Language Ecology in New Media: An Analysis of CCTV.com on Douyin
Bibliographic record
Abstract
The purpose of this paper is to find the correlation between linguistic features and social engagement so that we can employ proper language to solve the ecological problem in the new media context. It collected all 2647 video messages of CCTV.com (account name, not website), the official media, on Douyin (China’s domestic version of Tik Tok) from January 1, 2020, to December 28, 2020, which were analyzed and studied by SPSS 22.0 and Corpus Online. It is found that public concern for a topic was significantly influenced by public opinion (r=0.483, p=0.000) and public dissemination (r=0.590, p=0.000). Declarative (n=1858, f=0.57) and Exclamative (n=1132, f=0.35) were used most frequently by CCTV. com, while the former one (p=0.02) was the key point to influence public opinion, while the latter one (p=0.001) had a significant bearing on public concern through regression analysis. On the contrary, Imperative (n=0) is not favored. Interrogative (p>0.05), Punctuation (p>0.05) and Emoji (p>0.05) had no effect on social engagement. The results of this paper indicated that language could significantly guide users’ ecological behavior and value orientation across space-time in the new media context.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".