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.

在应对危机时，社交媒体平台起着至关重要的作用，通过促进沟通和协调资源。本研究提出了一个系统方法 CReMa 来高效地在社交媒体平台上识别和匹配援助请求和提供，利用危机专用模型和跨语言嵌入空间，从而优化识别和匹配任务，并在分类和危机嵌入生成任务中胜过强基线算法。同时，我们还介绍了一个模拟澳大利亚 16 种常用语言的社交媒体求助和援助场景的多语言数据集，并通过全面的跨语言实验证明了我们的方法。此外，我们还分析了一个包含百万级地理标记全球数据集，以了解社交媒体上的求助和援助模式。总的来说，这些研究成果推动了危机信息学领域的发展，并为未来的研究提供了基准。

CReMa：通过计算机识别和匹配社交媒体上的跨语言请求和提供以进行危机响应

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

We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.

本研究介绍了一种基于卷积神经网络的密集对应关系匹配方法，使用邻域一致性匹配模式在两幅图像所有可能的对应关系的 4D 空间中鉴别一组具有空间一致性的对应关系。该方法不需要全局几何模型，只需要弱监督的成对图像匹配信息进行训练。最后，该方法在类别匹配和实例级匹配任务中均取得了最优结果。