scholarly journals Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis

10.2196/14731 ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. e14731 ◽  
Author(s):  
Yahya Albalawi ◽  
Nikola S Nikolov ◽  
Jim Buckley
Keyword(s):  
Social Media ◽  
Saudi Arabia ◽  
Health Information ◽  
Application Programming Interface ◽  
Vital Role ◽  
Decorative Element ◽  
Metadata Analysis ◽  
Health Related ◽  
Social Media Platforms ◽  
Using Data

Background Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information. Objective The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail. Methods A health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets’ metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed. Results A total of 34 out of 109 tweets (31.2%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet’s author was tagged as favorited by the community. Conclusions The results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida—a decorative element in Arabic writing used to justify the text within lines—were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work.

Health-related fake news on social media platforms: A systematic literature review

2021 ◽  
pp. 146144482110387
Author(s):  
Cristiane Melchior ◽  
Mírian Oliveira
Keyword(s):  
Social Media ◽  
Health Information ◽  
Future Research ◽  
Fake News ◽  
Health Agencies ◽  
Health Related ◽  
Social Media Platforms ◽  
Future Work ◽  
News Sharing

This review aims to (a) investigate the characteristics of both the research community and the published research on health-related fake news on social media platforms, and (b) identify the challenges and provide recommendations for future research on the subject. We reviewed 69 journal articles found in the main academic databases up to April 2021. The studies extracted data mainly from Twitter, YouTube, and Facebook. Most articles aimed to investigate the public’s reaction to fake health information, concluding that health agencies and professionals should increase their online presence. The articles also suggest that future work should aim to improve the quality of health information on social media platforms, develop new tools and strategies to combat fake news sharing, and study the credibility of health information. Nonetheless, those in control of the platforms are the only ones which can take effective measures to ensure that their users receive reliable information.

scholarly journals Rapid deployment of a community engagement study and educational trial via social media: implementation of the UC-COVID study

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lauren E. Wisk ◽  
Russell G. Buhr
Keyword(s):  
Social Media ◽  
Search Activity ◽  
Community Partners ◽  
Community Values ◽  
Sample Representativeness ◽  
Adult Participants ◽  
Health Related ◽  
Social Media Platforms ◽  
Rapid Deployment

Abstract Background In response to the COVID-19 pandemic and associated adoption of scarce resource allocation (SRA) policies, we sought to rapidly deploy a novel survey to ascertain community values and preferences for SRA and to test the utility of a brief intervention to improve knowledge of and values alignment with a new SRA policy. Given social distancing and precipitous evolution of the pandemic, Internet-enabled recruitment was deemed the best method to engage a community-based sample. We quantify the efficiency and acceptability of this Internet-based recruitment for engaging a trial cohort and describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools. Methods We recruited 1971 adult participants (≥ 18 years) via engagement with community partners and organizations and outreach through direct and social media messaging. We quantified response rate and participant characteristics of our sample, examine sample representativeness, and evaluate potential non-response bias. Results Recruitment was similarly derived from direct referral from partner organizations and broader social media based outreach, with extremely low study entry from organic (non-invited) search activity. Of social media platforms, Facebook was the highest yield recruitment source. Bot activity was present but minimal and identifiable through meta-data and engagement behavior. Recruited participants differed from broader populations in terms of sex, ethnicity, and education, but had similar prevalence of chronic conditions. Retention was satisfactory, with entrance into the first follow-up survey for 61% of those invited. Conclusions We demonstrate that rapid recruitment into a longitudinal intervention trial via social media is feasible, efficient, and acceptable. Recruitment in conjunction with community partners representing target populations, and with outreach across multiple platforms, is recommended to optimize sample size and diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms. Trial registration ClinicalTrials.gov NCT04373135. Registered on May 4, 2020

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

Artificial Intelligence, Social Media and Depression. ‘Patient’ Autonomy Revisited

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 133-133
Author(s):  
Regina Mueller ◽  
◽  
Sebastian Laacke ◽  
Georg Schomerus ◽  
Sabine Salloch ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Mental Disorders ◽  
Patient Autonomy ◽  
Ethical Challenges ◽  
Traditional Concept ◽  
Ethical Implications ◽  
Health Related ◽  
Twitter Users ◽  
Using Data

