The Effect on Audience Reactions of Audience Opinion Adoption from SNS
Bibliographic record
Abstract
Like companies strategically using social networking services (SNS) competitively, TV broadcasters have tried to adopt audience opinion synchronously (AOS) using SNS in their broadcasting content. However, few studies have investigated the effect of AOS characteristics on audience reactions. This study used the theory of justice to empirically validate the effect of AOS adoption on such elements of justice perceptions, content quality, trust, satisfaction, and on such audience reactions as continuous viewing, purchase, word-of-mouth, and reciprocal participation. We conducted a laboratory experiment that used three types of virtual broadcasting content (a summary of majority opinion in SNS, a summary of majority opinion in SNS and its detailed comments, and, third, two majority and minority opinion summaries in SNS and their detailed comments). Data were collected from 294 participants and analyzed by PLS algorithms. As a result, we found that the depth and breadth of the AOS in broadcasting content could significantly enhance audience reaction . This study introduced into the research arena the issue of the strategic usage of SNS by TV broadcasters and used the theory of justice, which represents the public role of TV broadcasters to gather the public opinions. Also, we conducted a methodologically rigorous approach to validate the effect of this SNS usage. We expect that the results of this study will have practical implications for companies trying to use SNS strategically to enrich their services.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".