Investigation of Social Networking Function in the Development of Viral Marketing
Bibliographic record
Abstract
This study, to assess the functioning of social networks in the spread of viral marketing that took place in this particular case the country's IT industry were investigated. Therefore, the researcher has used the survey questionnaire and the population in this study, users of social page of Rahpooyan Rayan Gostar Bartar Company on Facebook. To test the validity of research tools, test reliability and validity study, Cronbach's alpha was used. According to Cochran sampling, sample size among Facebook users in the social network of Rahpooyan Rayan Gostar Bartar Company, 384 were identified. In this study, to determine the component that is used to analyze the hierarchy of the elite group of elite people in the study were 60 people, as a result, it was found there was a significant relationship between social media marketing and how its success that leads to the purchase of goods and services.According to the results of this study, it was found that there is a significant relationship between social network marketing to draw attention to animation, attractive image for color and design, attractive slogans, self-marketing, social networking, use of celebrities, the use of symbols and its impact on the purchase of goods. These results indicate that perhaps one of the most important reasons is to prevent people, goods or services in respect of their social networks, distrust of it. Trust, creates social space that organizations can operate in that space.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".