Languages and Translation - English Language

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.

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.

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.

This paper is set out to explore the students' attitudes towards online learning effectiveness using the Blackboard platform in three Saudi public universities (Taibah, Hail, and Al-Baha) during COVID-19 pandemic. It examines the main learning activities which ensure the achievement of education quality during unprecedented online learning. These activities are online learning preference, efficiency, participation, achievements, success, and assigned assessment tasks. The survey-based questionnaire method was used to elicit students' responses regarding online learning effectiveness. The total number of students who participated in the survey is 333 (entirely regular bachelor's courses in different majors). The main section of the questionnaire contains several questions about leading 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 overall mean is 0.20, and the standard deviation for the sample is 0.095. The findings positively emphasize the significant influence of online learning on students' academic achievements in most learning factors except in an assigned assessment factor, which is still problematic in the online learning process.

Finite State Morphology serves as an important tool for investigators of natural language processing. Morphological Analysis forms an essential preprocessing step in natural language processing. This paper discusses the morphological analysis and processing of verb forms in Arabic. It focuses on the inflected verb forms and discusses the perfective, imperfective and imperatives. The deterministic finite state morphological parser for the verb forms can deal with Morphological and orthographic features of Arabic and the morphological processes which are involved in Arabic verb formation and conjugation. We use this model to generate and add all the necessary information (prefix, suffix, stem, etc.) to each morpheme of the words; so we need subtags for each morpheme. Using Finite State tool to build the computational lexicon that are usually structured with a list of the stems and affixes of the language together with a representation that tells us how words can be structured together...

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.

Recently Natural Language Processing (NLP) constituted an important area of computational linguistics and artificial intelligence, where the virtual and digital world has become an essential aspect of our daily lives. Sentiment analysis and data mining are sub-fields of NLP, which draw the attention of researchers to search and mine various issues on social media. This study explores the public's sentiments and opinions toward the SARS-CoV-2 vaccination doses in Saudi Arabia. It tries to provide insights into the motivations and barriers to taking the first and second vaccine doses and how the public's awareness and attitudes differ in the two doses. The research objects are 6.232 public tweets and comments that have been harvested from official social media platforms (Twitter and YouTube) between December 19, 2020, and December 10, 2021. The sentiment analysis measured polarity using the NLTK VADER analyzer, and the opinions were identified and classified based on the multidimensional scaling method. The results show that in the case of the first vaccine dose of the 2989 opinions enrolled, 61.5% of the public were willing to take the COVID-19 vaccination—the majority trust the vaccine safety and the Ministry of Health measures and decisions. While 21.1% show negative attitudes towards the vaccination, most of them untrust the vaccine and are worried about its syndromes. In the case of the second vaccine dose of the 3,243 opinions enrolled, 63.2% also showed positive attitudes toward taking the vaccine. Trusting the vaccine safety and not being prevented from work, travel, and other activities are the primary motivations to receive the vaccine in this phase. While negative sentiments scored 30.3%, the most frequent determinant is the suspicion of the vaccine safety, symptoms, and decision discrepancies. Identifying public sentiments and attitudes toward COVID-19 vaccination would better understand the reasons behind vaccine rejection or acceptance. It would help the health policymakers better develop and implement vaccine awareness strategies and appropriate communication to enhance vaccine take.

This paper studies the Morphological analysis and processing of noun forms in Arabic language. It focuses on the inflected noun forms and discusses the singular, dual and plural formation as well as case representation. The deterministic finite state morphological parser for the Arabic forms can deal with Morphological and orthographic features of Arabic and the morphological processes which are involved in Arabic verb and noun formation and declension. The Morphological analyser adds all the necessary information (prefix, suffix, stem, etc.) to each morpheme of the words; so the values will be added to each morpheme. Using Finite State tool to build the computational lexicon that are usually structured with a list of the stems and affixes of the language together with a representation that tells us how they can be combined together and how the network of all forms can be represented.

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 synta...

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 relat...

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 devel...

The most important objective of our study was to build and construct a complete and comprehensive morphological analyzing scheme, tagging, and parsing system which can be used for annotating Arabic corpora. In our dissertation, we did an analytical study, implementation, and evaluation of Arabic morphological analysis, tagging, and syntactic analysis starting from raw text. The three different systems were implemented in various methods; for morphological analysis, we use finite-state automaton as discussed in chapter four, after doing the process of tokenization and segmentation of the raw text as explained in chapter three. The tagging system was implemented under a new very rich tag set, which was designed and developed by us. It consists of 30 tags in addition to some other features and linguistic information as described in chapter five. An appropriate set of tags has a direct influence on the accuracy and the usefulness of tagging system. So, the smaller the tag set, the highe...

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-...

The morphological analyzer is a computational process that combines lemmas with other linguistic features to produce new lexical word forms. This paper investigates the processing of a nominal system in the Urdu language. It focuses on the inflections of noun forms and studies number, gender, person, and case representations, using a Finite State Machine (FSM) to analyze and create all the possible forms of the standardized registers. The application of the analysis using this tool provides and displays all the possible structures and their declensions. This study adds all the necessary features and values to the lexical concatenating nouns according to their patterns. The accuracy score of the output is 92.7, where the actual output depends on the detailed design of the FSM and the specific morphological processes provided to the finite state tools