People Worldwide Use Research Articles

P-717 Add-ons availability and quality of online information provided by Brazilian IVF clinics

Abstract Study question How do Brazilian fertility clinic websites advertise four IVF add-ons: assistedhatching (AH), preimplantation genetic testing for aneuploidies (PGT-A), ERA and sperm DNA fragmentation test. Summary answer Available information, about ivf add-ons in Brazil, on researched websites is heterogeneous and is not evidence-based according to the last published recommendations. What is known already Over 1 billion people worldwide use online resources to obtain health-related information. People struggling with infertility are increasingly turning to online resources for infertility-related content and social support. “Add-on” procedures are actively promoted on some fertility clinic websites as means to improve IVF success rates. However, the actual contribution of these interventions to IVF success rates remains debatable. ESHRE has recently issued 42 recommendations were formulated on the use of add-ons in IVF. At present, no study has evaluated the type and quality of the information provided on the Brazilian IVF clinics’ websites regarding the merits of “add-ons. Study design, size, duration Prospective evaluation of the availability and quality of information on four common add-ons provided by IVF clinics in the southeast of Brazil between October-November 2023. Clinics were identified using the national registry (SisEmbrio) as well as through manual online search. As only public online available information was used no ethical approval was necessary. Statistical analyses were restricted to descriptive of the frequency of each add-on and frequency of claims made in relation to their use. Participants/materials, setting, methods One reviewer took screenshots of the included clinic pages and claims and a second reviewer double-checked everything. The folowing information was colletced: availability of of the four add-ons; information on the effectiveness on: implanation rate(IR), pregnancy rate (PR) or live-birth rate (LBR); information on the scientifc uncertainty of the use of add-on and possible negative impact; for PGT-A if the clinic informed that the data obtained should be confirmed by prenatal diagnostic testing. Main results and the role of chance The websites of 122 IVF clinics were included. Nine clinics had no website or were offline. PGT-A was the most frequent available add-on (n = 77; 68%) but only two of them (2,5%) informed on PR. There was no information on IR nor LBR. Thirteen clinics (16,8%) acknowledged it could have adeverse effects but only one mentioned the scientifc uncertainty related to its use. Forty (35%) websites displayed AH with 7 reporting on possible negative effects but none disclosed success rates (PR, IR or LBR). Eight clinics admitted AH had uncertain benefits and seven noted possible negative impact. The sperm DNA fragmentation test was presented in 37 (33%) websites but none reported on potential adverse effects nor success rates in terms of PR, IR nor LBR. No website provided information on the uncertainty of the benefit of the test. The ERA-test was advertised in 28 (25%) websites with no information on its benefits on sucess rates. Three clinics advised it could have a negative impact and six agreed its effectiveness was unclear. The cost was never pesented for any of the add-ons researched in accordance to national regulations. Of note, 36 websites advertised other add-ond such as MSOME, ZYMOT and endometrial scrathcing. Limitations, reasons for caution The study was restricted to 122 clinics websites in the southeast of Brazil. Evalaution was restricted to four commons add-ons and the quality of the information provided could not be assessed as the clinics follow no specific regulation on how to advertise scientifc information to their patiens and general public. Wider implications of the findings The information provided by Brazilian IVF clinics could be misleading. The use of add-ons is surrounded by scientific uncertainly related to their benefits and cost-effectiveness therfeore it is of utmost importance that clinics reviewt heir websites and offer sound scientific data to patients and the general public as well. Trial registration number Not applicable

Opportunities and Challenges Associated with the Uptake of Residential Clean Fuel Usage.

Almost 3 billion people worldwide use solid fuel for cooking and heating. This review examines (i) household energy practices and infrastructures and their influence on fuel usage in different contexts; (ii) barriers in adoption of household clean energy technologies and uses in diverse settings and population groups and (iii) potential air pollution exposure reduction in homes through using processed fuel. Population health burden from solid fuel combustion-derived particulate air pollution has been estimated in several low- and middle-income countries. However, such studies have not been carried out in high income countries (e.g., UK). Irrespective of the region, fuel prices are the most dominant factor influencing the choice of fuel. Laboratory studies suggest processed fuel - pellets and briquettes - reduce particulate matter emissions by 70-80% and can be a promising alternative. Adoption of clean fuels for domestic energy needs facilitates progress towards five of the UN Sustainable Development Goals (SDGs). There is evidence that a variety of factors, including cost savings, encourage and hinder such uptake. These factors include price fluctuations, expenses, and the usage of clean fuels. Due to their distinct development scenarios, more expansive policy frameworks, and political economies of energy, these determinants are localized in character and differ significantly amongst economies. Therefore, in order to create innovative plans for the adoption of clean fuel use, strategies centred on local settings must be developed while keeping broad socio-technical and socio-economic issues in mind. Solid fuel processing - pelletization and briquetting - have the potential to reach Liquefied Petroleum Gas (LPG)-like emissions, and could be a potential strategy to mitigate exposure to household air pollution.

