VERIFICATION OF SOCIAL IMPACT THEORY CLAIMSIN SOCIAL MEDIA CONTEXT
Bibliographic record
Abstract
Social media explosion changed the way of communication. It affected the ways companies used to interact with their consumers. Most important it changed the way consumers used to think. Present study attempts to verify the claims and assumptions of social impact theory in the social media environment. Based on social impact theory present study examines the impact of number of users (NUs) on the perceived credibility of user generated content (PCUGC). Furthermore, it examines the impact of PCUGC on the consumer attitude towards the product related content embedded in UGC (ATUGC). Empirical evidence was collected from a random sample of 459 students. Results substantiate the claims and assumptions of the social impact theory in the social media context. Results show positive impact of NUs on PCUGC. Similarly, they show positive relationship between PCUGC and ATUGC.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".