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Twitter vaccine misinformation data


Tan, Pang-Ning; Argyris, Young; Zhang, Nan; Bashyal, Bidhan (2022), Twitter vaccine misinformation data, Dryad, Dataset,


Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this disparity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, pro-vaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics.


This dataset corresponds to social media posts collected from twitter that have been annotated as pro-vaccine (1), anti-vaccine (-1), and neutral or vaccine unrelated (0). There are 2 sets of data; with the first set collected in October, 2019 (before the pandemic) and the second set collected between November 15 to December 15, 2021 (during pandemic). The dataset was collected using the Twitter v2 API. For more information about the data collection and annotation process, please refer to the following publication:

  • Argyris, Y.A., Zhang, N., Bashyal, B., and Tan, P.-N. (2022). "Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media". To appear in the Proceedings of the IEEE International Conference on Digital Health, Barcelona, Spain.

Usage Notes

The data file is stored in CSV format. Due to Twitter policy about publishing tweets, we provide only the tweet ID and their classes.


National Institutes of Health, Award: 1R21LM013638-01