocol follows the PRISMA-P guidelines for reporting systematic reviews.

Is social media our new quitline? A descriptive study assessing youtube coverage of tobacco cessation.

Background: Tobacco use and exposure are leading causes of morbidity and mortality worldwide. In the past decade, educational efforts to reduce tobacco use and exposure have extended to social media, including video-sharing platforms. YouTube is one of the most publicly accessed video-sharing platforms. Purpose: This cross-sectional descriptive study was conducted to identify and describe sources, formats, and content of widely viewed YouTube videos on smoking cessation. Methods: In August to September 2023, the keywords "stop quit smoking" were used to search in YouTube and identify 100 videos with the highest view count. Results: Collectively, these videos were viewed over 220 million times. The majority (n = 35) were posted by nongovernmental/organization sources, with a smaller number posted by consumers (n = 25), and only eleven were posted by governmental agencies. The format used in the highest number of videos was the testimonial (n = 32 videos, over 77 million views). Other popular formats included animation (n = 23 videos, over 90 million views) and talk by professional (n = 20 videos, almost 43 million views). Video content included evidence-based and non-evidence-based practices. Evidence-based strategies aligned with U.S. Public Health Service Tobacco Treatment Guidelines (e.g. health systems approach in tobacco treatment, medication management). Non-evidence-based strategies included mindfulness and hypnotherapy. One key finding was that environmental tobacco exposure received scant coverage across the videos. Conclusions: Social media such as YouTube promises to reach large audiences at low cost without requiring high reading literacy. Additional attention is needed to create videos with up-to-date, accurate information that can engage consumers.

Emerging Trends of Self-Harm Using Sodium Nitrite in an Online Suicide Community: Observational Study Using Natural Language Processing Analysis.

Background: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, "Sanctioned Suicide," which is a primary source of information on the use and procurement of SN. Objective: This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN. Methods: We collected all publicly available from the site's inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN. Results: Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere. Conclusions: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.

Characterization of the role of Facebook groups for patients who use scalp cooling therapy: a survey study.

Since the emergence of scalp cooling therapy (SCT) for the prevention of chemotherapy-induced alopecia (CIA), support groups on social media platforms for interested patients have surfaced. Though there are over 20,000 active members across SCT Facebook groups, little is known about how members use this platform. A 23-question survey was posted in five scalp cooling Facebook groups, reaching 219 women. Results indicated that these Facebook groups play clear roles in providing the following: (1) a supportive community for patients, (2) instructions for SCT use, (3) advice regarding insurance coverage and reimbursement, and (4) recommendations for over-the-counter products for hair loss. Despite reported interest in hair loss products, only 5% of patients sought medical treatment from dermatologists. Due to group-specific access restrictions, private Facebook groups provide patients with a protected platform to learn more about SCT from both those with personal experience and SCT company specialists. Providers may consider recommending these online groups to interested patients during the scalp cooling counseling process. As patients with CIA express a growing interest in over-the-counter hair, eyebrow, and eyelash products, it is important for dermatologists to be aware of where their patients obtain recommendations, and further, if these recommendations have clinical evidence of efficacy.

Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study.

BACKGROUND: The information epidemic emerged along with the COVID-19 pandemic. While controlling the spread of COVID-19, the secondary harm of epidemic rumors to social order cannot be ignored. OBJECTIVE: The objective of this paper was to understand the characteristics of rumor dissemination before and after the pandemic and the corresponding rumor management and debunking mechanisms. This study aimed to provide a theoretical basis and effective methods for relevant departments to establish a sound mechanism for managing network rumors related to public health emergencies such as COVID-19. METHODS: This study collected data sets of epidemic rumors before and after the relaxation of the epidemic prevention and control measures, focusing on large-scale network rumors. Starting from 3 dimensions of rumor content construction, rumor propagation, and rumor-refuting response, the epidemic rumors were subdivided into 7 categories, namely, involved subjects, communication content, emotional expression, communication channels, communication forms, rumor-refuting subjects, and verification sources. Based on this framework, content coding and statistical analysis of epidemic rumors were carried out. RESULTS: The study found that the rumor information was primarily directed at a clear target audience. The main themes of rumor dissemination were related to the public's immediate interests in the COVID-19 field, with significant differences in emotional expression and mostly negative emotions. Rumors mostly spread through social media interactions, community dissemination, and circle dissemination, with text content as the main form, but they lack factual evidence. The preferences of debunking subjects showed differences, and the frequent occurrence of rumors reflected the unsmooth channels of debunking. The χ2 test of data before and after the pandemic showed that the P value was less than .05, indicating that the difference in rumor content before and after the pandemic had statistical significance. CONCLUSIONS: This study's results showed that the themes of rumors during the pandemic are closely related to the immediate interests of the public, and the emotions of the public accelerate the spread of these rumors, which are mostly disseminated through social networks. Therefore, to more effectively prevent and control the spread of rumors during the pandemic and to enhance the capability to respond to public health crises, relevant authorities should strengthen communication with the public, conduct emotional risk assessments, and establish a joint mechanism for debunking rumors.

