TY - GEN
T1 - Measuring COVID-19 Vaccine Hesitancy
T2 - 13th International Conference on Social Informatics, SocInfo 2022
AU - Chen, Ninghan
AU - Chen, Xihui
AU - Pang, Jun
AU - Borga, Liyousew G.
AU - D’Ambrosio, Conchita
AU - Vögele, Claus
N1 - Funding Information:
Acknowledgement. This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference PRIDE17/12252781 (DRVIVEN), C21/IS/16281848 (HETERS) and 14840950 (COME-HERE). The research is also supported by André Losch Fondation, Art2Cure, Cargolux, and CIN-VEN Foundation.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/10/12
Y1 - 2022/10/12
N2 - We validate whether social media data can be used to complement social surveys to monitor the public’s COVID-19 vaccine hesitancy. Taking advantage of recent artificial intelligence advances, we propose a framework to estimate individuals’ vaccine hesitancy from their social media posts. With 745,661 vaccine-related tweets originating from three Western European countries, we compare vaccine hesitancy levels measured with our framework against that collected from multiple consecutive waves of surveys. We successfully validate that Twitter, one popular social media platform, can be used as a data source to calculate consistent public acceptance of COVID-19 vaccines with surveys at both country and region levels. In addition, this consistency persists over time although it varies among socio-demographic sub-populations. Our findings establish the power of social media in complementing social surveys to capture the continuously changing vaccine hesitancy in a global health crisis similar to the COVID-19 pandemic.
AB - We validate whether social media data can be used to complement social surveys to monitor the public’s COVID-19 vaccine hesitancy. Taking advantage of recent artificial intelligence advances, we propose a framework to estimate individuals’ vaccine hesitancy from their social media posts. With 745,661 vaccine-related tweets originating from three Western European countries, we compare vaccine hesitancy levels measured with our framework against that collected from multiple consecutive waves of surveys. We successfully validate that Twitter, one popular social media platform, can be used as a data source to calculate consistent public acceptance of COVID-19 vaccines with surveys at both country and region levels. In addition, this consistency persists over time although it varies among socio-demographic sub-populations. Our findings establish the power of social media in complementing social surveys to capture the continuously changing vaccine hesitancy in a global health crisis similar to the COVID-19 pandemic.
KW - COVID-19 vaccine hesitancy
KW - Surveys
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85141694177&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-19097-1_12
DO - 10.1007/978-3-031-19097-1_12
M3 - Conference contribution
AN - SCOPUS:85141694177
SN - 9783031190964
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 210
BT - Social Informatics - 13th International Conference, SocInfo 2022, Proceedings
A2 - Hopfgartner, Frank
A2 - Jaidka, Kokil
A2 - Mayr, Philipp
A2 - Jose, Joemon
A2 - Breitsohl, Jan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 October 2022 through 21 October 2022
ER -