Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure.

Accurate prediction of solar wind density fluctuations is essential for space weather forecasting due to their significant impact on satellite operations, power grids, and communication systems. In this work, we present a novel, physics-free forecasting framework that relies exclusively on binary masks of solar surface active regions and historical solar wind density measurements at L1. Our architecture integrates a Graph Neural Network (GNN) to encode the topological structure of binary active region maps (derived from the SDO dataset provided by NASA) with the time series prediction power of a Long Short-Term Memory (LSTM) network for modeling the electron and proton densities at L1 (extracted from the OMNI dataset provided by NASA) from 2012 to 2014. Based on the graph representation of binary masks only, our model simplifies and lowers the number of parameters by a significant degree compared to conventional convolutional approaches, also surpassing their predictive power. Our model demonstrated superior performance over two CNN-based baselines (ConvLSTM and CNN-LSTM). The strength of our solution lies in the use of graph representations to preserve the spatial topology information that pixel-based methods tend to overlook. The results indicate that light topology-preserving models capable of delivering reliable solar wind density predictions are feasible, making it possible to have efficient onboard space weather warning systems.