CReMa: Crisis Response Through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media

During times of crisis, social media platforms play a crucial role in facilitating communication and coordinating resources. In the midst of chaos and uncertainty, communities often rely on these platforms to share urgent pleas for help, extend support, and organize relief efforts. However, the overwhelming volume of conversations during such periods can escalate to unprecedented levels, necessitating the automated identification and matching of requests and offers to streamline relief operations. Additionally, there is a notable absence of studies conducted in multilingual settings despite the fact that any geographical area can have a diverse linguistic population. Therefore, we propose crisis response matcher (CReMa), a systematic approach that integrates textual, temporal, and spatial features to address the challenges of effectively identifying and matching requests and offers on social media platforms during emergencies. Our approach utilizes a crisis-specific pretrained model and a multilingual embedding space. We emulate human decision-making to compute temporal and spatial features and nonlinearly weigh the textual features. The results from our experiments are promising, outperforming strong baselines. Additionally, we introduce a novel multilingual dataset, simulating help-seeking and offering assistance on social media in 16 languages, and conduct comprehensive cross-lingual experiments. Furthermore, we analyze a million-scale geotagged global dataset to understand patterns in seeking help and offering assistance on social media. Overall, these contributions advance the field of crisis informatics and provide benchmarks for future research in the area.