Rejection or Inclusion in the Emotion-Identity Dynamics of TikTok Refugees on RedNote

2025-07-19

AI Summary

This study investigates cross-cultural interaction mechanisms between Chinese users and foreign “TikTok refugees” who migrated to RedNote following the U.S. TikTok ban. Analyzing 1,862 posts and 403,000 comments, we integrate large language model–based sentiment classification with BERT-driven topic modeling to examine affective dynamics across stances (pro-China/neutral/pro-foreign), topics (political/appearance/cultural), and identity negotiation. Results reveal significant affective asymmetry: political discourse is highly polarized, eliciting contempt and anger; appearance-related content fosters emotional equilibrium; and while neutral users express curiosity and joy, they implicitly reinforce dominant discursive norms. This work provides the first empirical evidence that affective expression in transnational digital platforms is jointly moderated by stance and topic—advancing theoretical frameworks for studying identity formation and affective politics in digital public spheres.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

This study examines cross-cultural interactions between Chinese users and self-identified "TikTok Refugees"(foreign users who migrated to RedNote after TikTok's U.S. ban). Based on a dataset of 1,862 posts and 403,054 comments, we use large language model-based sentiment classification and BERT-based topic modelling to explore how both groups engage with the TikTok refugee phenomenon. We analyse what themes foreign users express, how Chinese users respond, how stances (Pro-China, Neutral, Pro-Foreign) shape emotional expression, and how affective responses differ across topics and identities. Results show strong affective asymmetry: Chinese users respond with varying emotional intensities across topics and stances: pride and praise dominate cultural threads, while political discussions elicit high levels of contempt and anger, especially from Pro-China commenters. Pro-Foreign users exhibit the strongest negative emotions across all topics, whereas neutral users express curiosity and joy but still reinforce mainstream discursive norms. Cross-topic comparisons reveal that appearance-related content produces the most emotionally balanced interactions, while politics generates the highest polarization. Our findings reveal distinct emotion-stance structures in Sino-foreign online interactions and offer empirical insights into identity negotiation in transnational digital publics.

Problem

• Analyze cross-cultural interactions between Chinese users and TikTok Refugees on RedNote
• Examine emotional responses and stances in Sino-foreign online discussions
• Explore identity negotiation and affective asymmetry in transnational digital publics

Innovation

• Large language model sentiment classification
• BERT-based topic modelling analysis
• Cross-topic emotion-stance structure comparison