School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK.
School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK.
Int J Environ Res Public Health. 2021 Dec 10;18(24):13028. doi: 10.3390/ijerph182413028.
Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter's Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.
疫苗犹豫仍然是一个持续存在的问题，这对全球健康构成了重大威胁。SARS-CoV-2 COVID-19 疫苗也不例外，因为早在其研发阶段，就开始在社交媒体上传播错误信息。我们使用了 Python 的 Twitter 应用程序编程接口 (API)，在 2021 年 7 月 1 日至 7 月 21 日期间使用 43 个与 COVID-19 疫苗相关的搜索词，收集了 137781 条推文。使用 Microsoft Azure（一种机器学习方法）和 VADER 情感分析模型（一种基于词典的方法）对推文进行情感分析，其中自然语言处理工具包 (NLTK) 评估推文是否代表积极、消极或中性观点。大多数推文的情绪是消极的（53899 条），其次是积极的（53071 条）和中性的（30811 条）。负面推文的情绪强度高于正面推文。我们分发了一份问卷，分析发现，完全接种疫苗的人对接受疫苗的担忧较少，更有可能接受疫苗。总的来说，我们确定这种基于情绪的方法有助于确定公众对疫苗的犹豫程度，并且结合问卷，提出了应对具体问题和错误信息的策略。
