How is artificial intelligence shaping crisis communication? A systematic review and future research agenda
Bibliographic record
Abstract
As crises grow more complex and digital, artificial intelligence (AI) is emerging not only as a technological tool but also as a strategic actor in crisis communication. This study systematically reviews 177 SSCI-indexed articles across communication, technology, and management fields to evaluate how AI is reshaping crisis response. We examine trends in theoretical frameworks, methodological approaches, AI types and functions, and crisis contexts. Findings reveal a sharp increase in interdisciplinary interest since 2019, particularly around machine learning, chatbots, and predictive analytics for crisis detection, response, and emotional support. Yet the literature remains fragmented: only about one-fifth of studies apply explicit theoretical frameworks, research disproportionately emphasizes detection and classification over relational and trust-building functions, and ethical issues such as transparency, fairness, and accountability are acknowledged but seldom tested empirically. Moreover, most studies analyze AI in isolated crisis phases rather than across the full pre-crisis, crisis, and post-crisis lifecycle. This article calls for future research that strengthens theoretical foundations, integrates ethical and governance principles, and advances empirical testing across diverse contexts to ensure AI enhances public trust and organizational legitimacy in crisis communication.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".