Instagram for audience engagement: an evaluation of CERC framework in the GCC nations for digital public health during the Covid-19 pandemic.

BACKGROUND: In this study, we investigate the utilization of Instagram by public health ministries across the Gulf Cooperation Council (GCC) nations to disseminate health-related information during the COVID-19 pandemic. With Instagram's visual-centric approach and high user engagement, the research aims to investigate its critical yet complex role in information dissemination amid a health crisis. METHODS: To examine how Instagram communication strategies align with the CDC's Crisis and Emergency Risk Communication (CERC) framework, we employ the content analysis method. This approach helps to evaluate the effectiveness and challenges of employing Instagram for health communication within a region known for its significant social media usage. RESULTS: Findings indicate that Instagram serves as a vital platform for the rapid dissemination of health information in the GCC, leveraging its visual capabilities and wide reach. The GCC ministries of health utilized Instagram to demonstrate a consistent and strategic approach to communicate essential COVID-19 related information. Kuwait and Bahrain were the most active of all the assessed ministries with respect to the number of engagement metrics (likes and comments). Most of the posts, as per the CERC framework, were informational and related to vaccine infection and death cases. The second most salient theme in line with the CERC framework was about promoting actions, followed by Instagram posts about activities, events, and campaigns. CONCLUSIONS: The research underscores Instagram's potential as a powerful tool in enhancing public health resilience and responsiveness during health emergencies in the GCC. It suggests that leveraging social media, with careful consideration of its affordances, can contribute significantly to effective health communication strategies in times of crisis.

Alternating Direction Method of Multipliers-Based Constant Modulus Waveform Design for Dual-Function Radar-Communication Systems.

In this paper, we design constant modulus waveforms for dual-function radar-communication (DFRC) systems based on a multi-input multi-output (MIMO) configuration of sensors for a far-field scenario. At first, we formulate a non-convex optimization problem subject to waveform synthesis for minimizing the interference power while maintaining a constant modulus constraint. Next, we solve this non-convex problem, iteratively, using the alternating direction method of multipliers (ADMM) algorithm. Importantly, the designed waveforms approximate a desired beampattern in terms of a high-gain radar beam and a slightly high gain communication beam while maintaining a desired low sidelobe level. The designed waveforms ensure an improved detection probability and an improved bit error rate (BER) for radar and communications parts, respectively. Finally, we demonstrate the effectiveness of the proposed method through simulation results.

Assessing How Risk Communication Surveillance Prompts COVID-19 Vaccine Acceptance Among Internet Users by Applying the Situational Theory of Problem Solving: Cross-Sectional Study.

BACKGROUND: The World Health Organization has recently raised concerns regarding the low number of people fully vaccinated against COVID-19. The low ratio of fully vaccinated people and the emergence of renewed infectious variants correspond to worsening public health. Global health managers have highlighted COVID-19 vaccine-related infodemics as a significant risk perception factor hindering mass vaccination campaigns. OBJECTIVE: Given the ambiguous digital communication environment that has fostered infodemics, resource-limited nations struggle to boost public willingness to encourage people to fully vaccinate. Authorities have launched some risk communication-laden digital interventions in response to infodemics. However, the value of the risk communication strategies used to tackle infodemics needs to be evaluated. The current research using the tenets of the Situational Theory of Problem Solving is novel, as it explores the impending effects of risk communication strategies. The relationship between infodemic-induced risk perception of COVID-19 vaccine safety and risk communication actions to intensify willingness to be fully vaccinated was examined. METHODS: This study used a cross-sectional research design vis-à-vis a nationally representative web-based survey. We collected data from 1946 internet users across Pakistan. Participants voluntarily participated in this research after completing the consent form and reading ethical permissions. Responses were received over 3 months, from May 2022 to July 2022. RESULTS: The results delineated that infodemics positively affected risk perception. This realization pushed the public to engage in risky communicative actions through reliance on and searches for accurate information. Therefore, the prospect of managing infodemics through risk information exposure (eg, digital interventions) using the situational context could predict robust willingness to be fully vaccinated against COVID-19. CONCLUSIONS: These pioneering results offer strategic considerations for health authorities to effectively manage the descending spiral of optimal protection against COVID-19. This research concludes that the likelihood of managing infodemics using situational context through exposure to relevant information could improve one's knowledge of forfending and selection, which can lead to robust protection against COVID-19. Hence, more situation-specific information about the underlying problem (ie, the selection of an appropriate vaccine) can be made accessible through several official digital sources to achieve a more active public health response.

