Adjectival Marking of Stance in Twitter: A Corpus-Assisted Discourse Analysis of Saudi Women's Tweets on Social Reforms

Ashwaq A. Alsulami — Wed, 27 Aug 2025 00:00:00 +0800

Writers and speakers use various linguistic techniques to convey their stances. Researchers have shown significant interest in studying these methods over the years. With the rise of social media, it is anticipated that examining how writers express their stances on Twitter and other social media platforms can provide valuable insights, particularly on contentious issues, such as social reforms and women's empowerment. Accordingly, the present study employed a corpus-assisted discourse studies approach to analyse a 5,265-tweet corpus to understand how Saudi women perceive the recent social reforms in their country and how they use adjectives to convey their stances on these reforms. The study's key findings indicated that Saudi women generally view the reforms positively and tend to use evaluative specifically laudatory adjectives more frequently than attitudinal ones to express their support for these changes. The results reveal that laudatory adjectives function as prominent stance markers in social media discourse, supporting their inclusion as a distinct subcategory of evaluative stance markers within this register. These findings have implications for future research, suggesting that analysing linguistic structures in social media can offer valuable insights into the stances of social media users and can be informative for policymakers involved in social reforms.

Utilizing Artificial Intelligence (AI) for Vocabulary Learning by Saudi EFL Students: Perspectives, Practices, and Challenges

Norah Alghamdi — Wed, 27 Aug 2025 00:00:00 +0800

This study aimed to investigate the perceptions and practices of EFL students regarding utilizing AI tools to learn English vocabulary. It also aimed to explore the possible challenges that EFL students face when they use AI for this purpose. The study used a mixed-methods research design: the quantitative data consisted of responses to a questionnaire with closed-ended questions, while the qualitative data comprised responses to open-ended questions. The data were collected from 176 EFL students at a public university in Saudi Arabia. The quantitative data indicated that the EFL learners generally held positive perceptions of AI-assisted vocabulary learning. The participants considered AI an effective and beneficial tool for enhancing their vocabulary compared to traditional methods of vocabulary learning. The study results revealed that translating words and phrases and learning synonyms and antonyms of words are the instances when Saudi learners most often used AI tools. However, the EFL students reported concerns related to technical issues, the lack of cultural and language context and human interaction intrinsic to AI, and ethical considerations concerning the privacy of their personal data, as well as accuracy issues and bias. The study findings displayed that there was a positive correlation between the EFL students' perceptions and practices regarding utilizing AI tools to learn English vocabulary. This study concludes by providing some recommendations on the use of AI tools to support vocabulary learning and suggestions for future studies.

Forum for Linguistic Studies

Adjectival Marking of Stance in Twitter: A Corpus-Assisted Discourse Analysis of Saudi Women's Tweets on Social Reforms

Utilizing Artificial Intelligence (AI) for Vocabulary Learning by Saudi EFL Students: Perspectives, Practices, and Challenges