Longitudinal Sentiment Topic Modelling of Reddit Posts (preprint)

In this study, we analyze texts of Reddit posts written by students of four major Canadian universities. We gauge the emotional tone and uncover prevailing themes and discussions through longitudinal topic modeling of posts textual data. Our study focuses on four years, 2020-2023, covering COVID-19 pandemic and after pandemic years. Our results highlight a gradual uptick in discussions related to mental health.

MULTISYSTEM INFLAMMATORY SYNDROME IN CHILDREN COURSE AMID COVID-19 PANDEMIC IN THE REBUBLIC OF BELARUS (preprint)

Resume. In first two years of COVID-19 pandemic, children usually had a mild or asymptomatic form of the disease. However, in rare cases, after suffering COVID-19, children had clinical manifestations similar to incomplete Kawasaki Disease (CD) or toxic shock syndrome. This condition is known as multisystem inflammatory syndrome in children (MIS-C). The purpose of this research was to study clinical and laboratory features and outcomes of multisystem inflammatory syndrome in children who were hospitalized during COVID-19 pandemic. Materials and methods. In 19 months (May 2020 - December 2021) 63 patients with a diagnosis of Multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19 were observed in the departments of Anesthesiology and Intensive Care of the Healthcare Institution "City Children's Infectious Clinical Hospital" in Minsk, Republic of Belarus. MIS-C was diagnosed on criteria of CDC/WHO, 2020. All calculations were carried out in the statistical package R, version 4.1. The results of the analysis were considered statistically significant with p<0,05. The results of the study. All of 63 children with MIS-C didnt have an acute coronavirus infection. Therefore, it was impossible to determine which strain of SARS-CoV-2 patient exactly had. However, we formed 3 groups of patients based on circulation of the dominant strain of SARS-CoV-2 in Belarus at different times. The 1st group included 40 patients (63,5%) received treatment from 05.25.2020 to 02.21.2021 ("wuhan strains"); the 2nd group - 9 children (14,3%) from 02.23.2021 to 06.13.2021 ("alpha"); the 3rd group - 14 children (22,2%) from 07.01.2021 to 11.19.2021 ("delta"). 47 (74,6%) patients had complete and incomplete Kawasaki Disease phenotype of MIS-C. Nonspecific phenotype was observed in 16 (25,4%) children. It manifested as signs of shock. The mean age didnt differ in study groups. All children had hyperthermic syndrome. Fever reached febrile numbers 3-4 times a day. Average fever duration was 3,2 [1-15] days. The course of MIS-C in children also didnt depend on the circulating strain of the virus. For instance, gastrointestinal dysfunction was observed in all three groups with equal frequency (73%, 78% and 57%, respectively). The only a statistically significant increase was in the number of children with cheilitis. In the 2nd group 8 children (89%) and the 3rd group 13 children (93%) had cheilitis, respectively, p=0,002. Neurological disorders such as headache, hyperesthesia, hallucinations, photophobia were more often observed in the 1st group of children - 19 (48%) cases and less frequently in the 2nd and 3rd group (in 11% and 14% of cases), p=0,022. Pathological blood flow regurgitation was the most common disorder (68-71%). Several biochemical markers of inflammation levels, such as C-reactive protein (CRP) and procalcitonin (PCT), were high. CRP levels were 162 mg/l [130; 245]; 130 mg/l [90; 160]; 130 mg/l [106; 149] in 3 study groups, respectively. In children of the 1st group CRP level was significantly higher, p=0.052. PCT level was higher in patients of the 3rd group (4.2 ng/ml [2,4; 8,8]; 3.9 ng/ml [3,2; 11,9]; 8.7 ng/ml [3,4; 14,1], p=0.625). Conclusion. As a result of the research there wasnt found notable connection between clinical or laboratory features of MIS-C and the dominant circulating strain of SARS-CoV-2 in given time periods. During the circulation of "alpha" and "delta" strains, the only significant differences were decrease of the number of patients with neurological disorders and increase in the frequency of cheilitis, p=0,002. The remaining indicators of organ dysfunction were similar in three groups of children. There was 1 (1,6%) fatal outcome in our study.

Sentiment Analysis of Covid-related Reddits (preprint)

This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit. We apply manual annotation and three Machine Learning algorithms to analyze sentiments conveyed in those messages. We use VADER and TextBlob to label messages for Machine Learning experiments. Our results show that removal of shortest and longest messages improves VADER and TextBlob agreement on positive sentiments and F-score of sentiment classification by all the three algorithms

Sentiment Analysis of the COVID-related r/Depression Posts (preprint)

Reddit.com is a popular social media platform among young people. Reddit users share their stories to seek support from other users, especially during the Covid-19 pandemic. Messages posted on Reddit and their content have provided researchers with opportunity to analyze public concerns. In this study, we analyzed sentiments of COVID-related messages posted on r/Depression. Our study poses the following questions: a) What are the common topics that the Reddit users discuss? b) Can we use these topics to classify sentiments of the posts? c) What matters concern people more during the pandemic? Key Words: Sentiment Classification, Depression, COVID-19, Reddit, LDA, BERT

Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data (preprint)

Application of Machine Learning algorithms to the medical domain is an emerging trend that helps to advance medical knowledge. At the same time, there is a significant a lack of explainable studies that promote informed, transparent, and interpretable use of Machine Learning algorithms. In this paper, we present explainable multi-class classification of the Covid-19 mental health data. In Machine Learning study, we aim to find the potential factors to influence a personal mental health during the Covid-19 pandemic. We found that Random Forest (RF) and Gradient Boosting (GB) have scored the highest accuracy of 68.08% and 68.19% respectively, with LIME prediction accuracy 65.5% for RF and 61.8% for GB. We then compare a Post-hoc system (Local Interpretable Model-Agnostic Explanations, or LIME) and an Ante-hoc system (Gini Importance) in their ability to explain the obtained Machine Learning results. To the best of these authors knowledge, our study is the first explainable Machine Learning study of the mental health data collected during Covid-19 pandemics.