As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cybe
Using the DeLone and McLean (D&M) Model, the study explains the differences between female and male students’ accessibility to E-learning portals. This study compares female and male student groups regarding the usage of the E-learning portal in the higher education context. Using an online google survey, the data were collected from 254 students, including males and females. The study utilized PLS-SEM to perform a multi-group analysis examining female and male student groups. The multi-group analysis followed the MICOM procedure, and SmartPLS three was utilized to analyze the data. The study found a significant and direct relationship of E-service quality with system use and user satisfaction. System quality also supported the relationship with user satisfaction. The study further revealed a significant and positive relationship between system use and user satisfaction with E-learning portal success. This study found a direct relationship between system quality and user satisfa