# Improving Vaccine Stance Detection by Combining Online and Offline Data

arXiv: https://arxiv.org/abs/2208.04491 · HTML: https://mlsanity.com/paper/2208.04491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media](https://mlsanity.com/paper/2403.04009/) (2024)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)

## Abstract (arXiv)

Differing opinions about COVID-19 have led to various online discourses regarding vaccines. Due to the detrimental effects and the scale of the COVID-19 pandemic, detecting vaccine stance has become especially important and is attracting increasing attention. Communication during the pandemic is typically done via online and offline sources, which provide two complementary avenues for detecting vaccine stance. Therefore, this paper aims to (1) study the importance of integrating online and offline data to vaccine stance detection; and (2) identify the critical online and offline attributes that influence an individual's vaccine stance. We model vaccine hesitancy as a surrogate for identifying the importance of online and offline factors. With the aid of explainable AI and combinatorial analysis, we conclude that both online and offline factors help predict vaccine stance.