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 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.

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, and 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 about 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 towards the SARS-CoV-2 vaccination doses in Saudi Arabia. It tries to provide insights on the motivations and barriers in 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 show 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 provide a better understanding of the reasons behind vaccine rejection or acceptance and would help the health policymakers better develop and implement vaccine awareness strategies and appropriate communication to enhance vaccine taking.