论文标题

COVID-19与疫苗相关的社交媒体数据的微调情感分析:比较研究

Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study

论文作者

Melton, Chad A, White, Brianna M, Davis, Robert L, Bednarczyk, Robert A, Shaban-Nejad, Arash

论文摘要

这项研究调查并比较了与Covid-19的公共情绪,并在两个流行的社交媒体平台上表达的疫苗Reddit和Twitter表示,从2020年1月1日收获到2022年3月1日收获。为了完成这项任务,我们创建了一个微调的Distilroberta模型,以预测约950万Tweet的观点和7000万个tew和7000千的评论。为了微调我们的模型,我们的团队手动标记了3600条推文的情感,然后通过反向翻译方法增强了我们的数据集。然后,使用Python和Huggingface情感分析管道将每个社交媒体平台的文本情感与我们的微调模型进行了分类。我们的结果确定,在Twitter上表达的平均情绪比正阳性更为负(52%),而在Reddit上表达的情绪比负面(53%的阳性)更为正面。尽管发现这些社交媒体平台之间的平均情绪有所不同,但两者在大流行期间都表现出与关键疫苗相关发展中共享的情绪相关的行为。考虑到在社交媒体平台上证明的共同情绪中的这种类似趋势,Twitter和Reddit仍然是公共卫生官员可以利用的有价值的数据来源来增强疫苗的信心和打击错误信息。由于错误信息的传播构成了一系列心理和社会心理风险(焦虑,恐惧等),因此紧迫地了解公众对共同虚假的看法和态度。针对人口量身定制的全面教育交付系统,表达的情感促进了数字素养,寻求健康信息的行为和精确的健康促进,这可能有助于澄清这种错误信息。

This study investigated and compared public sentiment related to COVID-19 vaccines expressed on two 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 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 dataset by the method of back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python and the Huggingface sentiment analysis pipeline. Our results determined that the average sentiment expressed on Twitter was more negative (52% positive) than positive and the sentiment expressed on Reddit was more positive than negative (53% positive). Though average sentiment was found to vary between these social media platforms, both displayed similar behavior related to 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 utilize to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety, fear, etc.), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to the population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.

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