EXPRESSION OPINIONS ABOUT HONG KONG PROTESTS ON FACEBOOK: A STUDY OF THE SPIRAL OF SILENCE THEORY IN SOCIAL MEDIA
Bibliographic record
Abstract
do Rio de Janeiro.This political communication research analyzed the impact of digital media on public opinion.This is a qualitative and interpretive study that observes how users participate in political discussions through social media.The survey aims to analyze the opinions and expressions surrounding the proposed theme, using case study as a research strategy, and adopting methods such as frame methods and qualitative content analysis.Starting from this concept, question are raised: In the 2019 Hong Kong protests, existed a Spiral of silence phenomenon on social media?This research has important scientific and innovative contributions.In order to understand these issue, based on the Spiral of silence hypothesis, this study manually analyzed 76 posts on the Facebook website of the South China Morning Post and 2,000 public comments.The development of the theory Spiral of silence (Noelle-Neumann, 1974) pointed out that people retain their opinions when they think that the climate of opinion is contrary to their own opinions, and this silence will increase over time.The analysis of this research shows that on the Facebook platform, a minority of opinion holders tend to retain their opinions, but as time goes by, a minority of determined people begin to express their opinions, which shows an intermittent "Spiral of silence" in time, probably motivated by factors exogenous to the observed discussion environment.The research aims to contribute to the advancement of the environment for public political participation on the current social media, through the study of the media environment and media content.The background of the research is the Hong Kong Anti-Extradition Law Amendment Bill movement.This special background has innovative significance, enriching empirical research on Eastern countries, and providing new ideas for noncampaigns political participation research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".