Exploring Emotional Discourse and Identity Construction of Chinese Overseas Students on Xiaohongshu
Bibliographic record
Abstract
With the relaxed COVID-19 policies, China remains the largest source of international students, while social media profoundly reshapes their information acquisition and social patterns. This study adopts a multimodal discourse analysis framework, selecting 100 posts by Chinese overseas students on Xiaohongshu (2021-2025) across English-speaking countries (UK, US, Australia), non-English-speaking European nations (Germany, Sweden, Italy), and East Asian states (Japan, South Korea). It explores how students use multimodal content (text, images, videos) to express emotions, construct cross-cultural identities, and foster cultural and emotional resonance. Findings reveal Xiaohongshu as a key platform for cross-cultural identity construction. Students employ diverse strategies: humorous self-mockery to bridge language barriers, visual contrast to demonstrate cultural differences, and cultural proximity to alleviate academic and life pressures. They transform overseas predicaments into cross-cultural narratives, promoting integration and forming a hybrid identity combining critical thinking and cultural belonging. The study expands the theoretical framework of cross-cultural identity construction in the digital age, highlighting multimodal discourse’s role in harmonizing individual emotional expression and collective cultural identity. Practically, it provides references for educators, counselors, and policymakers to optimize international student support systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".