THEME 5 | Human Dynamics Upon Crisis
| 25 AUGUST 2021 (Wednesday) - Singapore Time | |
| 14:00 - 15:30 | THEME 5 | HUMAN DYNAMICS UPON CRISIS |
| Moderator: Tabarez Neyazi | What the Public Feels and Thinks during an Epidemic Crisis
Feng Chen-Chieh | Department of Geography, NUS |
| Attitude Polarization toward Immigrants: The Role of COVID-19 Misinformation Salience
Michelle See | Department of Psychology, NUS |
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| Modeling Tourist Distribution based on Geotagged Social Media Data for Facilitating Disaster and Crisis ManagementYan Yingwei | Department of Geography, NUS |
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Abstracts
What the Public Feels and Thinks during an Epidemic Crisis
Associate Professor Feng Chen-Chieh
During COVID-19, all affected countries have taken a series of contingent measures to prevent the virus from fast-spreading. Singapore is one of the countries in the first wave of the international COVID-19 spreading in January 2020, and as of May 24, one of the countries with the highest prevalence of COVID-19. It entered “Circuit Breaker” (CB) period on April 7 during which most workplaces were closed and all schools moved to full home-based learning. While this mobility restriction evidently changed the daily routine of the residents, how people are impacted psychologically by CB is less known. The presentation will look at the social-psychological impacts of COVID-19 to Singapore during different phases of CB and the reopening, with specific attention to the dynamic patterns of residents' sentiment and explores the contexts of such sentiment with topic modelling.
Attitude Polarization toward Immigrants: The Role of COVID-19 Misinformation Salience
Associate Professor Michelle See
With the increase in globalization, immigration has become a controversy in many parts of the world. Moreover, as COVID-19 has led governments to tighten border control, and caused individuals to look for someone to blame, discrimination against members of groups that are disliked in their host culture can occur. The current experiment examines whether the extent to which global orientation predicts acceptance of immigrants from mainland China in Singapore becomes intensified when COVID-19 misinformation is salient. Singaporeans were asked to read a description of the Protection from Online Falsehoods and Manipulation Act (POFMA). They were randomly assigned to a COVID-19 misinformation salience condition or a control condition. They were then asked to report their willingness to interact with mainland Chinese immigrants (e.g., accept one as a colleague, boss, or marriage partner). Given the salience of COVID-19 misinformation, the positive relationship between global orientation and acceptance toward mainland Chinese immigrants was even stronger, thus suggesting that COVID-19 causes the polarization of pre-existing tendencies toward mainland Chinese immigrants in Singapore. Moreover, support for an assimilation ideology increased when COVID-19 was salient. Additional findings suggest that other outcomes such as high concern about fake news, high trust in government, moderate trust in news websites and television news, and low trust in social media were not impacted by the salience of COVID-19.
Modeling Tourist Distribution based on Geotagged Social Media Data for Facilitating Disaster and Crisis Management
Dr Yan Yingwei
More than 10 years after the term volunteered geographic information (VGI) was coined to refer to a collage of concepts, actions, and technologies revolving around collectively volunteering geo-referenced data and using these data, VGI has become one of the most important research topics in GIScience. Aided by the rapid advancements in information technologies, collecting geo-referenced data in various forms, be it quantitative or qualitative, and sharing them among communities have been made easy. These in turns have fueled the interests in using VGI for a wide range of purposes, including disaster and crisis management. This research proposes to couple maximum entropy modeling with Flickr social media data (a type of VGI) to determine the geographic distribution of tourists for facilitating disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. The tourist geographic distribution in the study area was modeled by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events.
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