Articles | Volume 21, issue 6
Review article
15 Jun 2021
Review article |  | 15 Jun 2021

Review article: Detection of actionable tweets in crisis events

Anna Kruspe, Jens Kersten, and Friederike Klan

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Cited articles

Alam, F., Imran, M., and Ofli, F.: Image4Act: Online Social Media Image Processing for Disaster Response, in: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, 601–604, 2017. a
Alam, F., Joty, S., and Imran, M.: Domain Adaptation with Adversarial Training and Graph Embeddings, in: 56th Annual Meeting of the Association for Computational Linguistics (ACL), Melbourne, Australia, 2018a. a, b
Alam, F., Ofli, F., and Imran, M.: CrisisMMD: Multimodal Twitter Datasets from Natural Disasters, in: Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM), 31 July–3 August 2017, Sydney, Australia, 2018b. a, b, c
Alam, F., Ofli, F., and Imran, M.: Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria, Behav. Inform. Technol., 39, 288–318,, 2020. a
ALRashdi, R. and O'Keefe, S.: Deep Learning and Word Embeddings for Tweet Classification for Crisis Response, The 3rd National Computing Colleges Conference, 8–9 October 2018, Abha, Saudi Arabia, 2019. a
Short summary
Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowdsourcing, and (3) on machine learning. We also point out challenges and suggest future research.
Final-revised paper