TASR: Adversarial learning of topic-agnostic stylometric representations for informed crisis response through social media
Why this work is in the frame
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Bibliographic record
Abstract
The impact of crisis events can be devastating in a multitude of ways, many of which are unpredictable due to the suddenness in which they occur. The evolution of social media (for example Twitter) has given directly affected individuals or those with valuable information a platform to effectively share their stories to the masses. As a result, these platforms have become vast repositories of helpful information for emergency organizations. However, different crisis events often contain event-specific keywords, which results in the difficult extraction of useful information with a single model. In this paper, we put forward TASR, which stands for Topic-Agnostic Stylometric Representations, a novice deep learning architecture that uses stylometric and adversarial learning to remove topical bias to better manage the unknown surrounding unseen events. As an alternative to domain adaptive approaches requiring data from the unseen event, it reduces the work for those responding to the onset of a crisis. Overall, we conduct a comprehensive study of the situational properties of TASR, the benefits of its architecture including its topic-agnostic and explainable properties, and how it improves upon comparable models in past research. From two experiments, on average, TASR is able to outperform state-of-the-art methods such as transfer learning and domain adoption by 11% in AUC. The ablation study illustrates how different architecture choices of TASR impact the results and that TASR has been optimized for this task. Finally, we conduct a case study to show that explainable results from our model can be used to help guide human analysts through crisis information extraction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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