DisasterNeedFinder: Understanding the Information Needs in the 2024 Noto Earthquake (Comprehensive Explanation)

arXiv - CS - Social and Information Networks Pub Date : 2024-09-11 DOI:arxiv-2409.07102

Kota Tsubouchi, Shuji Yamaguchi, Keijirou Saitou, Akihisa Soemori, Masato Morita, Shigeki Asou

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Abstract

We propose and demonstrate the DisasterNeedFinder framework in order to provide appropriate information support for the Noto Peninsula Earthquake. In the event of a large-scale disaster, it is essential to accurately capture the ever-changing information needs. However, it is difficult to obtain appropriate information from the chaotic situation on the ground. Therefore, as a data-driven approach, we aim to pick up precise information needs at the site by integrally analyzing the location information of disaster victims and search information. It is difficult to make a clear estimation of information needs by just analyzing search history information in disaster areas, due to the large amount of noise and the small number of users. Therefore, the idea of assuming that the magnitude of information needs is not the volume of searches, but the degree of abnormalities in searches, enables an appropriate understanding of the information needs of the disaster victims. DNF has been continuously clarifying the information needs of disaster areas since the disaster strike, and has been recognized as a new approach to support disaster areas by being featured in the major Japanese media on several occasions.

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DisasterNeedFinder：了解 2024 年能登地震的信息需求（综合说明）

为了给能登半岛地震提供适当的信息支持，我们提出并演示了 DisasterNeedFinder 框架。在发生大规模灾害时，准确捕捉不断变化的信息需求至关重要。然而，要从混乱的现场情况中获取适当的信息却十分困难。因此，作为一种数据驱动型方法，我们的目标是通过综合分析灾民的位置信息和搜索信息，准确捕捉现场的信息需求。由于灾区噪音大、用户数量少，仅通过分析搜索历史信息很难明确估计信息需求。因此，假设信息需求的大小不是搜索量，而是搜索的异常程度，就能恰当地理解灾民的信息需求。自灾害发生以来，DNF 一直在不断阐明灾区的信息需求，并作为一种新的灾区支援方法多次被日本主要媒体报道。

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arXiv - CS - Social and Information Networks

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