Promotion of Physical Activity Among University Students With Social Media Or Text Messaging: A Systematic Review.

Regular physical exercise lowers the risk of all-cause mortality and various chronic diseases. New technologies, such as smartphones and social media, have been used successfully as health promotion tools in college populations. The purpose of this study was to conduct a systematic review of studies examining the effectiveness of interventions that used modern technologies, as with social media or text messaging, to promote physical activity or reducing sedentary behavior in college students. The systematic review was conducted on the PubMed and SCOPUS databases, considering studies published from 2012 to 2022. For a total of 19 articles selected, an evidence table was drawn up, and the quality of the studies was assessed using the PRISMA checklist. The interventions differed enormously in design, from the strategies implemented to the types of outcome considered. Fifteen of the 19 studies demonstrated an improvement in participants' physical activity levels, 3 studies found no such improvement, and 1 reported a worsening of baseline activity levels. Interventions to improve college students' physical activity levels through the use of social media and/or text messaging tend to be effective. However, many factors can influence the effectiveness of such interventions. For example, a gender-related difference emerged in student participation, and the interventions proved more effective if they were accompanied by the creation of social groups.

The public attitude towards ChatGPT on reddit: A study based on unsupervised learning from sentiment analysis and topic modeling.

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.

Social media influence on COVID-19 vaccine perceptions among University students: a Malawi case study.

INTRODUCTION: The global fight against the COVID-19 pandemic relies significantly on vaccination. The collective international effort has been massive, but the pace of vaccination finds hindrance due to supply and vaccine hesitancy factors. Understanding public perceptions, especially through the lens of social media, is important. This study investigates the influence of social media on COVID-19 vaccine perceptions among university students in Malawi. METHODS: The study utilized a quantitative methodology and employed a cross-sectional study design to explore the relationship between social media dynamics and COVID-19 vaccine perceptions among 382 randomly sampled students at MUBAS. Data, collected by use of a Likert-scale questionnaire, was analyzed using IBM SPSS 20 for descriptive statistics and Pearson correlation tests. RESULTS: The findings reveal crucial correlations. Specifically, trust in online vaccine information shows a positive correlation (r = 0.296, p < 0.01) with active engagement in social media discussions. Conversely, a negative correlation surfaces concerning individuals' reactions to vaccine availability in Malawi (r = -0.026, p > 0.05). The demographic overview highlights the prevalence of the 16 to 30 age group, representing 92.9% of respondents. CONCLUSIONS: The identified correlations emphasize the need for careful communication strategies tailored to combat misinformation and enhance vaccine acceptance among the younger demographic in Malawi. The positive correlation between trust in online vaccine information and social media engagement underscores digital platforms' potential for disseminating accurate information. Conversely, the negative correlation with vaccine availability reactions suggest the presence of complex factors shaping public perceptions.

Assessing the Quality of ChatGPT Responses to Dementia Caregivers' Questions: Qualitative Analysis.

Background: Artificial intelligence (AI) such as ChatGPT by OpenAI holds great promise to improve the quality of life of patients with dementia and their caregivers by providing high-quality responses to their questions about typical dementia behaviors. So far, however, evidence on the quality of such ChatGPT responses is limited. A few recent publications have investigated the quality of ChatGPT responses in other health conditions. Our study is the first to assess ChatGPT using real-world questions asked by dementia caregivers themselves. objectives: This pilot study examines the potential of ChatGPT-3.5 to provide high-quality information that may enhance dementia care and patient-caregiver education. Methods: Our interprofessional team used a formal rating scale (scoring range: 0-5; the higher the score, the better the quality) to evaluate ChatGPT responses to real-world questions posed by dementia caregivers. We selected 60 posts by dementia caregivers from Reddit, a popular social media platform. These posts were verified by 3 interdisciplinary dementia clinicians as representing dementia caregivers' desire for information in the areas of memory loss and confusion, aggression, and driving. Word count for posts in the memory loss and confusion category ranged from 71 to 531 (mean 218; median 188), aggression posts ranged from 58 to 602 words (mean 254; median 200), and driving posts ranged from 93 to 550 words (mean 272; median 276). Results: ChatGPT's response quality scores ranged from 3 to 5. Of the 60 responses, 26 (43%) received 5 points, 21 (35%) received 4 points, and 13 (22%) received 3 points, suggesting high quality. ChatGPT obtained consistently high scores in synthesizing information to provide follow-up recommendations (n=58, 96%), with the lowest scores in the area of comprehensiveness (n=38, 63%). Conclusions: ChatGPT provided high-quality responses to complex questions posted by dementia caregivers, but it did have limitations. ChatGPT was unable to anticipate future problems that a human professional might recognize and address in a clinical encounter. At other times, ChatGPT recommended a strategy that the caregiver had already explicitly tried. This pilot study indicates the potential of AI to provide high-quality information to enhance dementia care and patient-caregiver education in tandem with information provided by licensed health care professionals. Evaluating the quality of responses is necessary to ensure that caregivers can make informed decisions. ChatGPT has the potential to transform health care practice by shaping how caregivers receive health information.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Introduction: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Method: Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. Result: The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. Discussion: This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.