Healthcare workers such as doctors and nurses are expected to be trustworthy and creditable sources of vaccine-related information. Their opinions toward the COVID-19 vaccines may influence the vaccine uptake among the general population. However, vaccine hesitancy is still an important issue even among the healthcare workers. Therefore, it is critical to understand their opinions to help reduce the level of vaccine hesitancy. There have been studies examining healthcare workers' viewpoints on COVID-19 vaccines using questionnaires. Reportedly, a considerably higher proportion of vaccine hesitancy is observed among nurses, compared to doctors. We intend to verify and study this phenomenon at a much larger scale and in fine grain using social media data, which has been effectively and efficiently leveraged by researchers to address real-world issues during the COVID-19 pandemic. More specifically, we use a keyword search to identify healthcare workers and further classify them into doctors and nurses from the profile descriptions of the corresponding Twitter users. Moreover, we apply a transformer-based language model to remove irrelevant tweets. Sentiment analysis and topic modeling are employed to analyze and compare the sentiment and thematic differences in the tweets posted by doctors and nurses. We find that doctors are overall more positive toward the COVID-19 vaccines. The focuses of doctors and nurses when they discuss vaccines in a negative way are in general different. Doctors are more concerned with the effectiveness of the vaccines over newer variants while nurses pay more attention to the potential side effects on children. Therefore, we suggest that more customized strategies should be deployed when communicating with different groups of healthcare workers.
翻译:医生和护士等保健工作者预计将获得可靠和可信赖的疫苗相关信息来源。他们对COVID-19疫苗的看法可能会影响普通民众对疫苗的接种。然而,疫苗犹豫仍然是一个重要问题,即使在保健工作者中也是如此。因此,至关重要的是要了解他们的意见,以帮助降低疫苗犹豫的程度。已经研究过保健工作者对COVID-19疫苗的看法,并使用问卷。据报告,与医生相比,在护士中观察到的疫苗误食比例要高得多。我们打算用社交媒体数据来更大规模地核实和研究这种现象,并用精细谷物来研究这种现象。在COVI-19大流行病期间,研究人员有效地利用这些数据来解决现实世界的问题。更具体地说,我们用关键字搜索来查明保健工作者,并将他们进一步从相应推特用户的简介中划入医生和护士。此外,我们应用基于变换语言的模式来消除无关的推文。我们使用感官分析和主题模型来分析并比较医生和护士在推特上发表的情绪和主题差异。当医生和护士们更加积极地讨论有关疫苗时,我们发现医生和护士对疫苗的潜在作用。我们发现,医生们在总体上更加积极地讨论。