Review of Perspectives on Increasing Demand for "Cardamom Tincture Aromatic" in the Global Market

Abstract: Cardamom belongs to the family Zingiberaceae and is indigenous to India and Malesia. The crude drug has potential pharmacological applications such as antioxidant, analgesic, antibacterial antioxidant, and anti-inflammatory activities. Cardamom also contains bioactive constituents, including terpenoids, flavonoids, carbohydrates, essential oils, and carotenoids. The demand for herbal medicines has increased in most emerging nations, and their supply has steadily grown worldwide. Around 80% of people worldwide use herbal medicines to treat their illnesses. There are many dosage forms for herbal products, including tinctures, powders, liniments, lotions, capsules, balms, syrups, and tablets. "Herbal tincture" refers to concentrated herbal extracts with ethanol generally functioning as the solvent. Cardamom can be used as a tincture to extract its beneficial compounds and create a concentrated liquid form for various purposes. These are widely used as folk medicines and are even well-known in many medical systems, including Ayurveda and homeopathy. There are numerous herbal tinctures on the market, including those of tulsi, compound cardamom, and ginger. These preparations are simple to use and store. The current manuscript discusses several tincture preparation techniques, focusing on cardamom tincture and stability conditions, a market overview, and a few patents. : This article briefly overviews the global trading markets for herbal tinctures, particularly cardamom tinctures. Even the paper has explored the general information of cardamom and a market overview of the herbal tinctures.

Evaluation of the leaching of microplastics from discarded medical masks in aquatic environments: a case study of Mashhad city

The COVID-19 pandemic has led to a significant increase in the global use of face masks, with reports indicating that approximately 129 billion people worldwide use them every month. Many masks contain MPs, which can pose environmental and health risks. The aim of this study is to assess the properties of MPs that are released from ten different mask brands. The masks that were selected were weighed, immersed in deionized distilled water, stirred, and MPs that were released into the water were collected using a cellulose ester membrane. The collected MPs were then analyzed using an optical microscope to observe their shape and color. The results showed that the rates of MPs released from N95 masks, surgical masks, and 3D masks were 54, 23, and 23%, respectively. The N95 mask had the highest percentage of MPs due to its heavy weight. The observed shapes of MPs, in terms of abundance percentage, were filamentous > spherical > irregular > fragmented. Furthermore, the majority of MPs were found to be transparent or black in color. This study offers valuable insights into the mechanisms behind the release of MPs from disposable face masks, shedding light on the critical issue of microplastic pollution resulting from mask waste.

Negative psychological and physiological effects of social networking site use: The example of Facebook.

Social networking sites (SNS), with Facebook as a prominent example, have become an integral part of our daily lives and more than four billion people worldwide use SNS. However, the (over-)use of SNS also poses both psychological and physiological risks. In the present article, we review the scientific literature on the risk of Facebook (over-)use. Addressing this topic is critical because evidence indicates the development of problematic Facebook use ("Facebook addiction") due to excessive and uncontrolled use behavior with various psychological and physiological effects. We conducted a review to examine the scope, range, and nature of prior empirical research on the negative psychological and physiological effects of Facebook use. Our literature search process revealed a total of 232 papers showing that Facebook use is associated with eight major psychological effects (perceived anxiety, perceived depression, perceived loneliness, perceived eating disorders, perceived self-esteem, perceived life satisfaction, perceived insomnia, and perceived stress) and three physiological effects (physiological stress, human brain alteration, and affective experience state). The review also describes how Facebook use is associated with these effects and provides additional details on the reviewed literature, including research design, sample, age, and measures. Please note that the term "Facebook use" represents an umbrella term in the present work, and in the respective sections it will be made clear what kind of Facebook use is associated with a myriad of investigated psychological variables. Overall, findings indicate that certain kinds of Facebook use may come along with significant risks, both psychologically and physiologically. Based on our review, we also identify potential avenues for future research.

