Language Ecology in New Media: An Analysis of CCTV.com on Douyin
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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