The Course of News Events: A Comparison of Bottom-Up and Top-Down Approaches for Collecting Text-Based Data about Disasters
arXiv:2607.00849v1 Announce Type: new Abstract: News articles are an important source of information on disaster impacts and adaptation. A key methodological challenge in socio-environmental studies is how to select a representative data sample. Two approaches are common: querying news databases top-down with the aid of an existing disaster inventory or using NLP methods to cluster news texts bottom-up based on temporal and spatial features. Using a dataset of German news about landslides worldwide, we compare these approaches and discuss variations in event coverage. Such research design deci
The proliferation of AI and NLP methods allows for more sophisticated and comparative analyses of data collection methodologies for socio-environmental events.
Improving data collection methods for disaster impacts through AI-powered NLP can enhance response strategies, resource allocation, and adaptation planning for various stakeholders.
This research provides a framework for evaluating and optimizing how news data is used to understand disaster events, potentially leading to more accurate and timely information for decision-makers.
- · Socio-environmental researchers
- · Disaster management agencies
- · NLP developers
- · Traditional manual data aggregators
- · Outdated news database querying methods
More accurate and efficient identification of disaster events and their impacts from journalistic sources.
Improved predictive models for disaster preparedness and resource deployment due to better input data.
Enhanced global understanding of climate change impacts and localized vulnerabilities, influencing policy and investment.
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