TARGETING IN PROMOTING PRODUCTS IN SOCIAL NETWORK
Bibliographic record
Abstract
The Internet has been steadily evolving.New technologies, services and resources appear almost every day.Social networking is not just a place where people talk to each other.It is also a unique opportunity to find your target audience and build with them a long and good partnership.Now is the best time to consider about how to use the sales promotion in social networks like: VK.com , Odnoklassniki.ru, Twitter.com and Facebook.com.These sites are like magnets that attract more and more new visitors.One of the main things of successful product on the market is compliance with the target audience.Before starting promoting in social networks it is very important to choose a target audience whose attention you will get.This is an extremely important moment.This choice will depend on the success of social advancement.Main target audience of Vkontakte are the people from the CIS countries aged from 14 to 25 years, but also there are people from the USA, Canada, Brazil and Europe, mostly immigrants from the former Soviet Union.Older people are also registered in Vkontakte, who have found this site as a way to communicate with their ex-classmates.You can advertise your products in a social network using two most effective ways.The first one is the placing your ad ("post") on the wall (the first page) of the popular group or public pages ("public").To do this you must find the group, which will thematically fit your ad, find out statistics attendance and then advertise your product there.Your ad is seen by all consisting in the group or "public" people.However, if you have large quantities of goods, you need to create your own group or a public page, but it will be not very popular without promotion (important: creating groups Vkontakte is now for free).To do this, it is advisable to use the following type of advertising -targeting.This means that you do not advertise your product, you advertise your group/ ublic page or a video, where you can place your product.It has own advantages and disadvantages in comparison with advertising in groups.This type of advertising is very effective, because you promote your products directly to the target audience.Criteria used to select the audience are different, even to the people who are in other and competing groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".