ChatGPT: Is This New AI Tool a Risk to Your Business?

It is difficult not to have heard about and/or be intrigued by the latest version of the Artificial intelligence (AI) software, ChatGPT, which since being launched in January 2023 has quickly become one of the most popular applications used by consumers. With its impressive abilities to write about a very wide range of topics and subject matters, now over 100 million people worldwide use ChatGPT on a daily basis. However, with increased use of this tool for work purposes, does your organization need to consider some rules/regulations around employee use of ChatGPT? Artificial Intelligence, Digital Transformation, Legal regulation, Technology

A Novel Color Visual Cryptography Approach Based on Harris Hawks Optimization Algorithm

Hundreds of millions of people worldwide use computing devices and services, including smartphones, laptops, and messaging apps. Visual cryptography (VC) is one of the most secure encryption methods for image encryption in many applications, such as voting security, online transaction security, and privacy protection. An essential step in VC is encrypting secret images into multiple digital shares to hide them with the intention of successfully reverting them to their original form. Hence, a single share cannot reveal information about the secret image. Issues including pixel enlargement, high processing costs, and low decryption quality influence the current state of VC. We address these issues by introducing a novel technique based on (2, 2) secret sharing and the algorithm of Harris hawks optimization (HHO) for color photos. For the encryption process, the appropriate color levels are determined using the HHO algorithm. Consequently, images are decrypted with improved quality and a small impact on the overall processing complexity. The suggested scheme is also non-expandable due to the equal size of the initial secret image and the shared images. This results in lower memory requirements and improved image quality. The approach is applied to a set of well-known benchmark images. Moreover, a set of standard metrics is used to assess the robustness of the proposed scheme, including its capability in defending against cryptanalytic attacks, a correlation, a histogram, and the quality of encryption. According to the findings, the proposed solution provides better reconstructed image quality, time-efficient encryption, and nearly optimal statistical properties compared to previous approaches.

Deep Convolutional Neural Networks Transfer Learning Comparison on Arabic Handwriting Recognition System

Around 27 languages and more than 420 million people worldwide use Arabic letters. That makes the Arabic language one of the most used languages. However, the Arabic language has a challenge, namely the difference in letters based on their position. Arabic handwriting recognition is important for various applications, such as education and communication. One example is during a pandemic when most education has turned digital, making recognizing students' Arabic handwriting difficult. This paper aims to create a model that can recognize Arabic handwriting by comparing several CNN architectures using transfer learning to classify Arabic, Hijja, and AHCD handwriting datasets. Transfer learning is a model that has been trained by previous datasets to other datasets and is suitable for use in models with small datasets because it can improve model accuracy even with small datasets. The datasets were split into 60%, 20%, and 20% for training, validation, and testing. Each model uses data augmentation and 50% dropout on a fully connected layer to reduce overfitting. Some of the CNN architectures used in this study to create Arabic writing recognition models are ResNet, DenseNet, VGG16, VGG19, InceptionV3, and MobileNet. The models were compiled and trained with various parameters. The best model achieved to classify AHCD and Hijja dataset is VGG16 with Adam optimizer and 0.0001 learning rate. Based on this research, it is expected to know the performance of the best model for classifying Arabic handwriting.

Use of Social Media for Patient Education in Dermatology: Narrative Review.

