Tweeting a deluge: Understanding the use of social networking site content by journalists during a natural disaster
Bibliographic record
Abstract
This research examines the extent to which journalists assert narrative control over content from social networking sites during disaster events, using the Toronto flooding that occurred on July 8, 2013 as a case study. Using the theory of the disaster marathon narrative outlined by Liebes (1998), this research uses a hybrid of qualitative and quantitative research approaches to understand how the visual and linguistic elements of the news coverage worked together to generate meaning. The research reveals that the content selected from social networking sites generally served to reinforce the disaster narrative, as conceptualized by Liebes (1998). However, it was observed that the integration of some content from public stakeholders (i.e. police, hydro organizations) served to counteract the typical disaster narrative. This research contributes to the body of discourse analytic research dedicated to understanding the interactive practices between social networking sites and ‘traditional’ journalism.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| 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".