ABSTRACT
Twitter has become a popular platform to receive daily updates. The more the people rely on it, the more critical it becomes to get genuine information out. False information can easily be shared on Twitter, which influences people's feelings, especially if fake information is linked to COVID-19. Therefore, it is of utmost importance to detect fake information before it becomes uncontrollable. Real-time tweets were used as part of this study. A few features like tweet's text, sentiment etc., were extracted and analyzed. The project returns a set of statistics determining the tweet's veracity. In this study, various classifiers have been used to see which of them works best with the proposed model in classifying the used dataset. The proposed model achieved the best accuracy of 84.54% and the highest F1-score of 0.842 with Random Forest. With careful analysis while feature selection and using few features, the model developed is equivalent in performance to the other models that use a lot of features. This confirms that the model developed is less complex and highly dependable.
ABSTRACT
Background: Coronavirus disease-2019 (COVID-19) pandemic has been a cause of significant mental health disturbances in medical health personnel. However, 18 months into the pandemic, healthcare workers (HCWs) have become accustomed to the heightened stress and anxiety that comes with caring for COVID patients. Through this study, we aim to measure depression, anxiety, stress, and insomnia in doctors with the help of validated scales. Materials and methods: This was a cross-sectional study with an online survey design conducted among doctors from major hospitals in New Delhi. The questionnaire included participant demographics, including designation, specialty, marital status, and living arrangements. This was followed by questions from the validated depression, anxiety, stress scale (DASS-21), and insomnia severity index (ISI). Depression, anxiety, stress, and insomnia scores were calculated for each participant, and the data were analyzed statistically. Results: The mean scores of the whole study population showed no depression, moderate anxiety, mild stress, and subthreshold insomnia. Female doctors exhibited more psychological issues (mild depression and stress, moderate anxiety, but only subthreshold insomnia) as compared to males (mild anxiety, but no depression, stress, and insomnia). Junior doctors also had higher depression, anxiety, and stress scores than senior doctors. Similarly, single doctors, those living alone, and those not having kids had higher DASS and insomnia scores. Discussion: HCWs have been under tremendous mental stress during this pandemic which is influenced by multiple factors. Female sex, junior doctors, working on the frontline, not being in a relationship, and living alone may be some of the factors recognized in our study and corroborated by many authors, which may increase the chances of depression, anxiety, and stress in them. HCWs need regular counseling, time off for rejuvenation, and social support to overcome this hurdle. How to cite this article: Kohli S, Diwan S, Kumar A, Kohli S, Aggarwal S, Sood A, et al. Depression, Anxiety, Stress, and Insomnia amongst COVID Warriors across Several Hospitals after Second Wave: Have We Acclimatized? A Cross-sectional Survey. Indian J Crit Care Med 2022;26(7):825-832.
ABSTRACT
As the world facing COVID-19 crisis during 2020, more than 38.8 lakhs of human death as per the report from the World Health Organization (WHO) on June 2021. Because of this pandemic, worldwide people are restricted to travel, meeting, or gathering a group of people. Most of the people working from home and students' education are carried out through the online system. The stress caused in a person may depends upon different factors such as spending more time in online classes by watching the mobile phone, due to lack of interaction with others, insufficient money to survive, job insecurity, and other reasons. This kind of above reason may lead to a long-term stress that leads to suicidal thoughts in human. In order to reduce the suicide rate, it is important to analyze the level of stress in human beings. In this paperwork, a survey for stress measurement among human beings was carried out by two methods such as Perceived Stress Scale (PSS) and also by using the physiological signal as Electroencephalogram (EEG) discussed. The advantages and drawbacks of the PSS method and EEG signal method for the detection of stress measurement are discussed in this paper. © The Electrochemical Society
ABSTRACT
The Hashtags plays a vital role in the social media and it is easily highlighted by each and every people when they tag it for their own views. Marketing and advertisement is booming so that to make their products work through the views of the normal or common people. Sometimes they use the false content for their publicity and misleading the people. In this paper, the covid19 tweets are taken for finding out the popular hashtags using the correlation techniques like pearson, spearman and kendall rank correlation. The Covid19 hashtag is more popular with the correlation coefficient and sentimental analysis of the tweet than coronovirus tag. To justify the popularity, the weightages of the hashtag is found out by applying the topic modeling. In that the coronovirus tag is having more weightage than Covid19 tag.
ABSTRACT
With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure's (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.