CHINESE IT COMPANIES UNDER U.S.-CHINA TRADE WAR: A COMPUTATIONAL POLITICAL COMMUNICATION PERSPECTIVE
Bibliographic record
Abstract
Computational political communication, based on big data analytics of social media texts, provides a paradigm for understanding the public's view of and engagement with political events worldwide.This study reviews previous efforts by social and data scientists and offers a demo to show the potential of computational political communication.To characterize online political communication dynamics surrounding U.S.-China tensions and gain a better understanding of the U.S.-China power struggle, a vast amount of user-generated Twitter data is compiled from March 2020 to March 2021 globally.Chinese IT giants (Huawei, Tencent, and ByteDance) and major English-speaking countries (the United States, United Kingdom, Canada, Australia, New Zealand, India, and Pakistan) are chosen as keywords for filtering the tweets gathered.Sentiment analysis of the tweets is carried out automatically.It is found that the popularities of debates regarding certain nations and companies are uneven and might be triggered by events.Furthermore, rather than being segregated, the discourses of all of these companies are intertwined.It is expected that future studies can apply more fine-grained, categorized, and automated sentiment and topic analysis to show a panorama of online public opinion.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".