  During times of crisis, social media platforms play a vital role in
facilitating communication and coordinating resources. Amidst chaos and
uncertainty, communities often rely on these platforms to share urgent pleas
for help, extend support, and organize relief efforts. However, the sheer
volume of conversations during such periods, which can escalate to
unprecedented levels, necessitates the automated identification and matching of
requests and offers to streamline relief operations. This study addresses the
challenge of efficiently identifying and matching assistance requests and
offers on social media platforms during emergencies. We propose CReMa (Crisis
Response Matcher), a systematic approach that integrates textual, temporal, and
spatial features for multi-lingual request-offer matching. By leveraging
CrisisTransformers, a set of pre-trained models specific to crises, and a
cross-lingual embedding space, our methodology enhances the identification and
matching tasks while outperforming strong baselines such as RoBERTa, MPNet, and
BERTweet, in classification tasks, and Universal Sentence Encoder, Sentence
Transformers in crisis embeddings generation tasks. We introduce a novel
multi-lingual dataset that simulates scenarios of help-seeking and offering
assistance on social media across the 16 most commonly used languages in
Australia. We conduct comprehensive cross-lingual experiments across these 16
languages, also while examining trade-offs between multiple vector search
strategies and accuracy. Additionally, we analyze a million-scale geotagged
global dataset to comprehend patterns in relation to 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.
