Public Engagement in Climate Communication on China’s Weibo: Network Structure and Information Flows
Bibliographic record
Abstract
This article provides an empirical study of public engagement with climate change discourse in China by analysing how Chinese publics participate in the public discussion around two Intergovernmental Panel on Climate Change reports and how individual users interact with state and elite actors on the pre-eminent Chinese microblogging platform Weibo. Using social network analysis methods and a temporal comparison, we examine the structure of climate communication networks, the direction of information flows among multiple types of Weibo users, and the changes in information diffusion patterns between the pre- and post-Paris periods. Our results show there is an increasing yet constrained form of public engagement in climate communication on Weibo alongside China’s pro-environmental transition in recent years. We find an expansion of public engagement as shown by individual users’ increasing influence in communication networks and the diversification of frames associated with climate change discourse. However, we also find three restrictive interaction tendencies that limit Weibo’s potential to facilitate multi-directional communication and open public deliberation of climate change, including the decline of mutually balanced dialogic interactions, the lack of bottom-up information flows, and the reinforcement of homophily tendencies amongst eco-insiders and governmental users. These findings highlight the coexistence of both opportunities and constraints of Weibo being a venue for public engagement with climate communication and as a forum for a new climate politics and citizen participation in China.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".