Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee at Knoxville, Knoxville, TN, United States.
Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States.
J Med Internet Res. 2022 Oct 17;24(10):e40408. doi: 10.2196/40408.
The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies.
Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms-Reddit and Twitter-harvested from January 1, 2020, to March 1, 2022.
To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline.
Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic.
Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.
新型冠状病毒（COVID-19）的出现以及人群的必要隔离导致大量新的社交媒体用户寻求与大流行相关的信息。目前，全球社交媒体用户估计有 45 亿，社交媒体数据为分析与疾病爆发和疫苗接种相关的大量文本提供了近实时分析的机会。这些分析可被官员用于制定适当的公共卫生信息、数字干预措施、教育材料和政策。
我们的研究调查和比较了 2020 年 1 月 1 日至 2022 年 3 月 1 日在两个流行的社交媒体平台 Reddit 和 Twitter 上与 COVID-19 疫苗相关的公众情绪。
为了完成这项任务，我们创建了一个微调的 DistilRoBERTa 模型，以预测大约 950 万条推文和 7 万条 Reddit 评论的情绪。为了对我们的模型进行微调，我们的团队手动标记了 3600 条推文的情绪，然后通过回译扩充了我们的数据集。然后，我们使用 Python 编程语言和 Hugging Face 情感分析管道，使用我们的微调模型对每个社交媒体平台的文本情绪进行分类。
我们的结果确定，Twitter 上表达的平均情绪比正面情绪更为负面（5215830/9518270，54.8%），而 Reddit 上表达的情绪比负面情绪更为正面（42316/67962，62.3%）。尽管发现这两个社交媒体平台之间的平均情绪存在差异，但它们都显示出了与大流行期间与疫苗相关的关键发展相关的情绪分享相似的行为。
考虑到社交媒体平台上分享的情绪存在相似趋势，Twitter 和 Reddit 仍然是公共卫生官员可以用来增强疫苗信心和对抗错误信息的有价值的数据来源。由于错误信息的传播带来了一系列心理和社会心理风险（焦虑和恐惧等），因此迫切需要了解公众对共享虚假信息的看法和态度。针对特定人群表达的情绪量身定制的综合教育交付系统，促进数字素养、健康信息搜索行为和精准健康促进，可能有助于澄清此类错误信息。
