Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

Browse

Filters

20241

Settings

Author: search.filters.author.Ma, ZihuiSubject: search.filters.subject.natural language processing

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    NATURAL LANGUAGE PROCESSING, SOCIAL MEDIA, AND EPIDEMIC MODEL-ING FOR WILDFIRE RESPONSE AND RE-SILIENCE ENHANCEMENT
    (2024) Ma, Zihui; Baecher, Gregory B; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Effective disaster response is critical for communities to remain resilient and advance the development of smart cities. Responders and decision-makers benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and behaviors during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. A comprehensive literature review of natural language processing (NLP) of social media data in disaster management, covering 324 articles published between 2011 and 2022, revealed a gap in applying NLP techniques to wildfire scenarios. Meanwhile, the increasing frequency of wildfires highlights the need for advanced management tools. To address this, we integrated the BERTopic and SIR models to capture public responses on Twitter during the 2020 western U.S. wildfire season, analyzing both the magnitude and velocity of topic diffusion. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The parameters estimated from the SIR model for selected cities revealed that residents expressed various levels of concern or demand during wildfires. The study also demonstrated a practical framework for utilizing social media data to aid wildfire evacuations. Through social network analysis, we clarified the roles of key information disseminators and provided guidelines for extracting high-priority information. Although biases in social media and model limitations exist, the study offers qualitative and quantitative approaches to investigate wildfire response and sup-port community resilience enhancement.