Social media has rapidly become one of the main avenues for news and communication among those with access to technology. Nearly 60% or 4.7 billion people worldwide use social media. Different social media networks provide users with a barrage of posts, opinions, and transformations. With this noticeable uptick in physician and patient education usage of social media, exploration of the impacts of social media on patient education in dermatology is crucial. The goal of this narrative review was to evaluate existing peer-reviewed literature examining the use of social media for patient education in dermatology and to establish trends and implications. Additional attention was given to different social media sites, and potential differences in modalities of posts such as short-form videos on TikTok and Instagram Reels, long-form videos on YouTube, and infographics on Twitter, Instagram, and Facebook. PubMed, Access DermatologyDxRx, and Scopus searches of peer-reviewed publications were performed to discover articles with social media and patient education keywords in combination with other health care-relevant or dermatology-relevant keywords. Subsequently, the screening of these studies was performed by the author who has experience with education and research experience in health care, dermatology, social media, and telehealth. Ultimately, the selected articles were summarized through qualitative analysis of key points and presented for further discussion. Through this narrative review, the researcher was able to identify several publications focusing on dermatology and social media. Some common subject areas included the use of social media for the promotion of private dermatology practices, residency programs, and research journals. So long as providers, such as dermatologists, take ethical considerations into account, these platforms can provide patients with curated educational content. In addition, several publications emphasized the use of social media as a form of patient education on dermatologic conditions but also as a source of misinformation. This narrative review illuminated the use of social media as a form of patient education for dermatology, with its applications addressed across many demographics and situations. As social media platforms continue to update their algorithms, content filters, and posts, social media may become a reputable form of patient education in dermatology. Future studies and innovations should continue to explore innovations in this space, the efficacy of different modalities of posts, and longitudinal differences in patient outcomes and health literacy.

Nowcasting of fecal coliform presence using an artificial neural network

At least 2 billion people worldwide use drinking water sources that are contaminated with feces, causing waterborne diseases; poor sanitation, poor hygiene, and unsafe drinking water result in a daily death rate of more than 800 children under 5 years of age from diarrheal diseases. This study shows the feasibility of a novel method to nowcast fecal coliforms' (FC) presence in drinking water sources by applying a multilayer perceptron artificial neuron network (MLP-ANN) model. The model gives a binary answer for FC presence or absence in drinking water sources using a minimum of water quality and geographical parameters, which can be monitored in real-time as predictors with low-cost and in-situ equipment. Using 51,400 samples to train, validate and test the model with temperature, pH, electrical conductivity, turbidity, dissolved oxygen, and total dissolved solids (TDS) as water-quality inputs and the water source type and location (as districts in India) as geographical inputs. The model achieved a total accuracy of 92.8% and a sensitivity of 98.2%, meaning that most FC-contaminated samples were classified correctly. In addition, precision reached 93.1%, meaning that most FC-contamination classifications were actually contaminated. The MLP-ANN performed better than the Linear Regression and K-Nearest Neighbors models, with lower accuracies of 90.2% and 91.0%, respectively. The MLP-ANN model could characterize the water quality geospatially, learn from the parameters whether the water is contaminated by FC, and predict with high accuracy on new testing data. This method can be used as a part of a sensor for FC monitoring and management in water, reducing the time gaps between routine lab testing and thus improving drinking water quality and addressing the SDG 6 targets.

Corner coil heating mode improves the matrix uniformity of cooked rice in an induction heating cooker.

Nowadays, an increasing number of people worldwide use induction heating cookers to cook rice for consumption. This work reveals the influence of induction heating cooker heating modes on the quality attributes of cooked rice. Three heating modes, including bottom coil heating mode (mode 1), corner coil heating mode (mode 2), and side coil heating (mode 3), were used. Among the three modes, mode 2 allowed for an intermediate heating rate during rice cooking. For mode 2, the minimized temperature difference between the upper layer (including the central upper layer and peripheral upper layer) and the lower layer (including the central lower layer and peripheral lower layer) can reduce the effect of water absorption time difference on rice quality. Consequently, the rice cooked using mode 2 exhibited improved matrix uniformity, as indicated by the similar moisture content (59.92-61.89%), hardness (15.87-18.24 N), and water mobility (the relaxation time and peak area of the third relaxation peak) of rice samples at four different positions in the pot. The rice cooked by mode 2 showed better texture appearance and a more uniform porous microstructure. Consistently, the cooked rice samples by mode 2 at different positions did not show substantial differences in their starch digestion features.

Ally: Understanding Text Messaging to Build a Better Onscreen Keyboard for Blind People

Millions of people worldwide use smartphones every day, but the standard issue QWERTY keyboard is poorly optimized for non-sighted input. In this article, we document the variety of methods blind people use to enter text into their smartphones, and focus on one particular need: sending text messages. We analyze two modern corpora of text messages and contrast them with an older text message corpus, as well as other corpora gathered from news articles, chat rooms, and books. We present a virtual keyboard for blind people optimized for sending text messages called Ally. To evaluate Ally, we conducted two user studies with blind participants. Our first study found increasing speeds and our second study found that half of participants reached comparable speeds to QWERTY, suggesting it may be a viable replacement. We conclude with a discussion of future work for non-sighted text-entry of text messages.

