Although the recent rise and uptake of COVID-19 vaccines in the United States has been encouraging, there continues to be significant vaccine hesitancy in various geographic and demographic clusters of the adult population. Surveys, such as the one conducted by Gallup over the past year, can be useful in determining vaccine hesitancy, but can be expensive to conduct and do not provide real-time data. At the same time, the advent of social media suggests that it may be possible to get vaccine hesitancy signals at an aggregate level, such as at the level of zip codes. Theoretically, machine learning models can be learned using socioeconomic (and other) features from publicly available sources. Experimentally, it remains an open question whether such an endeavor is feasible, and how it would compare to non-adaptive baselines. In this article, we present a proper methodology and experimental study for addressing this question. We use publicly available Twitter data collected over the previous year. Our goal is not to devise novel machine learning algorithms, but to rigorously evaluate and compare established models. Here we show that the best models significantly outperform non-learning baselines. They can also be set up using open-source tools and software.
Copyright: © 2022 Melotte, Kejriwal. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.