School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States.
J Med Internet Res. 2021 Sep 9;23(9):e30451. doi: 10.2196/30451.
The vaccination uptake rates of the human papillomavirus (HPV) vaccine remain low despite the fact that the effectiveness of HPV vaccines has been established for more than a decade. Vaccine hesitancy is in part due to false information about HPV vaccines on social media. Combating false HPV vaccine information is a reasonable step to addressing vaccine hesitancy.
Given the substantial harm of false HPV vaccine information, there is an urgent need to identify false social media messages before it goes viral. The goal of the study is to develop a systematic and generalizable approach to identifying false HPV vaccine information on social media.
This study used machine learning and natural language processing to develop a series of classification models and causality mining methods to identify and examine true and false HPV vaccine-related information on Twitter.
We found that the convolutional neural network model outperformed all other models in identifying tweets containing false HPV vaccine-related information (F score=91.95). We also developed completely unsupervised causality mining models to identify HPV vaccine candidate effects for capturing risk perceptions of HPV vaccines. Furthermore, we found that false information contained mostly loss-framed messages focusing on the potential risk of vaccines covering a variety of topics using more diverse vocabulary, while true information contained both gain- and loss-framed messages focusing on the effectiveness of vaccines covering fewer topics using relatively limited vocabulary.
Our research demonstrated the feasibility and effectiveness of using predictive models to identify false HPV vaccine information and its risk perceptions on social media.
尽管 HPV 疫苗的有效性已得到证实超过十年，但 HPV 疫苗的接种率仍然很低。疫苗犹豫部分归因于社交媒体上有关 HPV 疫苗的错误信息。对抗虚假 HPV 疫苗信息是解决疫苗犹豫的合理步骤。
鉴于虚假 HPV 疫苗信息的巨大危害，迫切需要在其传播之前识别社交媒体上的虚假信息。本研究的目的是开发一种系统的、可推广的方法，以识别社交媒体上的虚假 HPV 疫苗信息。
本研究使用机器学习和自然语言处理来开发一系列分类模型和因果挖掘方法，以识别和检查 Twitter 上与 HPV 疫苗相关的真实和虚假信息。
我们发现卷积神经网络模型在识别包含虚假 HPV 疫苗相关信息的推文方面表现优于所有其他模型（F 分数=91.95）。我们还开发了完全无监督的因果挖掘模型，以识别 HPV 疫苗候选效应，以捕捉 HPV 疫苗的风险感知。此外，我们发现虚假信息主要包含损失框架信息，侧重于疫苗的潜在风险，涵盖各种主题，使用更多样化的词汇，而真实信息则包含增益和损失框架信息，侧重于疫苗的有效性，涵盖较少的主题，使用相对有限的词汇。
我们的研究证明了使用预测模型识别社交媒体上虚假 HPV 疫苗信息及其风险感知的可行性和有效性。
