Characterizing Users and Tracking Their Activities in Online Classified Ads
Why this work is in the frame
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Bibliographic record
Abstract
Characterizing users and tracking their activities in online classified ads is a topic of great importance. However, some of the underlying problems associated with modeling users and detecting their behavioral changes have not been well-studied. In this paper, we develop a probabilistic framework for characterizing users and quantifying some of the spatial and temporal variations in their posts. Our work on characterizing users study the problem in the context of detecting if a user belongs to a class, based on the ads the user has posted. Our approach is based on user profiling, where given statistics on user posts, the affinity of a user to a class is estimated. We show how profiles can be constructed with and without training data and report the effectiveness of our approaches in detecting two user classes business and non-business. Our work on quantifying changes due to spatial and temporal variations is based on a probabilistic model of user behavior and a generative model that can predict ad posts from each location. We evaluate these models on a relatively large set of users and ads, and report our results on two classes of users monitored over a period of almost a year.
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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 | 0.001 | 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 | 0.001 | 0.003 |
| Open science | 0.002 | 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