Extracting information from reddit for emergency management - A case study on British Columbia wildfire
Bibliographic record
Abstract
The advent of social media has revolutionized the way information is dissemi- nated and consumed during emergency situations, such as wildfires. This study provides an in-depth analysis of public sentiment and communication patterns on Reddit during wildfire events in British Columbia (BC), Canada. Utilizing a comprehensive methodological framework, the research employs data mining techniques, sentiment analysis, and comparative methods to explore the digi- tal discourse surrounding wildfires. The methodology integrates topic mining, keyword extraction, and sentiment analysis to evaluate the nature and scope of discussions within Reddit communities. Subreddit activity is scrutinized to under- stand regional and national concerns, while sentiment analysis offers insights into the emotional undertones of the discussions. A comparative analysis between Reddit posts and news articles is conducted to assess the interplay between social media narratives and traditional media reporting. The findings reveal a strong regional focus in discussions, reflecting the direct impact of wildfires on local communities. National concern is also evident, with broader societal implications being discussed in both general and niche subreddits. Temporal analysis of sub- reddit activity indicates that engagement is predominantly event-driven, with implications for emergency services, content creators, and community managers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".