Overview of Event Based Resources on Facebook and Twitter: Fort McMurray Wildfire, May 2016
Bibliographic record
Abstract
Event Based Resources (EBR) are the web resources, named after an event. In this study, we focus on Facebook and Twitter EBRs created around the 2016 Fort McMurray wildfire. We determine the time when different EBRs were created, and were closed (if closed). We also determine if the content posted by these EBRs were relevant to the Fort McMurray wildfire. We categorize these EBRs, on the basis of their social media profile and the content posted by them, into different categories. We take a closer look at the accounts that were most well-received by the public to see if their activity (number of messages posted over the data collection timeframe) and the response they got from members of public (in terms of number of likes (on Facebook) and followers (on Twitter)) were correlated with the wildfire’s progression. We also study the Event Based Resources that were active past 2 months of wildfire being ‘under control’, i.e., after July 5, 2016.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".