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Past studies in the domains of information systems have analyzed the potential and barriers of social media in emergencies. While information disseminated in social media can lead to valuable insights, emergency services and researchers face the challenge of information overload as data quickly exceeds the manageable amount. We propose an embedding-based clustering approach and a method for the automated labeling of clusters. Given that the clustering quality is highly dependent on embeddings, we evaluate 19 embedding models with respect to time, internal cluster quality, and language invariance. The results show that it may be sensible to use embedding models that were already trained on other crisis datasets. However, one must ensure that the training data generalizes enough so that the clustering can adapt to new situations. Confirming this, we found out that some embedding models were not able to perform as well on a German dataset as on an English dataset.

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Information Overload in Crisis Management: Bilingual Evaluation of Embedding Models for Clustering Social Media Posts in Emergencies

  • 章节

  • 发表日期：

    2025年

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  • Markus Bayer

摘要

Past studies in the domains of information systems have analyzed the potential and barriers of social media in emergencies. While information disseminated in social media can lead to valuable insights, emergency services and researchers face the challenge of information overload as data quickly exceeds the manageable amount. We propose an embedding-based clustering approach and a method for the automated labeling of clusters. Given that the clustering quality is highly dependent on embeddings, we evaluate 19 embedding models with respect to time, internal cluster quality, and language invariance. The results show that it may be sensible to use embedding models that were already trained on other crisis datasets. However, one must ensure that the training data generalizes enough so that the clustering can adapt to new situations. Confirming this, we found out that some embedding models were not able to perform as well on a German dataset as on an English dataset.