OBJECTIVE: To characterize MSM who never tested for HIV, to identify correlates of never testing, and to elucidate the perceived barriers to HIV testing.
METHODS: The present study used data from the Asian Internet MSM Sex Survey (AIMSS) and restricted the analysis to 4,310 MSM from the ten member countries of the Association of South East Asian Nations (ASEAN).
RESULTS: Among MSM participants from ASEAN in our sample, 1290 (29.9%) reported having never been tested for HIV, 471 (10.9%) tested for HIV more than 2 years ago, and 2186 (50.7%) reported their last test date was between 6 months and two years ago, with only 363 (8.4%) of these men having been tested in the past 6 months. In multivariable logistic regression, younger MSM (age 15-22 years old [AOR: 4.60, 95% CI: 3.04-6.96]), MSM with lower education (secondary school or lower [AOR: 1.37, 95% CI: 1.03-1.83]), MSM who identify as bisexual or heterosexual (compared to gay-identified) (AOR: 1.94, 95% CI: 1.60-2.35), and MSM who had never used a condom with male partners (AOR: 1.61, 95% CI: 1.32-1.97) had higher odds of never been HIV tested. Main reason for not being tested was a low risk perception of HIV exposure (n = 390, 30.2%).
CONCLUSION: Current HIV prevention response must not leave MSM "in the dark," but instead meet them where they are by utilizing the Internet creatively through social media and smart phones. As ASEAN Economic Community (AEC) is quickly becoming a reality, so must there be an equally fast and united response to slowing down the HIV epidemics among MSM in ASEAN.
OBJECTIVE: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.
METHODS: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.
RESULTS: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.
CONCLUSIONS: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.
METHODS: The social media analytics site SocialBlade.com was used to identify the most popular YouTube videos (n = 250) targeting children. Ads encountered while viewing these videos were recorded and analyzed for type of product promoted and ad format (video vs. overlay). Food and beverage ads were further coded based on food category and persuasive marketing techniques used.
RESULTS: In total 187 ads were encountered in sampled videos. Food and beverage ads were the most common at 38% (n=71), among which 56.3% (n = 40) promoted noncore foods. Ads for noncore foods were more commonly delivered as video rather than overlay ads. Among ads promoting noncore foods, the most commonly employed persuasive marketing techniques found were taste appeal (42.3%), uniqueness/novelty (32.4%), the use of animation (22.5%), fun appeal (22.5%), use of promotional characters (15.5%), price (12.7%), and health and nutrition benefits (8.5%).
CONCLUSIONS: Similar to television, unhealthy food ads predominate in content aimed toward children on YouTube. Policies regulating food marketing to children need to be extended to cover online content in line with a rapidly-evolving digital media environment. Service providers of social media can play a part in limiting unhealthy food advertising to children.
METHODS: A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment.
RESULT: OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics.
CONCLUSIONS: OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data.
OBJECTIVE: This study examined the COVID-19 pandemic-related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries.
METHODS: We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world's largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19-related chats across countries.
RESULTS: Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: "Questions on COVID-19 asked to the chatbot" (30.6%), "Preventive behaviors" (25.3%), "Outbreak of COVID-19" (16.4%), "Physical and psychological impact of COVID-19" (16.0%), and "People and life in the pandemic" (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19.
CONCLUSIONS: Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people's informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy.
METHODS: Using thematic analysis based on three frameworks, 120 posted messages and comments were examined from MyEndosis Facebook group-a support group for women with endometriosis from January to July 2014.
RESULTS: Results showed the issues discussed were (a) personal struggles, (b) medication and treatment, (c) alternative medication, (d) side effects, and (e) medication recommended by doctors. While using this social medium, users found (a) emotional support, (b) esteem support, (c) information support, (d) network support, and (e) tangible assistance in their engagement with others.
CONCLUSION: The analysis suggested that users' interactions were structured around information, emotion, and community building, which many doctors and nurses were not aware of. The group was shaped as a social network where peer users share social support, cultivate companionship, and exert social influence.
OBJECTIVES: This study aims to identify the determinants associated with Twitter use in psychiatric consultations and to assess the level of satisfaction in using the microblogging platform. In addition, the level of e-health literacy is also assessed among users.
METHODS: The target population included Twitter users seeking psychiatric consultation. A leading psychiatrist's twitter account with 4.5 million followers was selected and consent obtained. A validated Patient Satisfaction Questionnaire was adopted to assess the level of satisfaction in Twitter use and e-health literacy. The questionnaire was tagged to the chosen Twitter account and reminders were sent until the sample size was reached. Data was analysed using SPSS version 22.0. The analysis included descriptive statistics tabulation, multi-response analysis, and cross-tabulation for satisfaction variables and the chi-square test was used to measure association between different variables.
RESULTS: The study obtained 155 completed response sheets, of which 52 were Twitter users seeking psychiatric advice while the rest sought general health advice. Most of the study participants were females (71.6 %). Women, single status and income range between 4000-9000 Saudi riyal were found to be significantly associated with Twitter use for psychiatric consultation. Generally, most of the participants were satisfied with Twitter in seeking psychiatric consultation that reduced financial disbursement. Furthermore, concerns were expressed regarding the waiting period, word limitations and issues of privacy. The e-health literacy was higher among the participants.
CONCLUSION: Psychiatric consultations via Twitter is more popular among women. By addressing privacy issues and reducing response time, Twitter may be used as a major platform to deliver mental health services to the population.
Methods: This open label comparative design study randomized health professional clinicians to receive "practice points" on tendinopathy management via Twitter or Facebook. Evaluated outcomes included knowledge change and self-reported changes to clinical practice.
Results: Four hundred and ninety-four participants were randomized to 1 of 2 groups and 317 responders analyzed. Both groups demonstrated improvements in knowledge and reported changes to clinical practice. There was no statistical difference between groups for the outcomes of knowledge change (P = .728), changes to clinical practice (P = .11) or the increased use of research information (P = .89). Practice points were shared more by the Twitter group (P social media posts are as effective as longer posts for improving knowledge and promoting behavior change. Twitter may be more useful in publicizing information and Facebook for encouraging course completion.