Cyberbullying victimization identification and large language model-assisted assessment: a study of cyberbullying victimization lexicon construction and validation
Xingyun Liu, Yuehan Liao, Fan Feng, Yiming Tu, Xin Kang, Miao Liu, Nuo Han · 2026
Read Cyberbullying victimization identification and large language model-assisted assessment: a study of cyberbullying victimization lexicon construction and validation by Xingyun Liu, Yuehan Liao, Fan Feng, Yiming Tu, Xin Kang, Miao Liu, Nuo Han (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13341625.txt.
Description
A word list for spotting cyberbullying victims online is built and tested by Xingyun Liu and colleagues, in their 2026 study. Self-reports skew victim counts, so the team mined Weibo posts, psychology lexicons, and questionnaires to construct a Chinese lexicon across methods, perceived harm, and coping responses.
The lexicon held 442 words, validated against expert ratings on 500 posts and checked for cross-platform fit on 3,442 RedNote posts. DeepSeek-R1 and GPT-4o were also made to repeat the construction steps under simple and complex prompts, their agreement with human judgments scored statistically. Machine help in lexicon building is itself part of what gets assessed.
Catalog record
| CREATOR | Xingyun Liu, Yuehan Liao, Fan Feng, Yiming Tu, Xin Kang, Miao Liu, Nuo Han |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68659 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |