Fake News Data Exploration and Analytics

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models;random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

Medical Staff Work Burnout and Willingness to Work during COVID-19 Pandemic Situation in Pakistan.

Medical staff is vital for helping society through a health crisis, human-made or natural disaster, and pandemic. This study aims to investigate the medical staff's work-related burnout during Covid-19 and their willingness to work when they are most needed. The cross-sectional design was used, and an online survey was conducted through snowball sampling. Sample comprised on 250 participants (male = 89 & female= 161). The study's inclusion criteria were that only those medical staff of different hospitals was approached to collect data performed inwards isolated for Covid-19 treatment corona isolation wards. Maslach burnout inventory (MBI-HSS) and willingness to work (WTW) tools were used to collect data. Descriptive and Partial least square analysis was utilized to evaluate the relationships. The Coefficient of determination or R-Square value was 0.299, which means 29.9% or 30% of the work burnout variation was due to the impact of emotional exhaustion and personal accomplishment. Perceived danger, Role Competence, Self-Efficacy, and Sense of duty significantly impacted the willingness to work. Despite the workload and perceived risk, 42.6% of participants agreed to work if their department had to need their services, while 55.2% of participants agreed to work whether their department asked them or not. Government and hospital management should adopt a proactive and positive response during the pandemic to eradicate the employee stress and adopt adequate steps to improve the willingness to work with medical staff.