Measuring and Analyzing Community Resilience During COVID-19 Using Social Media
| dc.contributor.author | Valinejad, Jaber | en |
| dc.contributor.committeechair | Cho, Jin-Hee | en |
| dc.contributor.committeemember | Chen, Ing-Ray | en |
| dc.contributor.committeemember | Lu, Chang-Tien | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2022-01-14T14:30:57Z | en |
| dc.date.available | 2022-01-14T14:30:57Z | en |
| dc.date.issued | 2021-10-22 | en |
| dc.description.abstract | Community resilience (CR) has been studied as an indicator to measure how well a given community copes with a given disaster and provides policy directions on what aspects of the community should be improved with high priority. Although the impact of the COVID-19 has been serious all over the world and every aspect of our daily life, some countries have handled this disaster better than others. In this thesis, I aim to assess the effect of various news and Tweets collected during the COVID-19 pandemic on community functionality and resilience. First, we measure the community resilience (CR) in five different countries using Tweeter data and investigated how each country shows different trends of the CR, which is measured based on real or fake Tweets. We use Tweets generated in Australia (AUS), Singapore (SG), Republic of Korea (ROK), the United Kingdom (UK), and the United States (US) for Mar.-Nov. 2020 and measured the CR of each country and associated attributes for analyzing the overall trends. In the next step, we scrap and manually clean 4,952 full-text news articles from Jan. 2020 to Jun. 2021 and classify them into real, mixed, and fake news by fact-checking. Then we retrieve Tweets from 42,877,312 Tweets IDs from the same period and classify them into real, mixed, and fake Tweets using machine learning classifiers. We compare CR measured from news articles and Tweets based on three categories, namely, real, mixed, and fake. Based on the news articles and Tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution. We evaluate community wellbeing by assessing mental wellbeing and physical wellbeing while evaluating resource distribution by assessing economic resilience, infrastructural resilience, institutional resilience, and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and Tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from Tweets. | en |
| dc.description.abstractgeneral | The COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. It is critical to accurately assess the current state of community functionality and resilience under this pandemic to recover from it successfully. To accomplish this, various types of social sensing techniques, such as Tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. In this thesis, I aim to assess the effect of various news and Tweets collected during the COVID-19 pandemic on community functionality and resilience. First, we measure the community resilience (CR) in five different countries, i.e., Australia (AUS), Singapore (SG), Republic of Korea (ROK), the United Kingdom (UK), and the United States (US), for Mar.-Nov. 2020 and measured the CR of each country and associated attributes for analyzing the overall trends. In the next step, we compare CR measured from news articles and Tweets based on three categories, namely, real, mixed, and fake. We quantify CR based on two key factors, community wellbeing and resource distribution. We evaluate community wellbeing by assessing mental wellbeing and physical wellbeing while evaluating resource distribution by assessing economic resilience, infrastructural resilience, institutional resilience, and community capital. | en |
| dc.description.degree | M.S. | en |
| dc.format.medium | ETD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/107631 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en |
| dc.subject | COVID-19 | en |
| dc.subject | Community Resilience | en |
| dc.subject | Big Data | en |
| dc.subject | Social Media | en |
| dc.subject | Fake News | en |
| dc.subject | Data Analytic | en |
| dc.subject | Text Mining | en |
| dc.subject | Natural Language Processing | en |
| dc.subject | Machine learning | en |
| dc.subject | Wellbeing | en |
| dc.subject | Resource Distribution | en |
| dc.subject | Social Science | en |
| dc.subject | Social Wellbeing | en |
| dc.subject | Community Capital | en |
| dc.title | Measuring and Analyzing Community Resilience During COVID-19 Using Social Media | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Science | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | M.S. | en |