How is artificial intelligence shaping crisis communication? A systematic review and future research agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it