Health Misinformation in the Covid-19 Era - Detecting Misinformation on Bi-lingual Corpora using Lexical Features

Social media use spiked amid the COVID-19 pandemic, resulting in an increase in fake news proliferation, especially health misinformation. Many misinformation detection studies have primarily focused on English texts, and of these, very few have examined linguistic features (syntactic, lexical, and semantic). Lexical features such as number of upper-case letters have been shown to improve misinformation detection in English and non-English texts, however, use of lexical features is still in its infancy, and thus warrants further investigation. Therefore, a novel lexical-based health misinformation detection model is proposed using machine learning techniques, specifically focusing on two languages, namely, English, and standard Malay. A new dataset containing fake and real news were developed from a fact- checking portal and local media, targeting news related to COVID-19. Common natural language processing tasks including filtering, tokenization, stemming etc. and lexical feature extraction were administered prior to data modelling. Evaluation on a dataset containing 1060 fake and real news each show Random Forest to yield the best performance with 99.6% for F-measure and accuracy of 96%, followed closely by Support Vector Machine. A similar observation was noted for the Malay corpus. Improved health misinformation detection was observed when linguistic features were included as part of the model, hence implying that the features can be successfully used in detecting fake news. © 2022 IEEE.

SentiMage: A Sentiment-Image-based COVID-19 Health Misinformation Detection using Machine Learning

The rapid dissemination of misinformation (generally known as fake news) has become worrisome, especially during the on-going COVID-19 pandemic both globally, and locally. In fact, the proliferation of health-related misinformation intensified on social media, which many experts believe is contributing to the threats of the pandemic. Sentiment has been shown to improve detection mechanisms in various social media related studies, however this aspect is under-researched in the context of health misinformation. Further, metadata such as location or image that constitute part of real and fake news were not fully explored as well. This study develops a health misinformation detection model using machine learning algorithms, and further assesses the impact of sentiment and image on the model performance. Local data gathered from a fact-checking portal were pre-processed, translated, and used to train the detection model. Evaluation results show Support Vector Machine to yield the best performance with 99.4% for F-measure and accuracy of 99.1%, followed closely by Random Forest when sentiment was included, however, the presence of image was not found to significantly improve health misinformation detection. © 2022 IEEE.

Staphylococcus Aureus Infection Risk in Intensive Care Unit Patients during the Covid-19 Pandemic

INTRODUCTION: Although Staphylococcus aureus is known to be a poor prognostic factor in coronavirus disease of 2019 (SARS-CoV-2 or COVID-19), it is unclear if COVID-19 increases the risk of S. aureus infections. The purpose of this study is to give healthcare providers a better understanding of the pharmacological risk factors that may predispose patients to a S. aureus co-infection in COVID-19 positive patients. METHOD(S): This retrospective chart review included adult patients treated at a Spectrum Health medical or cardiothoracic ICU between October 2020 and November 2021. To be included in the exposure arm of the analysis, patients had to have a positive culture for S. aureus. A chi-square analysis was utilized for the primary outcome while a logistic regression was used to uncover possible risk factors for S. aureus in COVID-19 patients. Overall, S. aureus infections were compared between patients with and without COVID-19 with a secondary analysis that was done for patients who had been treated with tocilizumab or dexamethasone. RESULT(S): A total of 406 patients were included;96 patients were positive for S. aureus, and 310 patients remained negative throughout their admission. COVID-19 patients were more likely to acquire a S. aureus infection than their COVID-19 negative counterparts (p < 0.0001). Neither tocilizumab nor dexamethasone use were statistically significant in increasing risk of S. aureus co-infection. CONCLUSION(S): COVID-19 patients are more likely to acquire S. aureus infections than their COVID-19 negative counterparts. Dexamethasone and tocilizumab use were not associated with increased incidence of S. aureus infections in COVID-19 patients.

Machine Learning Models for Predicting Financially Vigilant Low-Income Households

The COVID-19 pandemic has adversely affected households’lives in terms of social and economic factors across the world. The Malaysian government has devised a number of stimulus packages to combat the pandemic’s effects. Stimulus packages would be insufficient to alleviate household financial burdens if they did not target those most affected by lockdowns. As a result, assessing household financial vigilance in the case of crisis like the COVID-19 pandemic is crucial. This study aimed to develop machine learning models for predicting and profiling financially vigilant households. The Special Survey on the Economic Effects of Covid-19 and Individual Round 1 provided secondary data for this study. As a research methodology, a cross-industry standard process for data mining is followed. Five machine learning algorithms were used to build predictive models. Among all, Gradient Boosted Tree was identified as the best predictive model based on F-score measure. The findings showed machine learning approach can provide a robust model to predict households’financial vigilances, and this information might be used to build appropriate and effective economic stimulus packages in the future. Researchers, academics and policymakers in the field of household finance can use these recommendations to help them leverage machine learning. Author

Coronavirus Stress: Stressors and Socio-demographic Correlates among Malaysian Women in the Midst of Lockdown

This study identifies the stressors and socio-demographic correlates among Malaysian women amid the COVID-19 pandemic nationwide lockdown (March 2020-June 2020). An online questionnaire adapted from previous studies, the Distress Anxiety Stress Scale-21 and the Multidimensional Scale of Perceived Social Support scale was used to solicit data, resulting in 1,793 women. Stress prevalence was observed among 24% (N = 430) of the women (M = 11.08;SD = 8.25). Structured equation modelling revealed Marital Issues (ß = 0.091), Financial Issues (ß = 0.091), Emotion (ß = 0.340), Working from Home (ß = 0.122) and Social Support (ß=-0.094) to collectively account for 25% of the women's stress, with all path coefficients being significant at p < 0.05. Further, significant differences were noted for age and monthly total household income, with younger women and those with lower incomes experiencing more stress than their counterparts. The study and its findings provide a strong basis for relevant authorities including mental health professionals and policymakers to devise specific measures to help this vulnerable cohort to cope psychologically better. © 2021 IEEE.