Clinical Potential of Himalayan Herb Bergenia ligulata: An Evidence-Based Study.

Herbal products have been used in traditional systems of medicine and by ethnic healers for ages to treat various diseases. Currently, it is estimated that about 80% of people worldwide use herbal traditional medicines against various ailments, partly due to easy accessibility and low cost, and the lower side effects they pose. Bergenia ligulata, a herb ranging from the Himalayas to the foothills, including the north-eastern states of India, has traditionally been used as a remedy against various diseases, most prominently kidney stones. The medicinal properties of B. ligulata have been attributed to bergenin, its most potent bioactive component. Apart from bergenin, the other compounds available in B. ligulata are arbutin, gallic acid, protocatechuic acid, chlorogenic acid, syringic acid, catechin, ferulic acid, afzelechin, paashaanolactone, caryophyllene, 1,8-cineole, β-eudesmol, stigmasterol, β-sitosterol, parasorbic acid, 3-methyl-2-buten-1-ol, phytol, terpinen-4-ol, tannic acid, isovalaric acid, avicularin, quercetin, reynoutrin, and sitoinoside I. This review summarizes various medicinal properties of the herb, along with providing deep insight into its bioactive molecules and their potential roles in the amelioration of human ailments. Additionally, the possible mechanism(s) of action of the herb’s anti-urolithiatic, antioxidative, antipyretic, anti-diabetic, anti-inflammatory and hepatoprotective properties are discussed. This comprehensive documentation will help researchers to better understand the medicinal uses of the herb. Further studies on B. ligulata can lead to the discovery of new drug(s) and therapeutics for various ailments.

Lung Effects of Household Air Pollution

Biomass fuel smoke, secondhand smoke, and oxides of nitrogen are common causes of household air pollution (HAP). Almost 2.4 billion people worldwide use solid fuels for cooking and heating, mostly in low- and middle-income countries. Wood combustion for household heating is also common in many areas of high-income countries, and minorities are particularly vulnerable. HAP in low- and middle-income countries is associated with asthma, acute respiratory tract infections in adults and children, chronic obstructive pulmonary disease, lung cancer, tuberculosis, and respiratory mortality. Although wood smoke exposure levels in high-income countries are typically lower than in lower-income countries, it is similarly associated with accelerated lung function decline, higher prevalence of airflow obstruction and chronic bronchitis, and higher all-cause and respiratory cause-specific mortality. Household air cleaners with high-efficiency particle filters have mixed effects on asthma and chronic obstructive pulmonary disease outcomes. Biomass fuel interventions in low-income countries include adding chimneys to cookstoves, improving biomass fuel combustion stoves, and switching fuel to liquid petroleum gas. Still, the impact on health outcomes is inconsistent. In high-income countries, strategies for reducing biomass fuel-related HAP are centered on community-level woodstove changeout programs, although the results are again inconsistent. In addition, initiatives to encourage home smoking bans have mixed success in households with children. Environmental solutions to reduce HAP have varying success in reducing pollutants and health problems. Improved understanding of indoor air quality factors and actions that prevent degradation or improve pollutedindoor air may lead to enhanced environmental health policies, but health outcomes must be rigorously examined.

PRODUCTION OF BIODIESEL FROM WASTE GROUNDNUT OIL USING CATALYZED TRANSESTERIFICATION REACTION TECHNIQUE.

Waste groundnut oil can be considered as a potential waste which can be utilized as energy source and raw material for chemical processes. People worldwide use edible oil for cooking which is discarded. The increase in the energy demand was typically provided by fossil fuel, which otherwise are limited and cause serious environmental concern. This study investigated the development of biodiesel from waste groundnut oil using 1.0g KOH catalyzed transesterification reaction technique, in the presence of different concentration of ethanol (80, 60 and 40ml) respectively to produce 180, 140 and 160ml biodiesel at 60oC temperature for 30minutes time. This study shows that production of biodiesel could be a suitable alternative green material to petrodiesel.

