s not it deplorable that in a country that tops in the entire world in using several social media sites does not utilize the same media in acquiring knowledge and skills? In Saudi Arabia, undergraduate students spend a significant amount of time on social media every day, but they are reluctant (or not motivated enough) to use the same media for educational purposes. This study was carried out on the undergraduate English majors of King Khalid University in Muhayil Asir in Saudi Arabia. In the English department, every student carries at least one smart phone with Internet connection, and they are found occupied with their phones on the campus, sometimes even in classrooms, but they are weak both in subject knowledge and skills of English language. The teachers-cum-researchers were baffled with students’ competence because regular users of Internet and social media are supposed to be updated with the subject knowledge as well as confident in using English language. The researchers designed an empirical study to explore students’ rationale of using the social media and their language preference. The study concludes with gloomy findings that students use the media mainly for entertainment and ineffective communication in English language. The worst fact is: they are not motivated enough to use the social media for educational purposes.

To investigate the effectiveness of e-learning by using a particular mobile application, namely WhatsApp, an empirical study was conducted on sixty undergraduate English language majors at King Khalid University in Saudi Arabia. The objective of the study was to determine whether the levels of motivation, content knowledge and grades of the students (who took the course “Syntax”)-, developed after receiving additional support through WhatsApp apart from traditional classroom lectures. The results showed that the experimental group that got extra support from fellow students and the course teachers through WhatsApp outperformed the students of the control group who studied the course only through traditional method. Moreover, the gap of success rate between the experimental group and the control group is about eighty nine percent with zero failure in the experimental group. The study proved that WhatsApp can be effectively used for providing supplementary support to motivate students to study properly and to get higher grades.

Due to the rapid increase in the exchange of text information via internet networks, the security and the reliability of digital content have become a major research issue. The main challenges faced by researchers are authentication, integrity verification, and tampering detection of the digital contents. In this paper, a Robust English Text Watermarking and Natural Language Processing Approach (RETWNLPA) is proposed based on word mechanism and first level order of Markov model to improve the accuracy of tampering detection of sensitive English text. The RETWNLPA approach embeds and detects the watermark logically without altering the original text document. Based on the hidden Markov model (HMM), the first-level order of word mechanism is used to analyze the interrelationship between English text. The extracted features are used as watermark information and integrated with text zero-watermarking techniques. To detect eventual tampering, RETWNLPA has been implemented and validated with attacked English text. Experiments were performed on four datasets of varying sizes under random locations of common tampering attacks. The simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks. Comparison results show that RETWNLPA outperforms baseline approaches HNLPZWA (an intelligent hybrid of natural language processing and zero-watermarking approach) and ZWAFWMMM (Zero-Watermarking Approach based on Fourth level order of Word Mechanism of Markov Model) in terms of tampering detection accuracy.

The present cross-sectional empirical study investigates the different types of strategies and methods that the undergraduate students employ when translating from their native language into the target language and vice versa. The study was conducted on one hundred twenty, third and fourth year, students at the College of Science and Arts, King Khalid University. The data were collected through translation tasks and questionnaires. Both qualitative and quantitative methods were used to analyze and interpret the data collected to achieve the objectives of this study. The study revealed valuable information. The most favored strategies by Arab college students were literal translation, free translation and word-for-word translation respectively. More than half of the all used strategies were literal translation with a percentage of about fifty-five. The mixed translation strategies were found to be about twenty one percent for all the three levels. Free translation strategy was only fourteen percent which is, somehow, a low percentage. The students showed considerable improvement as they progress from one level to a higher one. It is expected that translation instructors as well as course designers will reflect on the findings of this study by raising the learners' awareness of the great differences between English and Arabic when teaching or designing translation courses. Parallel texts that include literal translation as well as free translation have to be included to show the deficiency and ungrammaticality of the texts produced when applying literal translation.Like Nida, Catford (1965) states that translation is a process in which a text in one language is replaced by another text that has the same meaning in another language. However, Catford (1965) lays more emphasis on the linguistic aspect of the language than on the cultural one.Similarly, Hatem and Mason (1990) see translation as transferring of the meaning from the source language to the target language. Newmark (2001, p. 7) even went further when he claims that translation is 'a craft' in which the

Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format. Besides, the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network (LSTM-RNN) model for fake news detection and classification. Finally, hunter prey optimization (HPO) algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model. The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets. The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57% and 93.53% on Covid19Fakes and satirical datasets, respectively.