A cross-platform comparison of China’s confrontational diplomatic communication
Bibliographic record
Abstract
In recent years, increasingly confrontational diplomatic communication has become a concerning trend, marked by a rise in aggressive narratives between nations. This study explores the transformation of diplomatic communication in the digital age, focusing on the practices of Chinese wolf-warrior diplomacy on social media. By analysing Zhao’s social media posts on international (Twitter) and domestic (Weibo) platforms, we quantified his confrontational diplomatic narratives using structural topic modelling. We then investigate how social media affordances like micro-targeting and boundary spanning, combined with domestic social, political, and economic contexts, shape these confrontational narratives. Our findings demonstrate that Zhao’s communication style varies between platforms: it is reactive and defensive on Twitter, addressing international audiences, while being proactive and offensive on Weibo, targeting domestic audiences. This strategic use of social media serves not only diplomatic goals but also aligns with China’s nationalist sentiments and domestic policies. Our study reveals that China’s wolf-warrior diplomacy is both a result of and a contributor to the changing dynamics of international relations and domestic politics, offering a novel framework for understanding confrontational diplomacy’s rise globally.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".