Facebook rolls out new advertising products
Bibliographic record
Abstract
Facebook's betting ad money is better spent on mobile devices than on TV, and ahead of New York's Advertising Week conference, the company announced several new advertising products that're designed to capture some of the more than $70B being spent on television advertisement. Mobile ads already bring Facebook more than 3 quarters of its annual ad revenue, with digital video ad spending growing more than 5 times faster than spending on television ads, according to Reuters. Facebook's new ad-buying option's going to be called Target Rating Point buying, or T-R-P buying. 'Advertising Age' says it's a new spin on the nielson gross rating points that TV advertisers've been using for decades. Ad buyers'll need to call a Facebook sales rep, tell them the target audience and what share of that audience they want to buy ads for. Facebook says it's a lot like a traditional TV-buy, where advertisers submit their goals and budget, but the company says it's got a millenial audience that TV-advertising can't reach anywhere else. Facebook also announced a new kind of mobile polling, asking people whether they remember an ad and whether it led them to buy a product. It also introduced another new ad-buying option called \Brand Awareness Optimization.\ Facebook says that'll use special algorithms to put advertising campaigns in front of a specifically targeted profile of people. Facebook says TRP-buying'll be available in all the same markets that digital ad ratings from nielsen are already available, including the UK, Australia, France, Canada, Italy, Germany, Brazil and the United States. The new buying options begin this year with Facebook's video ads, and will extend to ads on Instagram in the first quarter of next year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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; both teacher heads agree on what is shown here.
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".