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Title: An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a Pandemic
During the COVID-19 health crisis, local public officials continue to expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health officials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health officials’ situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a possible starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results offer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness.
Authors:
; ; ; ;
Award ID(s):
1951917
Publication Date:
NSF-PAR ID:
10281194
Journal Name:
Proceedings of the International ISCRAM Conference
ISSN:
2411-3387
Sponsoring Org:
National Science Foundation
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