In recent months, many governments have announced COVID-19 vaccination programs and plans to help end the crises the world has been facing since the emergence of the coronavirus pandemic. In Saudi Arabia, the Ministry of Health called for citizens and residents to take up the vaccine as an essential step to return life to normal. However, the take-up calls were made in the face of profound disagreements on social media platforms and online networks about the value and efficacy of the vaccines. Thus, this study seeks to explore the responses of Saudi citizens to the COVID-19 vaccines and their sentiments about being vaccinated using opinion mining methods to analyze data extracted from Twitter, the most widely used social media network in Saudi Arabia. A corpus of 37,467 tweets was built. Vector space classification (VSC) methods were used to group and categorize the selected tweets based on their linguistic content, classifying the attitudes and responses of the users into three defined categories: positive, negative, and neutral. The lexical semantic properties of the posts show a prevalence of negative responses. This indicates that health departments need to ensure citizens are equipped with accurate, evidence-based information and key facts about the COVID-19 vaccines to help them make appropriate decisions when it comes to being vaccinated. Although the study is limited to the analysis of attitudes of people to the COVID-19 vaccines in Saudi Arabia, it has clear implications for the application of opinion mining using computational linguistic methods in Arabic.

The thesis abstract, as a genre has a set of communicative functions mutually-understood by established members of the academic community. A vast majority of translation studies of source language (SL) and target language (TL) equivalence seems to have overlooked the inherent relationship between form and function when translating. The purpose of this study was to find out whether the Arab students would translate the English passive structures into their corresponding Arabic passive in order to maintain the pragma-generic functions associated with these constructions or would employ other translation replacements when translating English passives into Arabic. A further purpose was to find out what grammatical factors constrain the choice of these translation options. To fulfill these purposes, we investigated the voice choice in 90 MA thesis abstracts and their 90 Arabic translated versions written in English by the same MA students, drawn from the field of Linguistics. The data analysis revealed that when the Arab student-translators come across the English passive sentence, they resort to either of the following options: Transposing English passives into verbal nouns (masdar), or into pseudo-active verbs or active sentence structures, or into vowel melody passives, or omitting these passive structures.

The recent horrors of the COVID-19 pandemic have renewed interest in Gothic fiction in general and Mary Shelley's Frankenstein in particular. The image of Frankenstein has become associated with the COVID-19 discourses (e.g. literary, journalistic, medical, and social media) as reflected in the representation of the horrors of isolation and contagion. The influence of the novel is clearly reflected in literary, medical, scientific, and everyday discourses today. Despite the prolific literature on the representation of Gothic elements and motifs, reflections on Gothic motifs in the reproduction of COVID-19 discourse have not yet been elaborated. In light of this, the paper seeks to explore the manipulation and reproduction of Shelley's Frankenstein in the COVID-19 journalistic discourse. A corpus of 542 editorials and opinion commentaries from 83 newspapers was built. The corpus included only editorials and opinions written in English. Corpus-based critical discourse analysis was used. The results indicate that the Gothic motifs of Frankenstein are manipulated and reproduced in the journalistic discourse of COVID-19 to depict the chaos and destruction caused by the enormously powerful and catastrophic virus/monster that has come to rule the world. The COVID-19 journalistic discourse based on the fictional image of Frankenstein represents a distinct discourse genre that manipulates Gothic motifs in the thematic representations of the horrors associated with the pandemic itself on the one hand, and the social, economic and political problems and crises that have shaken the stability of the entire world on the other. It can be concluded that this representation has been developed and generated from the interaction between the writers and the text of Frankenstein.

Digital news platforms and online newspapers have multiplied at an unprecedented speed, making it difficult for users to read and follow all news articles on important, relevant topics. Numerous automatic text summarization systems have thus been developed to address the increasing needs of users around the world for summaries that reduce reading and processing time. Various automatic summarization systems have been developed and/or adapted in Arabic. The evaluation of automatic summarization performance is as important as the summarization process itself. Despite the importance of assessing summarization systems to identify potential limitations and improve their performance, very little has been done in this respect on systems in Arabic. Therefore, this study evaluated three text summarizers AlSummarizer, LAKHASLY, and RESOOMER using a corpus built of 40 news articles. Only articles written in Modern Standard Arabic (MSA) were selected as this is the formal and working language of Arab newspapers and news networks. Three expert examiners generated manual summaries and examined the linguistic consistency and relevance of the automatic summaries to the original news articles by comparing the automatic summaries to the manual (human) summaries. The scores for the three automatic summarizers were very similar and indicated that their performance was not satisfactory. In particular, the automatic summaries had serious problems with sentence relevance, which has negative implications for the reliability of such systems. The poor performance of Arabic summarizers can mainly be attributed to the unique morphological and syntactic characteristics of Arabic, which differ in many ways from English and other Western languages (the original language/s of automatic summarizers), and are critical in building sentence relevance and coherence in Arabic. Thus, summarization systems should be trained to identify discourse markers within the texts and use these in the generation of automatic summaries. This will have a positive impact on the quality and reliability of text summarization systems. Arabic summarization systems need to incorporate semantic approaches to improve performance and construct more coherent and meaningful summaries. This study was limited to news articles in MSA. However, the findings of the study and their implications can be extended to other genres, including academic articles.