social media became a fertile soil for various threats, extremism, and radicalization. This challenged policy-makers, researchers and practitioners. Preventing such extreme activities from happening becomes an ultimate priority at local and global scale. This paper introduces a new intertwine between radicalization and natural language processing capable of estimating the risk score of individuals based on their social media activities. The system uses a hybridized ERG22+ and VERA-ER model, which classifies individuals as high or low risk radicalization profile. The developed system was tested and validated on the Video Comments Threat Corpus dataset and Twitter pro-ISIS fanboys datasets where it achieves 95.1% and 64.9% accuracy, respectively.

A comprehensive investigation on the variation of the Covid-19 pandemic adequate reproduction number (R) in four critical European countries is provided in this study as a function of the variations related to the weather, mobility, government responses, and epidemiology. In this study, an open data set about the Covid-19 pandemic is used for the analysis. The data contains newly recorded components with different metrics since the appearance of the COVID19 pandemic. The aim is to investigate the impact of the weather, mobility, and government restriction on the R values and pandemic. The appropriate statistical analyses are used to reveal the association and the effect of the various attributes to the R. In addition, multiple predictive models were applied. The results show significant differences between studied countries and have different factors that influence the virus’s spread.

The paper built on First Impression Challenge from Chalearn V2 Workshop on Explainable Computer Vision Multimedia and Job Candidate Screening Competition CVPR17 by focusing solely on Textual Input in contrast to other Challenge’s participants who considered video or audio modalities. Therefore, the paper aims to develop a new deep learning architecture capable of predicting human personality traits and job interview from the video transcripts. Several feature representations that involve statistical and deep learning have contrasted. Our approach achieved the best score when text modality alone were employed, yielding an average of 89% score in human personality traits and 89.10% value for job interview. The research results will help companies and other organization studying human personality to assess a human personality using a minimum textual resources from the job candidates