Reaching a desired set of users via different paths: an online advertising technique on micro-blogging platforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media and micro-blogging platforms have been successful for communication and information exchange enjoying vast number of user participation. Given their millions of users, it is natural that there is a lot of interest for marketing and advertising on these platforms as attested by the introduced advertising platforms on Twitter and Facebook. In this paper, inspired by micro-blogging advertising platforms, we introduce two problems to aid ad and marketing campaigns. The first problem identifies topics (called analogous topics) that have approximately the same audience in a micro-blogging platform as a given query topic. The main idea is that by bidding on an analogous topic instead of the original query topic, we reach approximately the same audience while spending less of our budget. Then, we present algorithms to identify expert users on a given query topic and categorize these experts to finely understand their diversified expertise. This is imperative for word of mouth marketing where individuals have to be targeted precisely. We evaluate our algorithms and solutions for both problems on a large dataset from Twitter attesting to their eciency and accuracy compared with alternate approaches.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it