Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan
Bibliographic Data
| ID | 7519944 |
|---|---|
| Authors | Ting Zhang (0000-0002-6840-1367, Beijing Normal University), Changxiu Cheng (0000-0003-2988-8915, Beijing Normal University, corresponding author) |
| Year | 2021 |
| Volume | 10 |
| Issue | 5 |
| Pages | 299-299 |
| Publication date | 2021-05-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijgi10050299 |
| OpenAlex | W3158533974 |
| Language | EN |
| Citations received | 13 |
| References cited | 38 |
The public’s attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability during disasters. Social media data have become the most effective resource for studying public sentiment. The TextBlob tool is used to calculate the sentiment value of tweets, and this research analyzed the public’s sentiment state during Typhoon Haiyan, used the biterm topic model (BTM) to classify topics, explored the changing process of public discussion topics at different stages during the disaster, and analyzed the differences in people’s discussion content under different sentiments. We also analyzed the spatial pattern of sentiment and quantitatively explored the influencing factors of the sentiment spatial differences. The results showed that the overall public sentiment during Typhoon Haiyan tended to be positive, that compared with positive tweets, negative tweets contained more serious disaster information and more urgent demand information, and that the number of tweets, population, and the proportion of the young and middle-aged populations were the dominant factors in the sentiment spatial differences
Cartography · Geography · Meteorology · Natural (archaeology · Natural disaster · Typhoon · Disaster Management and Resilience · Opinion Dynamics and Social Influence · Public Relations and Crisis Communication
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| Unique citing works | 13 |
|---|---|
| Citations per year | 2,6 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 13 |