"Artificial Intelligence (AI) systems are increasingly being developed and various applications are already used in medical practice. This development promises improvements in prediction, diagnostics and treatment decisions. As one example, in the field of psychiatry, AI systems can already successfully detect markers of mental disorders such as depression. By using data from social media (e.g. Instagram or Twitter), users who are at risk of mental disorders can be identified. This potential of AI-based depression detectors (AIDD) opens chances, such as quick and inexpensive diagnoses, but also leads to ethical challenges especially regarding users’ autonomy. The focus of the presentation is on autonomy-related ethical implications of AI systems using social media data to identify users with a high risk of suffering from depression. First, technical examples and potential usage scenarios of AIDD are introduced. Second, it is demonstrated that the traditional concept of patient autonomy according to Beauchamp and Childress does not fully account for the ethical implications associated with AIDD. Third, an extended concept of “Health-Related Digital Autonomy” (HRDA) is presented. Conceptual aspects and normative criteria of HRDA are discussed. As a result, HRDA covers the elusive area between social media users and patients. "

scholarly journals Multi-Platform Social Media Use: Little Evidence of Impacts on Adult Well-Being

2020 ◽  
Author(s):  
Sophie Lohmann ◽  
Emilio Zagheni
Keyword(s):  
Social Media ◽  
Media Use ◽  
Scientific Evidence ◽  
General Social Survey ◽  
Well Being ◽  
Young Female ◽  
Social Media Use ◽  
Social Media Platforms ◽  
Data Source ◽  
Using Data

Social media have become a near-ubiquitous part of our lives. The growing concern that their use may alter our well-being has been met with elusive scientific evidence. Existing literature often simplifies social media use as a homogeneous process. In reality, social media use and functions vary widely depending on platform and demographic characteristics of users, and there may be qualitative differences between using few versus many different social media platforms. Using data from the General Social Survey, an underanalyzed data source for this purpose, we characterize intensive social media users and examine how differential platform use impacts well-being. We document substantial heterogeneity in the demography of users and show that intensive users tend to be young, female, more likely to be Black than Hispanic, from high SES backgrounds, from more religious backgrounds, and from families with migration background, compared to both non-users and moderate users. The intensity of social media use seemed largely unrelated to well-being in both unadjusted models and in propensity-score models that adjusted for selection bias and demographic factors. Among middle-aged and older adults, however, intensive social media use may be slightly associated with depressive symptoms. Our findings indicate that although mediums of communication have changed with the advent of social media, these new mediums are not necessarily detrimental to well-being.

scholarly journals Physical Activity and Attitudes Toward Social Media Use of Active Individuals During the COVID-19 Pandemic in Saudi Arabia: Cross-Sectional Survey

2021 ◽  
Vol 12 ◽  
Author(s):  
Mezna A. AlMarzooqi
Keyword(s):  
Physical Activity ◽  
Social Media ◽  
Saudi Arabia ◽  
Media Use ◽  
Real Life ◽  
Cross Sectional Survey ◽  
Social Media Use ◽  
Cross Sectional ◽  
Physically Active ◽  
Social Media Platforms

Background: Social media became an integral part of the lives of people because it encourages social relations and shares interests, activities, and real-life connections. As quarantine and lockdown orders are prolonged, many people, as well as those physically active individuals, typically responded to this stressful condition by using social media platforms.Objective: This study aimed to examine the level of physical activity of physically active individuals and their attitudes toward social media use during the COVID-19 pandemic.Methods: A descriptive cross-sectional survey was conducted among physically active individuals in Saudi Arabia between June 2020 and July 2020. An online survey was employed among eligible participants who completed a self-administered questionnaire that covered reasons for physical activity and attitudes toward social media platforms during the COVID-19 pandemic.Results: Of these 323 participants, 29.1% were in the age group of 18–24 years, 66.6% were women, and 67.8% were single. The proportion of participants whose metabolic equivalent of tasks-min/week from vigorous activity <1,680 was 31.9%, and all of the participants follow people or pages in social media related to sports teams and fitness models. The average number of hours spent on social media per day was 2.95 ± 0.90 h. The majority of the participants showed positive attitudes toward social media used for exercise and physical activity. Of the eight variables, age, level of physical activity, and the average of hours spent on social media emerged as significant predictors of attitudes toward the use of social media (P < 0.05).Conclusions: The present survey results indicate adverse consequences of home quarantine as reflected by a small proportion of participants who had differences in levels of vigorous activities during the COVID-19 pandemic in Saudi Arabia. Social media appears to be a key avenue to promote and disseminate health interventions to promote physical activity during this COVID-19 pandemic.

scholarly journals Ontology Meter for Twitter Fake Accounts Detection

2021 ◽  
Vol 14 (1) ◽  
pp. 410-419
Author(s):  
Mohammed Jabardi ◽  
◽  
Asaad Hadi ◽  
Keyword(s):  
Social Media ◽  
Service Providers ◽  
Decision Rules ◽  
Application Programming Interface ◽  
Detection Accuracy ◽  
Online Fraud ◽  
Social Media Platforms ◽  
Consumer Data ◽  
Application Programming ◽  
And Control

One of the most popular social media platforms, Twitter is used by millions of people to share information, broadcast tweets, and follow other users. Twitter is an open application programming interface and thus vulnerable to attack from fake accounts, which are primarily created for advertisement and marketing, defamation of an individual, consumer data acquisition, increase fake blog or website traffic, share disinformation, online fraud, and control. Fake accounts are harmful to both users and service providers, and thus recognizing and filtering out such content on social media is essential. This study presents a new approach to detect fake Twitter accounts using ontology and Semantic Web Rule Language (SWRL) rules. SWRL rules-based reasoner is utilized under predefined rules to infer whether the profile is trust or fake. This approach achieves a high detection accuracy of 97%. Furthermore, ontology classifier is an interpretable model that offers straightforward and human-interpretable decision rules.

