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 cyberbullying detection tools for Chinese social networking platforms. Implications also offer guidance for regulating cyberbullying and fostering ethical and responsible digital citizens.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100568PMC
http://dx.doi.org/10.3389/fpsyg.2022.861823DOI Listing

Publication Analysis

Top Keywords

ethical responsible
12
responsible digital
12
digital citizens
12
cyberbullying
10
social media
8
linguistic features
8
sentiment polarity
8
cyberbullying language
8
chinese social
8
explicit implicit
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!