Using Machine Learning–Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions

Abstract

Background The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)–based methods. Methods Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

Online health search via multi-dimensional information quality assessment based on deep language models

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Trajectories of and spatial variations in HPV vaccine discussions on Weibo, 2018-2023: a deep learning analysis

“…Our study also contributes to address a methodological issue reported in previous social media listening research, that performance of DL algorithms would be hurt by training dataset with highly imbalanced label distribution. 42,43 Since some framework constructs are imbalanced labeled in our social media data, we alleviated this issue to improve the DL performance by integrating an advanced few-shot DL approach (SetFit in our study) and down-sampling technique. The few-shot approach reduces the requirement for manually labeled data and provides the opportunity of designing training dataset using diverse strategies.…”

AVPCancerFree : Impact of a digital behavior change intervention on parental HPV vaccine –related perceptions and behaviors

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