Buchanan Covington (packetsushi58)

Online social media (OSM) has emerged as a prominent platform for debate on a wide range of issues. Even celebrities and public figures often share their opinions on a variety of topics through OSM platforms. One such subject that has gained a lot of coverage on Twitter is the Novel Coronavirus, officially known as COVID-19, which has become a pandemic and has sparked a crisis in human history. In this study, we examine 29 million tweets over three months to study highly influential users, whom we refer to as leaders. We recognize these leaders through social network techniques and analyse their tweets using text analysis. Using a community detection algorithm, we categorize these leaders into four clusters research, news, health, and politics, with each cluster containing Twitter handles (accounts) of individual users or organizations. e.g., the health cluster includes the World Health Organization (@WHO), the Director-General of WHO (@DrTedros), and so on. The emotion analysis reveals that (i) all clusters show an equal amount of fear in their tweets, (ii) research and news clusters display more sadness than others, and (iii) health and politics clusters are attempting to win public trust. According to the text analysis, the (i) research cluster is more concerned with recognizing symptoms and the development of vaccination; (ii) news and politics clusters are mostly concerned with travel. We then show that we can use our findings to classify tweets into clusters with a score of 96% AUC ROC. The COVID-19 pandemic constitutes a global mental health challenge that has disrupted the lives of millions of people, with a considerable effect on university students. The aim of this study was to assess the feasibility of a brief online Mindfulness and Compassion-based Intervention to promote mental health among first year university students during COVID-19 home confinement. Participants ( =66) were first-year psychology students from a university in Spain with no prior meditation experience. Intervention lasted for 16 days and was designed ad-hoc. Using a pre-post within-subjects design, feasibility was assessed in five domains (acceptability, satisfaction, implementation, practicality, and limited efficacy testing). Participants completed both baseline and post-intervention assessments of perceived stress, anxiety, and self-compassion. The intervention showed to be feasible in all domains evaluated. It was implemented as planned with constrained resources, and limited efficacy testing showed promalth burden derived from the COVID-19 pandemic.Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.To improve the performance of local learned descriptors, many researchers pay primary attention to the triplet loss network. As expected, it is useful to achieve state-of-the-art performa