Unlocking infodemics and mysteries in COVID-19 vaccine hesitancy: Nexus of conspiracy beliefs, digital informational support, psychological Well-being, and religious fatalism.

Guarding against an anti-science camouflage within infodemics is paramount for sustaining the global vaccination drive. Vaccine hesitancy remains a growing concern and a significant threat to public health, especially in developing countries. Infodemics, conspiracy beliefs and religious fatalism primarily fuel vaccine hesitancy. In addition, anti-vaccine disinformation, lack of understanding, and erroneous religious beliefs also trigger vaccine hesitancy. Global behavioral strategies such as wearing face masks and long-term preventive measures (i.e., COVID-19 vaccination) have effectively limited the virus's spread. Despite the alarming rate of global deaths (i.e., over 99% being unvaccinated), a large proportion of the global population remains reluctant to vaccinate. New evidence validates the usefulness of technology-driven communication strategies (i.e., digital interventions) to address the complex socio-psychological influence of the pandemic. Hence, the present research explored the digital information processing model to assess the interface between informational support (through digital interventions) and antecedents of vaccine hesitancy. This research involved two separate studies: a focus group to operationalize the construct of infodemics, which remained ambiguous in previous literature (Study 1), followed by a cross-sectional survey (Study 2) to examine the conceptual model. Data were collected from 1906 respondents through a standard questionnaire administered online. The focus group's findings revealed a multi-dimensional nature of infodemics that was also validated in Study 2. The cross-sectional survey results substantiated infodemics, religious fatalism and conspiracy beliefs as significant predictors of vaccine hesitancy. Similarly, conspiracy beliefs negatively influence an individual's psychological well-being. Furthermore, information support (through digital intervention) affected infodemics and religious fatalism, whereas it inversely influenced the strength of their relationships with vaccine hesitancy. Information support (through digital intervention) also moderated the relationship between conspiracy beliefs and psychological well-being.

How Does Digital Media Search for COVID-19 Influence Vaccine Hesitancy? Exploring the Trade-off between Google Trends, Infodemics, Conspiracy Beliefs and Religious Fatalism.

Digital media has remained problematic during COVID-19 because it has been the source of false and unverified facts. This was particularly evident in the widespread misinformation and confusion regarding the COVID-19 vaccine. Past research suggested infodemics, conspiracy beliefs, and religious fatalism as potential threats to public COVID-19 vaccine hesitancy. However, the literature is primarily void of empirical evidence associating demographic attributes with efforts to build vaccine hesitancy. Therefore, this research uses two studies: (Study 1) Google Trends and (Study 2) survey method to provide inclusive empirical insight into public use of digital media during COVID-19 and the detrimental effects of infodemics, conspiracy beliefs, and religious fatalism as they were related to building COVID-19 vaccine hesitancy. Using Google Trends based on popular keywords the public searched over one year, Study 1 explores public digital media use during COVID-19. Drawing on this exploration, Study 2 used a cross-sectional national representative survey of 2120 adult Pakistanis to describe the influence of potential hazards such as infodemics on public vaccine hesitancy. Study 2 revealed that infodemics, conspiracy beliefs, and religious fatalism predict vaccine hesitancy. In addition, gender moderates the relationship between infodemics and conspiracy beliefs and vaccine hesitancy. This implies that there is a dispositional effect of the infodemics and conspiracy beliefs spread digitally. This study's findings benefit health and other concerned authorities to help them reduce religious fatalism, vaccine hesitancy, and conspiracy theories with targeted communication campaigns on digital media.

Immunity debt or vaccination crisis? A multi-method evidence on vaccine acceptance and media framing for emerging COVID-19 variants.

Renewed COVID-19 outbreaks, stemming from the highly infectious Delta and Omicron variants, prompted rising fears of a 'pandemic among the unvaccinated'. To address this prevalent vaccination crisis, media framing communication strategies can amplify the scientific evidence on COVID-19 vaccines to reach diverse geographic and socio-economic communities. The critical role of media framing strategies to engage and encourage large populations regarding vaccine acceptance has been rarely studied, despite growing evidence on vaccine hesitancy. The present study used a multi-method approach (i.e., content analysis and quasi-experiments) that unpacked the framing practices employed by the mainstream media in Pakistan. The findings of the content analysis revealed that the media extensively used uncertainty, conflict, consequences, and action rather than new evidence and reassurance frames in its COVID-19 related campaigns. In a series of quasi-experiments involving 720 participants, we manipulated these six frames of COVID-19 related news coverage (i.e., uncertainty, conflict, consequences, action, new evidence, and reassurance) to investigate the underlying mechanism that influences vaccine acceptance. The findings established that the message-consistent effects of media frames manifesting fear (e.g., consequence and uncertainty) and action cues made receivers more supportive of vaccination. The present study findings theoretically address the calls for a more inclusive "community-health reporting model", besides offering new evidence on the media framing strategies to deliver more targeted, meaningful, and effective campaigns to raise public acceptance for COVID-19 vaccines.

