Ahamed Sajid Hussain Rafi, Shakil Shahid, Lyu Hanjia, Zhang Xinping, Luo Jiebo
University of Rochester.
University of Rochester Medical Center.
Proc IEEE Int Conf Big Data. 2022 Dec;2022:5865-5870. doi: 10.1109/bigdata55660.2022.10020853.
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 . 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.
医生和护士等医护人员被期望成为与疫苗相关信息的可靠来源。他们对新冠疫苗的看法可能会影响普通人群的疫苗接种率。然而，即使在医护人员中，疫苗犹豫仍然是一个重要问题。因此，了解他们的看法对于帮助降低疫苗犹豫程度至关重要。已经有研究通过问卷调查来考察医护人员对新冠疫苗的观点。据报道，与医生相比，护士中观察到的疫苗犹豫比例要高得多。我们打算利用社交媒体数据在更大规模和更精细的层面上验证和研究这一现象，在新冠疫情期间，研究人员已经有效地利用社交媒体数据来解决现实世界的问题。更具体地说，我们使用关键词搜索来识别医护人员，并根据相应推特用户的个人资料描述将他们进一步分为医生和护士。此外，我们应用基于Transformer的语言模型来去除不相关的推文。采用情感分析和主题建模来分析和比较医生和护士发布的推文中的情感和主题差异。我们发现医生总体上对新冠疫苗更为积极。医生和护士以负面方式讨论疫苗时的关注点通常有所不同。医生更关注疫苗对新变种的有效性，而护士则更关注对儿童的潜在副作用。因此，我们建议在与不同群体的医护人员沟通时应部署更具针对性的策略。