Prevalence and Correlates of US Adult Public Opinion on Restricting Junk Food Advertising to Children on Social Media: 2020 Health Information National Trends Survey

2021 ◽  
pp. 1-13
Author(s):  
Ariella R. Korn ◽  
Kelly D. Blake ◽  
Heather D’Angelo ◽  
Jill Reedy ◽  
April Oh
Keyword(s):  
Social Media ◽  
Health Information ◽  
Normal Weight ◽  
Food Advertising ◽  
Junk Food ◽  
Cross Sectional ◽  
Health Related ◽  
Advertising To Children ◽  
National Trends ◽  
Advertising Restrictions

Abstract Objective: To describe US adults’ levels of support, neutrality, and opposition to restricting junk food advertising to children on social media and explore associations with sociodemographic and health-related characteristics. Design: In 2020-2021, we used cross-sectional data from the National Cancer Institute’s 2020 Health Information National Trends Survey to estimate the prevalence of opinions toward advertising restrictions, and correlates of neutrality and opposition using weighted multivariable logistic regression. Setting: United States. Participants: Adults aged 18+ years. Results: Among the analytic sample (n=2852), 54% of adults were neutral or opposed to junk food advertising restrictions on social media. The odds of being neutral or opposed were higher among Non-Hispanic Black adults (vs non-Hispanic White; OR: 2.03 (95% CI: 1.26, 3.26)); those completing some college (OR: 1.68 (95% CI: 1.20, 2.34)) or high school or less (OR: 2.62 (95% CI: 1.74, 3.96)) (vs those with a college degree); those who were overweight (vs normal weight; OR: 1.42 (95% CI: 1.05, 1.93)); and those reporting a moderate (OR: 1.45 (95% CI: 1.13, 1.88)) or conservative (OR: 1.71 (95% CI: 1.24, 2.35)) political viewpoint (vs liberal). Having strong (vs weaker) weight and diet-related cancer beliefs was associated with 53% lower odds of being neutral or opposed to advertising restrictions (OR: 0.47 (95% CI: 0.36, 0.61)). Conclusions: This study identified subgroups of US adults for whom targeted communication strategies may increase support for policies to improve children’s food environment.

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

2022 ◽  
pp. 20-39
Author(s):  
Elliot Mbunge ◽  
Benhildah Muchemwa
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Hospitality Industry ◽  
Learning Models ◽  
Online Data ◽  
Social Media Platforms ◽  
Using Data ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

Engagement with Social Media Platforms via Mobile Apps for Improving Quality of Personal Health Management

2014 ◽  
Vol 16 (1) ◽  
pp. 73-89 ◽  
Author(s):  
Sinjini Mitra ◽  
Rema Padman
Keyword(s):  
Social Media ◽  
Health Plan ◽  
Health Management ◽  
Mobile Apps ◽  
Blood Pressure Level ◽  
Health Condition ◽  
Health Plans ◽  
Health And Wellness ◽  
Health Related ◽  
Social Media Platforms

Patient engagement in self health and wellness management has been identified as an important goal in improving health outcomes. As a result, the use of mobile and social media for health and wellness promotion is gathering considerable momentum. Several early adopting health plans and provider organizations have begun to design and pilot social and mobile media platforms to empower members to enhance self management of health and wellness goals. Based on a member survey of a large health plan in Pennsylvania, the authors identify factors that are significantly associated with member interest in adopting such technology platforms for obtaining health related information and services. Analysis of relevant data from more than 4,000 responses from health plan members indicate significant effects of several factors such as age, gender, general health condition (including presence of chronic conditions like diabetes and high blood pressure), level of computer and social media usage and frequency of engaging in different online activities such as banking, shopping, and emailing. This analysis allows us to identify important consumer segments that are correlated with professed willingness to use applications and programs offered by the health plan. Besides, the authors also develop statistical models to predict people's odds of adopting health-related mobile apps and identify the significant predictors thereof. The authors anticipate that these insights can assist health plans to develop and deploy targeted services and tools through integration of mobile and social media platforms for health and wellness management.

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