COVID-19 has disrupted most of the industries in the world. Education is the only industry that is completely transferred to online mode in most countries around the world. Online learning was the best solution for continuing education during the pandemic, especially in tertiary education. This study aims to determine the challenges and obstacles confronted by English language learners (EFL) in Science and Arts College, Alula, Taibah University, Saudi Arabia, during switching to online learning in the second semester of 2020 due to the COVID-19 pandemic. The contribution of this study is to evaluate the learners’ new experiences in online education and to assess the feasibility of the virtual methods of learning. This is achieved by analyzing 184 learners’ responses to the survey-based questionnaire. A descriptive statistical method was used to test the validation of the study. It is found that the main problems that influence and impact online EFL learning during COVID-19 are related to technical, academic, and communication challenges. The study results show that most EFL learners are not satisfied with continuing online learning, as they could not fulfill the expected progress in language learning performance.

The tremendous growth and impact of fake news as a hot research field gained the public’s attention and threatened their safety in recent years. However, there is a wide range of developed fashions to detect fake contents, either those human-based approaches or machine-based approaches; both have shown inadequacy and limitations, especially those fully automatic approaches. The purpose of this analytic study of media news language is to investigate and identify the linguistic features and their contribution in analyzing data to detect, filter, and differentiate between fake and authentic news texts. This study outlines promising uses of linguistic indicators and adds a rather unconventional outlook to prior literature. It utilizes qualitative and quantitative data analysis as an analytic method to identify systematic nuances between fake and factual news in terms of detecting and comparing 16 attributes under three main linguistic features categories (lexical, grammatical, and syntactic features) assigned manually to news texts. The obtained datasets consist of publicly available right documents on the Politi-fact website and the raw (test) data set collected randomly from news posts on Facebook pages. The results show that linguistic features, especially grammatical features, help determine untrustworthy texts and demonstrate that most of the test news tends to be unreliable articles.

This paper is set out to explore the students’ attitudes towards online learning effectiveness using the Blackboard platform in three public Saudi universities (Taibah, Hail, and Al-Baha) during COVID 19 pandemic. It examines the learning activities which ensure the achievement of education quality during unprecedented online learning. The survey based- questionnaire method was used to elicit students’ responses. The numbers of students who participated in the survey are 333. The main section of the questionnaire contains questions about the main online learning activities. The coefficient relation of the p-value is highly correlated when tested using Pearson’s r and Spearman’s. The score of Cronbach’s Alpha is (0.93) which indicates (greater internal consistency) an acceptable level of reliability. The findings positively emphasize the effective influence of online learning on student’s academic achievements in most of learning factors except in an assessment factor where the development of new assessment methods is needed.

This study aims to find out the impacts of using emojis by EFL learners on their writing skills and highlight the learners’ attitudes towards this new communication phenomenon. It discusses the different uses of emojis in social media apps, investigates the reasons for the rise of using emojis in everyday social interaction, and to which extent the occurrence of this pictographic script can substitute the written language. A qualitative and quantitative analysis has been applied in this investigation where a survey-based questionnaire was distributed among 143 EFL learners in Taibah University in Saudi Arabia. Descriptive statistics and ANOVA (analysis of variables) are used to analyze the obtained data. The results show that the p-value of the study variables is equal to one which is much bigger than alpha and there is no big difference between the variables’ estimation in the participants’ responses, i.e. the emojis’ use in texting affects the use of the language. Moreover, the findings display that the use of emojis and short forms (contractions and acronyms) in text messages form a real threat to the standard and non-standard languages. The outcomes of this study make it clear this new sort of communication may replace mainly languages where social media users found that emojis best represent their feelings and thoughts. This research concluded that the use of emojis has an important role in interpersonal communications, however, standard writing skills would be negatively affected using these newly emerged communication tools. The consequences of these impacts are aptly evidenced in the form of spelling, structural errors, and weakness of expressions in EFL learners’ language learning.

While different variants of COVID-19 dramatically affected the lives of millions of people across the globe, a new version of COVID-19, "SARS-CoV-2 Omicron," emerged. This paper analyzes the public attitude and sentiment towards the emergence of the SARS-CoV-2 Omicron variant on Twitter. The proposed approach relies on the text analytics of Twitter data considering tweets, retweets, and hashtags' main themes, the pandemic restriction, the efficacy of covid-19 vaccines, transmissible variants, and the surge of infection. A total of 18,737 tweets were pulled via Twitter Application Programming Interface (API) from December 3, 2021, to December 26, 2021, using the SentiStrength software that employs a lexicon of sentiment terms and a set of linguistic rules. The analysis was conducted to distinguish and codify subjective content and estimate the strength of positive and negative sentiment with an average of 95% confidence intervals based upon emotion strength scales of 1-5. It is found that negativity was dominated after the outbreak of Omicron and scored 31.01% for weak, 16.32% for moderate, 5.36% for strong, and 0.35% for very strong sentiment strength. In contrast, positivity decreased gradually and scored 16.48% for weak, 11.19% for moderate, 0.80% for strong, 0.04% for very strong sentiment strength. Identifying the public emotional status would help the concerned authorities to provide appropriate strategies and communications to relieve public worries towards pandemics.