TikTok Users Migration to Xiaohongshu (Rednote): Emotional Dynamics, Platform Governance, and an NCA-SEM Analysis in Cross-Cultural Adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the key factors influencing emotional dynamics on digital platforms, with a particular focus on the migration of American TikTok users to Xiaohongshu (Rednote) and their cross-cultural adaptation process. Guided by the Emotional Contagion Theory and Platform Governance Theory, using PLS-SEM and Necessary Condition Analysis (NCA), this research aim to investigate the relationships between platform features, emotional contagion, and platform interaction frequency in shaping emotional dynamics. The findings reveal the moderating effects of comment mechanisms and algorithmic recommendations on the relationship between emotional contagion and interaction frequency. This suggests that cultural factors, personal preferences, and social influences may exert a more profound impact on user interactions than platform functionalities themselves. Through NCA, the study underscores that emotional contagion and interaction frequency are necessary conditions for the emergence of emotional dynamics, while other factors are insufficient.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it