Impact of caricature-based datasets on demographic inference
Bibliographic record
Abstract
The widespread adoption of social networking platforms has generated interest in studying groups of users. Interest in the composition of groups has led to the development of methods to infer demographic attributes of participants such as such as age, ethnicity, and political orientation. While all demographic inference methods report excellent performance, there is a concern that this is the product of the way the dataset was constructed and not just the methods accuracy. In our research we observed an overemphasis on classifying users that exhibit easy-to-classify traits ; we call such users caricatures. In this thesis, we establish the extent to which caricatures introduce a bias that leads to results that give an overoptimistic characterization of the inference engine's abilities. We further continued our research and introduced a simple and effective method to create non-caricature-based datasets. In this work we focus on political caricatures in Twitter, although we consider our results representa- tive of the effect of caricatures in Twitter demographic inference datasets, regardless of the attribute of interest. Therefore our research should serve as a warning call to researchers using caricature-based datasets to do demographic inference.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".