Media Impact Index for Disaster Vulnerability Assessment: A Thematic Classification and Vulnerability Indexing Framework
Conference
Patel, JA, Lor, MA, Chen, SC et al. (2025). Media Impact Index for Disaster Vulnerability Assessment: A Thematic Classification and Vulnerability Indexing Framework
. 61-66. 10.1109/IRI66576.2025.00019
Patel, JA, Lor, MA, Chen, SC et al. (2025). Media Impact Index for Disaster Vulnerability Assessment: A Thematic Classification and Vulnerability Indexing Framework
. 61-66. 10.1109/IRI66576.2025.00019
This paper proposes a data-driven framework for quantifying disaster vulnerability using social media analytics, repurposing a previously collected Twitter dataset originally intended for evacuation behavior analysis. After refining the dataset to isolate signals of distress and need, a category based classification strategy is introduced in which thematic dictionaries guide the grouping of Tweets based on the semantic similarity of their embeddings. Focusing on Hurricane Dorian, a compound disaster during the COVID-19 pandemic characterized by high distress and negative sentiment, a weighted amplification factor is incorporated that prioritizes Tweet categories based on the immediacy of impact on human life, while normalizing by Tweet volume and population density. The resulting Media Impact Index (MII) is calculated at the Census Block Group (CBG) level for the United States. To demonstrate the cross-cultural flexibility of the pipeline, the same methodology is applied to Typhoon Hagibis in Japan, with a comparable vulnerability index generated at the district level. The findings suggest that the proposed framework can provide emergency management agencies with a scalable and adaptable tool for identifying and prioritizing vulnerable regions in diverse types of disasters and sociocultural contexts.
