Impact of Internet Banner Advertisement on Social MediaâS Consumer Buying Behaviour: A Case Study of Facebook
Bibliographic record
Abstract
Internet is the utmost imposing media of communication in this gen eternity. Internet users are increasing day to day. Every moment people are surfing internet for work purpose or passing their leisure time and connecting with the people. As a result they are connecting with the social media e.g. Twiter, Facebook and so on. People are chatting, sharing photo and connecting with the friends and family by the social media. Face book is really close to its admirers of the growth and the metrics of Facebook. It is really astounded that between the year of 2009 and 2000 the growth of international users. In the year of 2008, the total International Facebook users were 34 million people. The number had increased to 95 million by the first day of 2009 which had increased by 70% of the total Facebook users. Now the number of Facebook users is more than 400 million. Now a day the rate growing is increasing into the doubled digit in every month. In this quarter the rate of growth hastened by 25%. In 28 countries the rate is doubled in this quarter (Smith, 2010). The most important advantage of internet is the update information in all about the world. People are searching internet for update information and buy products because of most of the company using online as an advertising media to promote their product.In the modern era people are connected with friend and family using social media. Social media great place to advertisement because a person must be login into his or social media website. So the different organization taking this
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".