Harnessing Social Media to Explore Youth Social Withdrawal in Three Major Cities in China: Cross-Sectional Web Survey
Bibliographic record
Abstract
BACKGROUND: Socially withdrawn youth belong to an emerging subgroup of youth who are not in employment, education, or training and who have limited social interaction intention and opportunities. The use of the internet and social media is expected to be an alternative and feasible way to reach this group of young people because of their reclusive nature. OBJECTIVE: The aim of this study was to explore the possibility of using various social media platforms to investigate the existence of the phenomenon of youth social withdrawal in 3 major cities in China. METHODS: A cross-sectional open Web survey was conducted from October 2015 to May 2016 to identify and reach socially withdrawn youth in 3 metropolitan cities in China: Beijing, Shanghai, and Shenzhen. To advertise the survey, 3 social media platforms were used: Weibo, WeChat, and Wandianba, a social networking gaming website. RESULTS: In total, 137 participants completed the survey, among whom 13 (9.5%) were identified as belonging to the withdrawal group, 7 (5.1%) to the asocial group, and 9 (6.6%) to the hikikomori group (both withdrawn and asocial for more than 3 months). The cost of recruitment via Weibo was US $7.27 per participant. CONCLUSIONS: Several social media platforms in China are viable and inexpensive tools to reach socially withdrawn youth, and internet platforms that specialize in a certain culture or type of entertainment appeared to be more effective in reaching socially withdrawn youth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".