"Double vaccinated, 5G boosted!": Learning Attitudes towards COVID-19 Vaccination from Social Media

TY - JOUR

T1 - "Double vaccinated, 5G boosted!"

T2 - Learning Attitudes towards COVID-19 Vaccination from Social Media

AU - Chen, Ninghan

AU - Chen, Xihui

AU - Zhong, Zhiqiang

AU - Pang, Jun

PY - 2025/2/14

Y1 - 2025/2/14

N2 - The sudden onset of the recently concluded COVID-19 pandemic has driven substantial progress in various scientific fields. One notable example is the comprehension of public vaccination attitudes and the timely monitoring of their fluctuations through social media platforms. This approach can serve as a cost-effective means to supplement surveys in gathering public vaccine hesitancy levels. In this article, we propose a deep learning framework leveraging textual posts on social media to extract and track users' vaccination stances in near real time. Compared to previous works, we integrate into the framework the recent posts of a user's social network friends to collaboratively detect the user's genuine attitude towards vaccination. Based on our annotated dataset from X (formerly known as Twitter), the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to the state-of-the-art text-only models. Using this framework, we successfully confirm the feasibility of using social media to track the evolution of vaccination attitudes in real life. In addition, we illustrate the generality of our framework in extracting other public opinions such as political ideology. We further show one practical use of our framework by validating the possibility of forecasting a user's vaccine hesitancy changes with information perceived from social media.

AB - The sudden onset of the recently concluded COVID-19 pandemic has driven substantial progress in various scientific fields. One notable example is the comprehension of public vaccination attitudes and the timely monitoring of their fluctuations through social media platforms. This approach can serve as a cost-effective means to supplement surveys in gathering public vaccine hesitancy levels. In this article, we propose a deep learning framework leveraging textual posts on social media to extract and track users' vaccination stances in near real time. Compared to previous works, we integrate into the framework the recent posts of a user's social network friends to collaboratively detect the user's genuine attitude towards vaccination. Based on our annotated dataset from X (formerly known as Twitter), the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to the state-of-the-art text-only models. Using this framework, we successfully confirm the feasibility of using social media to track the evolution of vaccination attitudes in real life. In addition, we illustrate the generality of our framework in extracting other public opinions such as political ideology. We further show one practical use of our framework by validating the possibility of forecasting a user's vaccine hesitancy changes with information perceived from social media.

KW - COVID-19

KW - dataset

KW - graph neural networks

KW - Social media

KW - text mining

KW - vaccine hesitancy

UR - https://www.scopus.com/pages/publications/85219034966

U2 - 10.1145/3702654

DO - 10.1145/3702654

M3 - Journal article

AN - SCOPUS:85219034966

SN - 1559-1131

VL - 19

JO - ACM Transactions on the Web

JF - ACM Transactions on the Web

IS - 1

M1 - 8

ER -