Bibliographic record
Abstract
As social media grows more rapidly each day, new ways to harness worldwide connectivity are being continually discovered. The role of social media in disaster management emerged in 2012; social media data can yield rescue and aid opportunities for humanitarians. Immediately after a natural disaster, an overwhelming amount of this data floods social workers. Unfortunately, the majority of this data carries no value to disaster responders, who are only interested in location and severity of damage. MicroFilters is a system designed to take advantage of image data by scraping tweets and the links therein for images, then using machine learning to classify them. This classification will eliminate images that do not show direct damage and therefore are not useful to rescue efforts. This paper outlines the development of the MicroFilters system from start to finish, including key technical problems involved such as data sparseness, feature engineering, and classification. The experimental evaluation validates the proposal and shows the efficiency of our techniques (average 88% recall and 70% precision). We also discuss opportunities for future development of the MicroFilters system.
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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 | 0.000 | 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 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".