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المؤلفSuwaileh, Reem
المؤلفElsayed, Tamer
المؤلفImran, Muhammad
المؤلفSajjad, Hassan
تاريخ الإتاحة2024-03-11T06:03:07Z
تاريخ النشر2022
اسم المنشورInternational Journal of Disaster Risk Reduction
المصدرScopus
الرقم المعياري الدولي للكتاب22124209
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.ijdrr.2022.103107
معرّف المصادر الموحدhttp://hdl.handle.net/10576/52845
الملخصGeolocation information is important for humanitarian organizations to gain situational awareness and deliver timely aid during disasters. Towards addressing the problem of recognizing locations, i.e., Location Mention Recognition (LMR), within social media posts during disasters, past studies mainly focused on proposing techniques that assume the availability of abundant training data at the disaster onset. In this work, we adopt the more realistic assumption that no (i.e., zero-shot setting) or as little as a few hundred examples (i.e., few-shot setting) from the just-occurred event is available for training. Specifically, we examine the effect of training a BERT-based LMR model on past events using different settings, datasets, languages, and geo-proximity. Extensive empirical analysis provides several insights for building an effective LMR model during disasters, including (i) Twitter crisis-related and location-specific data from geographically-nearby disaster events is more useful than all other combinations of training datasets in the zero-shot monolingual setting, (ii) using as few as 263–356 training tweets from the target language (i.e., few-shot setting) remarkably boosts the performance in the cross- and multilingual settings, and (iii) labeling about 500 target event's tweets leads to an acceptable LMR performance, higher than F1 of 0.7, in the monolingual settings. Finally, we conduct an extensive error analysis and highlight issues related to the quality of the available datasets and weaknesses of the current model.
راعي المشروعThis work was made possible by the Graduate Sponsorship Research Award (GSRA) # GSRA5-1-0527-18082 from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors.
اللغةen
الناشرElsevier
الموضوعGeolocation recognition
Social good
Twitter
العنوانWhen a disaster happens, we are ready: Location mention recognition from crisis tweets
النوعArticle Review
رقم المجلد78
dc.accessType Abstract Only


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