Visualizing Relation Between (De)Motivating Topics and Public Stance toward COVID-19 Vaccine (preprint)

While social media plays a vital role in communication nowadays, misinformation and trolls can easily take over the conversation and steer public opinion on these platforms. We saw the effect of misinformation during the COVID-19 pandemic when public health officials faced significant push-back while trying to motivate the public to vaccinate. To tackle the current and any future threats in emergencies and motivate the public towards a common goal, it is essential to understand how public motivation shifts and which topics resonate among the general population. In this study, we proposed an interactive visualization tool to inspect and analyze the topics that resonated among Twitter-sphere during the COVID-19 pandemic and understand the key factors that shifted public stance for vaccination. This tool can easily be generalized for any scenario for visual analysis and to increase the transparency of social media data for researchers and the general population alike.

Domestic Violence and Mental Health During the COVID-19 Pandemic in Bangladesh.

BACKGROUND: The COVID-19 lockdown, the advent of working from home, and other unprecedent events have resulted in multilayer and multidimensional impacts on our personal, social, and occupational lives. Mental health conditions are deteriorating, financial crises are increasing in prevalence, and the need to stay at home has resulted in the increased prevalence of domestic violence. In Bangladesh, where domestic violence is already prevalent, the lockdown period and stay-at-home orders could result in more opportunities and increased scope for perpetrators of domestic violence. OBJECTIVE: In this study, we aimed to determine the prevalence and pattern of domestic violence during the initial COVID-19 lockdown period in Bangladesh and the perceptions of domestic violence survivors with regard to mental health care. METHODS: We conducted this cross-sectional web-based study among the Bangladeshi population and used a semistructured self-reported questionnaire to understand the patterns of domestic violence and perceptions on mental health care from August to September 2020. The questionnaire was disseminated on different organizational websites and social media pages (ie, those of organizations that provide mental health and domestic violence services). Data were analyzed by using IBM SPSS (version 22.0; IBM Corporation). RESULTS: We found that 36.8% (50/136) of respondents had faced domestic violence at some point in their lives; psychological abuse was the most common type of violence. However, the prevalence of the economical abuse domestic violence type increased after the COVID-19 lockdown was enforced. Although 96.3% (102/136) of the participants believed that domestic violence survivors need mental health support, only 25% (34/136) of the respondents had an idea about the mental health services that are available for domestic violence survivors in Bangladesh and how and where they could avail mental health services. CONCLUSIONS: Domestic violence is one of the most well-known stressors that have direct impacts on physical and mental health. However, the burden of domestic violence is often underreported, and its impact on mental health is neglected in Bangladesh. The burden of this problem has increased during the COVID-19 crisis, and the cry for mental health support is obvious in the country. However, it is necessary to provide information about available support services; telepsychiatry can be good option for providing immediate mental health support in a convenient and cost-effective manner.

Real-time credible online health information inquiring: a novel search engine misinformation notifier extension (SEMiNExt) during COVID-19-like disease outbreak (preprint)

Public health-related misinformation spread rapidly in online networks, particularly, in social media during any disease outbreak. Misinformation of coronavirus disease 2019 (COVID-19) drug protocol or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities are utilizing several surveillance tools to detect, and slow down the rapid misinformation spread online, still millions of misinformation are found online. However, there is no currently available tool for receiving real-time misinformation notification during online health or COVID-19 related inquiries. Our proposed novel combinational approach, where we have integrated machine learning techniques with novel search engine misinformation notifier extension (SEMiNExt), helps to understand which news or information is from unreliable sources in real-time. The extension filters the search results and shows notification beforehand; it is a new and unexplored approach to prevent the spread of misinformation. To validate the user query, SEMiNExt transfers the data to a machine learning algorithm or classifier which predicts the authenticity of the search inquiry and sends a binary decision as either true or false. The results show that the supervised learning algorithm works best when 80% of the data set have been used for training purpose. Also, 10-fold cross-validation demonstrate a maximum accuracy and F1-score of 84.3% and 84.1% respectively for the Decision Tree classifier while the K-nearest-neighbor (KNN) algorithm shows the least performance. The SEMiNExt approach has introduced the possibility to improve online health communication system by showing misinformation notifications in real-time which enables safer web-based searching while inquiring on health-related issues.