Modeling Public Sentiments About JUUL Flavors on Twitter Through Machine Learning.

INTRODUCTION: The availability of a variety of e-cigarettes flavors is one of the frequently cited reasons for their adoption. An active stream of discussion about flavoring can be observed online. Analyzing these real-time conversations offers nuanced insights into key factors related to the adoption of flavors, subsequently supporting public health interventions. METHODS: Google's BERT, a state-of-the-art deep learning method was employed to model the first sentiment corpus on JUUL flavors. BERT, which is pre-trained with the complete English Wikipedia was fine-tuned by integrating a classification model, with human labeled Tweets, as training data. A collection of 30 075 Tweets about JUUL flavors was classified into positive and negative sentiments. Finally, using topic models, we identify and grouped thematic areas into positive and negative Tweets. RESULTS: With an average of 89% cross-validation precision for classifying Tweets, the fine-tuned BERT model classified 24 114 Tweets as positive and 5961 Tweets as negative. Through the topic modeling approach 10 thematic topics were identified from the predicted positive and negative sentiments expressed in the Tweets. CONCLUSIONS: JUUL flavors, notably mango, mint, and cucumber, provoke overwhelmingly positive sentiments indicating a strong likeness due to favorable taste and odor. Negative discourse about JUUL flavors revolve around addictiveness, high nicotine content, and youth targeted marketing. IMPLICATIONS: Limiting the content related to flavors and positive perceptions on social media is necessary to minimize exposure to youth. The novel methodology used in this study may be adopted to monitor e-cigarette discourse periodically, as well as other critical public health phenomena online.

Gamification for Older Adults: A Systematic Literature Review.

BACKGROUND AND OBJECTIVES: During past years, gamification has become a major trend in technology, and promising results of its effectiveness have been reported. However, prior research has predominantly focused on examining the effects of gamification among young adults, while other demographic groups such as older adults have received less attention. In this review, we synthesize existing scholarly work on the impact of gamification for older adults. RESEARCH DESIGN AND METHODS: A systematic search was conducted using 4 academic databases from inception through January 2019. A rigorous selection process was followed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: Twelve empirical peer-reviewed studies written in English, focusing on older adults aged ≥55, including a gameful intervention, and assessing subjective or objective outcomes were identified. Eleven of the 12 studies were conducted in the health domain. Randomized controlled study settings were reported in 8 studies. Positively oriented results were reported in 10 of 12 studies on visual attention rehabilitation, diabetes control, increasing positive emotions for patients with subthreshold depression, cognitive training and memory tests, engagement in training program, perceptions of self-efficacy, motivation and positive emotions of social gameplay conditions, increased physical activity and balancing ability, and increased learning performance and autonomy experiences. The results are, however, mostly weak indications of positive effects. DISCUSSION AND IMPLICATIONS: Overall, the studies on gameful interventions for older adults suggest that senior users may benefit from gamification and game-based interventions, especially in the health domain. However, due to methodological shortcomings and limited amount of research available, further work in the area is called for.

Public health agencies outreach through Instagram during the COVID-19 pandemic: Crisis and Emergency Risk Communication perspective.

