The Course of News Events: A Comparison of Bottom-Up and Top-Down Approaches for Collecting Text-Based Data about Disasters
For researchers in socio-environmental studies, this paper highlights methodological biases in disaster news sampling, but the findings are incremental and domain-specific.
This study compares top-down (using disaster inventories) and bottom-up (NLP clustering) approaches for collecting news data on disasters, finding that the choice significantly affects event coverage and sample representativeness.
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 decision can influence the resulting news sample, affecting its use in studies of inequality in media coverage, disaster monitoring and inventory enrichment.