Typology of Image Crises Using Large Language Models: A Novel Approach to Crisis Classification
Bibliographic record
Abstract
ABSTRACT Image crises pose significant challenges for organizations and public figures, often requiring rapid identification and classification to mitigate reputational damage. This study introduces a novel typology of brand crises and demonstrates its application using large language models (LLMs) to enhance crisis detection and classification. We review the current state of knowledge of brand crises and LLMs, underlining their relevance in real‐world text analytics tasks. Based on an analysis of 300 actual crisis cases, we propose an original typology that captures various types and causes of crises. Our methodology combines expert data annotation with automatic crisis type annotation using a generative LLM. This approach enables both classification and early detection of crises in media texts. The results demonstrate that the GPT‐4‐turbo achieved strong performance in distinguishing ideological from nonideological crises (accuracy: 0.903; F1: 0.874), while GPT‐5 with a 2‐shot prompt and GPT‐4o‐mini excelled in identifying affected actors (accuracy and F1: 0.984). Performance was comparatively lower for detailed cause classification, highlighting the greater complexity of fine‐grained categorizations. This study highlights the potential and limitations of LLMs in developing automated crisis management systems to enhance organizational resilience.
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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 | 0.001 | 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".