The current study used the explanatory position to distinguish the relationship between cultural socialization and mental health, considering several variables for the members of the study community, who are students at A’Sharqiyah University. The sample consisted of 800 students, 435 female students, 365 male students. The current study includes five colleges of the university, namely the College of Arts and Humanities, the College of Engineering, the College of Law, the College of Business Administration, and College of Applied and Health Science and the university includes various academic degrees, namely the diploma, the bachelor’s, Master’s, for the academic year 2021/2022. The researcher used the Cultural Socialization Behaviors Measure scale (CSBM) Derlan et al (2016) and the Warwick-Edinburgh Mental Wellbeing scale (WEMWBS) Brown and Platt (2007). To answer the research question the researchers used Mean and standard deviation, and to analyze the data it has been using T. test and ANOVA , the results show there are correlation between cultural socialization and mental health, in addition, the results indicate a noteworthy difference between male and female students in terms of cultural socialization and mental health which means this finding interpret the females are more socially and they have a more positive health pattern than males. The researcher tested the psychometric properties of the scales used in the current study, and in order to answer the three questions of the study, the following mathematical statistics were used; Pearson’s correlation coefficient, T-test, one-way ANOVA, LSD test, and regression analysis. The results of the study concluded that there is a relationship between cultural socialization and mental and psychological health, in addition to the presence of indications that there is a difference in favor of female students in cultural socialization and its impact on mental and psychological health. As for the majors offered at the university, the study did not find an impact on mental health.

Fake news existed ever since there was news, from rumors to printed media then radio and television. Recently, the information age, with its communications and Internet breakthroughs, exacerbated the spread of fake news. Additionally, aside from e-Commerce, the current Internet economy is dependent on advertisements, views and clicks, which prompted many developers to bait the end users to click links or ads. Consequently, the wild spread of fake news through social media networks has impacted real world issues from elections to 5G adoption and the handling of the Covid-19 pandemic. Efforts to detect and thwart fake news has been there since the advent of fake news, from fact checkers to artificial intelligence-based detectors. Solutions are still evolving as more sophisticated techniques are employed by fake news propagators. In this paper, R code have been used to study and visualize a modern fake news dataset. We use clustering, classification, correlation and various plots to analyze and present the data. The experiments show high efficiency of classifiers in telling apart real from fake news.