Aspect-based classification of vaccine misinformation: a spatiotemporal analysis using Twitter chatter

Abstract Background The spread of misinformation of all types threatens people’s safety and interrupts resolutions. COVID-19 vaccination has been a widely discussed topic on social media platforms with numerous misleading and fallacious information. This false information has a critical impact on the safety of society as it prevents many people from taking the vaccine, decelerating the world’s ability to go back to normal. Therefore, it is vital to analyze the content shared on social media platforms, detect misinformation, identify aspects of misinformation, and efficiently represent related statistics to combat the spread of misleading information about the vaccine. This paper aims to support stakeholders in decision-making by providing solid and current insights into the spatiotemporal progression of the common misinformation aspects of the various available vaccines. Methods Approximately 3800 tweets were annotated into four expert-verified aspects of vaccine misinformation obtained from reliable medical resources. Next, an Aspect-based Misinformation Analysis Framework was designed using the Light Gradient Boosting Machine (LightGBM) model, which is one of the most advanced, fast, and efficient machine learning models to date. Based on this dataset, spatiotemporal statistical analysis was performed to infer insights into the progression of aspects of vaccine misinformation among the public. Finally, the Pearson correlation coefficient and p-values are calculated for the global misinformation count against the vaccination counts of 43 countries from December 2020 until July 2021. Results The optimized classification per class (i.e., per an aspect of misinformation) accuracy was 87.4%, 92.7%, 80.1%, and 82.5% for the “Vaccine Constituent,” “Adverse Effects,” “Agenda,” “Efficacy and Clinical Trials” aspects, respectively. The model achieved an Area Under the ROC Curve (AUC) of 90.3% and 89.6% for validation and testing, respectively, which indicates the reliability of the proposed framework in detecting aspects of vaccine misinformation on Twitter. The correlation analysis shows that 37% of the countries addressed in this study were negatively affected by the spread of misinformation on Twitter resulting in reduced number of administered vaccines during the same timeframe. Conclusions Twitter is a rich source of insight on the progression of vaccine misinformation among the public. Machine Learning models, such as LightGBM, are efficient for multi-class classification and proved reliable in classifying vaccine misinformation aspects even with limited samples in social media datasets

Sulfated exopolysaccharides from Porphyridium cruentum: A useful strategy to extend the shelf life of minced beef meat

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A Review on Worldwide Ephedra History and Story: From Fossils to Natural Products Mass Spectroscopy Characterization and Biopharmacotherapy Potential

Growing worldwide, the genus Ephedra (family Ephedraceae) had a medicinal, ecological, and economic value. The extraordinary morphological diversity suggests that Ephedra was survivor of an ancient group, and its antiquity is also supported by fossil data. It has recently been suggested that Ephedra appeared 8–32 million years ago, and a few megafossils document its presence in the Early Cretaceous. Recently, the high analytical power provided by the new mass spectrometry (MS) instruments is making the characterization of Ephedra metabolites more feasible, such as ephedrine series. In this regard, the chemical compounds isolated from crude extracts, fractions, and few isolated compounds of Ephedra species were characterized by MS-based techniques (LC-MS, LC-ESI-MS, HPLC-PDA-ESI/MS, LC-DAD-ESI/MSn, LC/Orbitrap MS, etc.). Moreover, we carry out an exhaustive review of the scientific literature on biomedicine and pharmacotherapy (anticancer, antiproliferative, anti-inflammatory, antidiabetic, antihyperlipidemic, antiarthritic, and anti-influenza activities; proapoptotic and cytotoxic potential; and so on). Equally, antimicrobial and antioxidant activities were discussed. This review is focused on all these topics, along with current studies published in the last 5 years (2015–2019) providing in-depth information for readers