AI-Powered Social Listening for Strategic Crisis Management in the Digital Era
DOI:
https://doi.org/10.5281/zenodo.16896012Keywords:
Artificial Intelligence, Social Listening, Technology, Crisis Management, Situational Crisis, Crisis InformaticsAbstract
The convergence of artificial intelligence (AI) and social listening has redefined crisis management in the technology sector, enabling organizations to detect, interpret, and respond to emerging threats with unprecedented speed and contextual precision. While prior research has examined AI in communication and monitoring separately, there remains a literature gap in synthesizing how AI-powered social listening operates across the whole crisis lifecycle and aligns with established communication theories such as Situational Crisis Communication Theory (SCCT). This paper addresses this gap through a systematic literature review (SLR) of 68 peer-reviewed studies published between 2013 and 2024, identifying key technological capabilities, applications, limitations, and ethical considerations. Findings indicate that transformer-based natural language processing (NLP) models, multimodal AI, and advanced anomaly detection systems significantly enhance early warning capabilities, real-time situational sensemaking, and adaptive stakeholder engagement. These tools have proven particularly critical in high-velocity crises where public narratives can shift rapidly across digital platforms. However, the review highlights persistent challenges, including algorithmic bias, platform coverage gaps, vulnerability to synthetic media, and ethical governance deficits. Drawing on arguments by SCCT, Crisis Informatics, and Sensemaking Theory, the current paper states that AI-based social listening can be regarded as a strategic practice rather than an auxiliary tool in resilient, transparent, and responsive crisis communication. The paper concludes with recommendations that practitioners and policymakers can implement to ensure AI systems meet both operational and social needs.
Keywords: Artificial Intelligence, Social Listening, Crisis Management, Situational Crisis, Crisis Informatics
References
Alboji, M., Öz, S., Topal, B. G., & Gökçek, T. (2024). Building relationships on Instagram: Enhancing customer engagement and visit intentions in restaurant. https://doi.org/10.5267/j.ijdns.2024.5.021
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? . Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). Language (Technology) is Power: A Critical Survey of “Bias” in NLP. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5454–5476). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.485
Coombs, W. T. (2021). Ongoing Crisis Communication: Planning, Managing, and Responding. SAGE Publications.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464–472. https://doi.org/10.1016/j.ijinfomgt.2013.01.001
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing Social Media Messages in Mass Emergency: A Survey. ACM Comput. Surv., 47(4), 67:1-67:38. https://doi.org/10.1145/2771588
Imran, M., Ofli, F., Caragea, D., & Torralba, A. (2020). Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities, Challenges, and Future Directions. Information Processing & Management, 57(5), 102261. https://doi.org/10.1016/j.ipm.2020.102261
Jin, Y., & Austin, L. L. (Eds.). (2017). Social Media and Crisis Communication. Routledge. https://doi.org/10.4324/9781315749068
Lachlan, K. A., Spence, P. R., Lin, X., Najarian, K., & Del Greco, M. (2016). Social media and crisis management: CERC, search strategies, and Twitter content. Computers in Human Behavior, 54, 647–652. https://doi.org/10.1016/j.chb.2015.05.027
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach (arXiv:1907.11692). arXiv. https://doi.org/10.48550/arXiv.1907.11692
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, 8748–8763. https://proceedings.mlr.press/v139/radford21a.html
Reuter, C., & Kaufhold, M.-A. (2018). Fifteen years of social media in emergencies: A retrospective review and future directions for crisis Informatics. Journal of Contingencies and Crisis Management, 26(1), 41–57. https://doi.org/10.1111/1468-5973.12196
Skitka, L. J., Mosier, K. L., Burdick, M., & Rosenblatt, B. (2000). Automation Bias and Errors: Are Crews Better Than Individuals? The International Journal of Aviation Psychology, 10(1), 85–97. https://doi.org/10.1207/S15327108IJAP1001_5
Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics – Challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39, 156–168. https://doi.org/10.1016/j.ijinfomgt.2017.12.002
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Muhannad Alboji
This work is licensed under a Creative Commons Attribution 4.0 International License.
