Comparison of Different Machine Learning Methods to Detect Fake News

Information content that is inaccurate, misleading, or whose source cannot be verified is fake news. This content could be created to purposely harm people’s reputations, deceive them, or draw attention to themselves. Since December 2019, the epidemic of coronavirus disease has sparked considerable alarm and has had a significant impact on people’s lives. Also, misinformation on COVID-19 is frequently spread on social media. This project aims to use Machine learning algorithms to recognize fraudulent news. For this, we use seven essential algorithms, namely Logistic regression, Naïve Bayes, Support Vector Machine (SVM), Neural Network (NN), K-Nearest Neighbours (KNN), Decision tree, and Random forest. We compared the results of all the algorithms stated above and found that neural networks and random forest achieved the highest accuracy of 83%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SARS-CoV-2 Seroprevalence among Health Care Workers-A Voluntary Screening Study in a Regional Medical Center in Southern Germany

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is associated with a potentially severe clinical manifestation, coronavirus disease 2019 (COVID-19), and currently poses a worldwide challenge. Health care workers (HCWs) are at the forefront of any health care system and thus especially at risk for SARS-CoV-2 infection due to their potentially frequent and close contact with patients suffering from COVID-19. Serum samples from 198 HCWs with direct patient contact of a regional medical center and several outpatient facilities were collected during the early phase of the pandemic (April 2020) and tested for SARS-CoV-2-specific antibodies. Commercially available IgA- and IgG-specific ELISAs were used as screening technique, followed by an in-house neutralization assay for confirmation. Neutralizing SARS-CoV-2-specific antibodies were detected in seven of 198 (3.5%) tested HCWs. There was no significant difference in seroprevalence between the regional medical center (3.4%) and the outpatient institution (5%). The overall seroprevalence of neutralizing SARS-CoV-2-specific antibodies in HCWs in both a large regional medical center and a small outpatient institution was low (3.5%) at the beginning of April 2020. The findings may indicate that the timely implemented preventive measures (strict hygiene protocols, personal protective equipment) were effective to protect from transmission of an airborne virus when only limited information on the pathogen was available.