Using Data Mining Techniques to Examine Domestic Violence Topics on Twitter
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
This study aims to discover hidden topics and thematic structures among domestic violence-related texts on Twitter. We collected 322,863 messages using the key term “domestic violence.” We used unsupervised machine-learning methodology Latent dirichlet allocation, and found that the most common 20 pairs of words were “violence awareness,” “greg hardy,” “awareness month,” “victims domestic,” “stop domestic,” and “ronda rousey.” We identified 20 topics that appear most frequently, such as Topic 19 with frequent words “greg hardy,” “photos greg,” “dallas cowboys,” “charges expunged,” “hardy girlfriend,” and also assigned themes (e.g., “Greg Hardy domestic violence case”) for the topics. This study demonstrates the feasibility of using topic-modeling methods for mining gender-based violence data on Twitter.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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