A Big Data Platform For Spatio-Temporal Social Event Discovery
Bibliographic record
Abstract
The tremendous rise of location-enriched microblogging has made it possible to discover social events from social media, as well as their evolution over space and time. Over the last decade, multiple studies on event detection from social media have been proposed, with the aim of extracting specific types of events, such as, social gatherings, natural disasters, and emergency situations, among others. However, existing works do not consider the incremental and continuous processing of events over the large amounts of social streams, and therefore, cannot determine the spatial and temporal evolution of such events. This work presents a big data mining platform for the incremental discovery of geo-social events based on a scalable and efficient architecture that can manage and mine a huge data flow of unstructured streams. We demonstrate our early results over twitter datasets and discuss its main advantage by incorporating advanced features for event extraction, thus allowing for great opportunities from application perspectives.
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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".