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.

The current research takes place at the English language department at Taibah University, Saudi Arabia, where all students are enrolled in undergraduate studies and must study English language course as a core module in their first year. One of the most challenging tests faced by Saudi students in their studies, is the summative speaking test. The test is consisting of three tasks in which students required to go through them all. Accordingly, there is a need to seek approaches to enhance students' performance in the speaking test. In other words, formative assessment has not been used to overcome the challenges faced by the Saudi students at Taibah University in the speaking test. This research aims to investigate whether a formative speaking assessment has a significant impact on students' performance in the summative test. Also, it aims to monitor student learning and to provide constructive feedback that can be used by teachers to improve students' learning and help the students to identify their strengths and weaknesses in speaking skills. This study concludes that formative assessment helps Saudi students to overcome the challenges they face in speaking test. It is also recommend constructive feedback to improve their speaking performance.

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.

Recent developments in English language teaching and learning have heightened the need for the use of tasks to foster second language (L2) learning. Central to task-based interaction is the repetition of the same task. Task repetition (TR) stimulates cognitive skills for speech learning and functionality. It has been emphasised in research and practice how task repetition boosts learner processing tools by fortifying form-meaning correlations, facilitating lexicon integration, and providing practical expertise. This study aims to examine the impact of TR on reading comprehension of EFL learners, focusing on individual reading performance and group differences in familiar and recycled tasks. A total of 50 students participated in the current study. The participants were divided into two sample groups (25 male and female respondents). A quantitative research method was utilised in the data analysis. Data management and analyses were performed using IBM SPSS 24.0 (2019). Results indicated that content familiarity and TR significantly impact participants' reading skill. In addition, this study provides insights into how teachers may utilise TR within L2 lessons to support learners' language production. The findings observed in this study mirror those of the previous studies which have reported TR as being an effective tool for enhancing reading comprehension. The study concludes by discussing pedagogical implications on the role of TR in L2 learning within EFL contexts.

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