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 multi-lingual settings, despite the fact that any geographical area can have a diverse linguistic population. Therefore, we propose CReMa (Crisis Response Matcher), 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 pre-trained model and a multi-lingual embedding space. We emulate human decision-making to compute temporal and spatial features and non-linearly weigh the textual features. The results from our experiments are promising, outperforming strong baselines. Additionally, we introduce a novel multi-lingual 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.

翻译：在危机时期，社交媒体平台在促进沟通和协调资源方面发挥着关键作用。在混乱和不确定的环境中，社区通常依赖这些平台分享紧急求助信息、提供支持并组织救援工作。然而，此类时期的海量对话信息可能达到前所未有的规模，亟需通过自动化识别与匹配求助与援助信息来优化救援行动。此外，尽管任何地理区域都可能存在多元语言群体，但目前仍明显缺乏针对多语言环境的相关研究。为此，我们提出CReMa（危机响应匹配器），这是一种集成文本、时空特征的系统性方法，旨在应对紧急情况下社交媒体平台中求助与援助信息高效识别与匹配的挑战。我们的方法采用针对危机场景预训练的模型和多语言嵌入空间，通过模拟人类决策机制计算时空特征并对文本特征进行非线性加权。实验结果表明该方法性能优异，超越了现有强基线模型。此外，我们构建了一个新颖的多语言数据集，模拟16种语言在社交媒体上的求助与援助行为，并开展了全面的跨语言实验。进一步地，我们分析了百万级地理标记的全球数据集，以探究社交媒体上求助与援助行为的模式特征。总体而言，这些研究推进了危机信息学领域的发展，并为该领域的未来研究提供了基准参照。