Background: Governmental and non-governmental institutions increasingly use social media as a strategic tool for public outreach. Global spread, promptness, and dialogic potentials make these platforms ideal for public health monitoring and emergency communication in crises such as COVID-19. Objective: Drawing on the Crisis and Emergency Risk Communication framework, we sought to examine how leading health organizations use Instagram for communicating and engaging during the COVID-19 pandemic. Methods: We manually retrieved Instagram posts together with relevant metadata of four health organizations (WHO, CDC, IFRC, and NHS) shared between January 1, 2020, and April 30, 2020. Two coders manually coded the analytical sample of 269 posts related to COVID-19 on dimensions including content theme, gender depiction, person portrayal, and image type. We further analyzed engagement indices associated with the coded dimensions. Results: The CDC and WHO were the most active of all the assessed organizations with respect to the number of posts, reach, and engagement indices. Most of the posts were about personal preventive measures and mitigation, general advisory and vigilance, and showing gratitude and resilience. An overwhelming level of engagement was observed for posts representing celebrity, clarification, and infographics. Conclusions: Instagram can be an effective tool for health organizations to convey their messages during crisis communication, notably through celebrity involvement, clarification posts, and the use of infographics. There is much opportunity to strengthen the role of health organizations in countering misinformation on social media by providing accurate information, directing users to credible sources, and serving as a fact-check for false information.

Characterizing vaping posts on Instagram by using unsupervised machine learning.

Electronic cigarettes (e-cigarettes) usage has surged substantially across the globe, particularly among adolescents and young adults. The ever-increasing prevalence of social media makes it highly convenient to access and engage with content on numerous substances, including e-cigarettes. A comprehensive dataset of 560,414 image posts with a mention of #vaping (shared from 1 June 2019 to 31 October 2019) was retrieved by using the Instagram application-programming interface. Deep neural networks were used to extract image features on which unsupervised machine-learning methods were leveraged to cluster and subsequently categorize the images. Descriptive analysis of associated metadata was further conducted to assess the influence of different entities and the use of hashtags within different categories. Seven distinct categories of vaping related images were identified. A majority of the images (40.4 %) depicted e-liquids, followed by e-cigarettes (15.4 %). Around one-tenth (9.9 %) of the dataset consisted of photos with person(s). Considering the number of likes and comments, images portraying person(s) gained the highest engagement. In almost every category, business accounts shared more posts on average compared to the individual accounts. The findings illustrate the high degree of e-cigarettes promotion on a social platform prevalent among youth. Regulatory authorities should enforce policies to restrict product promotion in youth-targeted social media, as well as require measures to prevent underage users' access to this content. Furthermore, a stronger presence of anti-tobacco portrayals on Instagram by public health agencies and anti-tobacco campaigners is needed.

Live, Love, Juul: User and Content Analysis of Twitter Posts about Juul.

Objectives: In this study, we identified patterns of communication around Juul use and users on Twitter.Methods: Public tweets were collected from April 27, 2018 until June 27, 2018. We categorized 1008 randomly selected tweets on 4 dimensions: user type, sentiment, genre, and theme. Results: Most tweets were through personal accounts followed by ones of the tobacco industry. Participation by anti-tobacco campaigners, educational, and governmental entities was limited. Posts were mostly about first-hand use, use intentions, and personal opinions. Tweets advocating Juul were most common; meanwhile a handful of tweets discouraged Juul use. Young women, young men, and the tobacco industry expressed positive sentiments about Juul. Conclusions: Twitter data are a rich source of public communication to complement surveillance of emerging tobacco products. Youth actively and positively communicate about Juul on Twitter. Educational content and strategies must be examined for curtailing dissemination of positive sentiments and advocacy that normalize and promote Juul use among youth and non-smokers. We observed limited evidence supporting a claim for Juul to be a smoking cessation adjunct.

Numerical solution to generalized Burgers'-Fisher equation using Exp-function method hybridized with heuristic computation.

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems.

A novel modulation classification approach using Gabor filter network.

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.

Performance analysis of relay subset selection for amplify-and-forward cognitive relay networks.

Cooperative communication is regarded as a key technology in wireless networks, including cognitive radio networks (CRNs), which increases the diversity order of the signal to combat the unfavorable effects of the fading channels, by allowing distributed terminals to collaborate through sophisticated signal processing. Underlay CRNs have strict interference constraints towards the secondary users (SUs) active in the frequency band of the primary users (PUs), which limits their transmit power and their coverage area. Relay selection offers a potential solution to the challenges faced by underlay networks, by selecting either single best relay or a subset of potential relay set under different design requirements and assumptions. The best relay selection schemes proposed in the literature for amplify-and-forward (AF) based underlay cognitive relay networks have been very well studied in terms of outage probability (OP) and bit error rate (BER), which is deficient in multiple relay selection schemes. The novelty of this work is to study the outage behavior of multiple relay selection in the underlay CRN and derive the closed-form expressions for the OP and BER through cumulative distribution function (CDF) of the SNR received at the destination. The effectiveness of relay subset selection is shown through simulation results.