P10-07 m-Health apps and health-promoting behavior: medical students' opinion

BackgroundThe recent COVID-19 pandemic showed us that the use of digital health technologies could contribute and make health systems and services more effective. People worldwide use digital health solutions to protect their health and promote well-being. WHO recognizes digital health as key to realizing better access to health for people and an important part of the future of health. The aim of the survey is to study medical students attitude towards m-Health applications installed on their devices, assess their usage, and identify their relation to students sport habits. The research as well focused on medical students intention to apply m-Health apps into their clinical practice in order to improve the quality of services provided to patients.MethodsIn order to achieve the purpose of the study, an anonymous web based questionnaire was used among first year medical students in Medical University -Sofia in Bulgaria, and all the students participated voluntarily. Both the attitudes of the students towards m-Health application, and how sports habits are related with them were measured with closed questions. The study was conducted during January and February 2021. We used a descriptive statistical method to analyze the data.ResultsCompleted questionnaires were received from 133 students. Although the majority of the students (67%) have a positive attitude towards m-Health applications, only half of them (47%) believe that m-Health applications have affected (better and much better) their health-promoting behavior. Data about the use of m-Health applications into their future clinical practice shows that medical students will recommend these applications to their patients (60%), but less than half of them (39%) feel confident with the protection of their patients' personal data.ConclusionsThe study was conducted to explore medical students' opinion about m-Health applications and the related health-promoting behavior. The results shows that although they are keen in using, and recommending m-Health applications, they still do not trust the treatment of the personal data. It is thus recommended that more efforts should be in the direction of m-Health applications trustworthiness development. Great acknowledge, and guidelines in this area are important to ensure the wider use of m-Health applications.

Social comparison and problematic social media use: Relationships between five different social media platforms and three different social comparison constructs

Almost four billion people worldwide use social media platforms. For some individuals, the social rewards obtained on these sites can lead to problematic social media use (PSMU). Research attempting to understand social media use in general has found relationships with various types of social comparison, but no study has yet investigated PSMU with respect to specific platforms and type of social comparison. To address this, we conducted an online survey (n = 601) to assess how three different trait constructs of social comparison (comparison of abilities, comparison of opinions, comparison directionality) relate to the problematic use of five social media platforms (Facebook, Instagram, Snapchat, TikTok, and Twitter). While controlling for demographic characteristics and total number of social media platforms used, linear regression models revealed different associations between problematic use of each platform depending on trait social comparison type. Overall, comparison of abilities was positively related with PSMU of all five platforms, whereas comparison of opinions was negatively related with PSMU of only Facebook, Instagram, and Snapchat. However, social comparison directionality was not significantly related with PSMU. Results, limitations, and future directions are discussed.

Herb-Drug Interaction in Inflammatory Diseases: Review of Phytomedicine and Herbal Supplements.

Many people worldwide use plant preparations for medicinal purposes. Even in industrialized regions, such as Europe, where conventional therapies are accessible for the majority of patients, there is a growing interest in and usage of phytomedicine. Plant preparations are not only used as alternative treatment, but also combined with conventional drugs. These combinations deserve careful contemplation, as the complex mixtures of bioactive substances in plants show a potential for interactions. Induction of CYP enzymes and pGP by St John’s wort may be the most famous example, but there is much more to consider. In this review, we shed light on what is known about the interactions between botanicals and drugs, in order to make practitioners aware of potential drug-related problems. The main focus of the article is the treatment of inflammatory diseases, accompanied by plant preparations used in Europe. Several of the drugs we discuss here, as basal medication in chronic inflammatory diseases (e.g., methotrexate, janus kinase inhibitors), are also used as oral tumor therapeutics.

Impact of COVID-19 forecast visualizations on pandemic risk perceptions

People worldwide use SARS-CoV-2 (COVID-19) visualizations to make life and death decisions about pandemic risks. Understanding how these visualizations influence risk perceptions to improve pandemic communication is crucial. To examine how COVID-19 visualizations influence risk perception, we conducted two experiments online in October and December of 2020 (N = 2549) where we presented participants with 34 visualization techniques (available at the time of publication on the CDC’s website) of the same COVID-19 mortality data. We found that visualizing data using a cumulative scale consistently led to participants believing that they and others were at more risk than before viewing the visualizations. In contrast, visualizing the same data with a weekly incident scale led to variable changes in risk perceptions. Further, uncertainty forecast visualizations also affected risk perceptions, with visualizations showing six or more models increasing risk estimates more than the others tested. Differences between COVID-19 visualizations of the same data produce different risk perceptions, fundamentally changing viewers’ interpretation of information.

COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques.

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.