Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy.
Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa, Italy.
Hum Vaccin Immunother. 2020 May 3;16(5):1062-1069. doi: 10.1080/21645515.2020.1714311. Epub 2020 Mar 2.
Social media have become a common way for people to express their personal viewpoints, including sentiments about health topics. We present the results of an opinion mining analysis on vaccination performed on Twitter from September 2016 to August 2017 in Italy. Vaccine-related tweets were automatically classified as against, in favor or neutral in respect of the vaccination topic by means of supervised machine-learning techniques. During this period, we found an increasing trend in the number of tweets on this topic. According to the overall analysis by category, 60% of tweets were classified as neutral, 23% against vaccination, and 17% in favor of vaccination. Vaccine-related events appeared able to influence the number and the opinion polarity of tweets. In particular, the approval of the decree introducing mandatory immunization for selected childhood diseases produced a prominent effect in the social discussion in terms of number of tweets. Opinion mining analysis based on Twitter showed to be a potentially useful and timely sentinel system to assess the orientation of public opinion toward vaccination and, in future, it may effectively contribute to the development of appropriate communication and information strategies.
社交媒体已成为人们表达个人观点的常用方式,包括对健康主题的看法。我们展示了 2016 年 9 月至 2017 年 8 月期间在意大利对 Twitter 上关于疫苗接种的意见挖掘分析结果。利用有监督的机器学习技术,自动将与疫苗相关的推文分类为针对疫苗接种主题的反对、支持或中立。在此期间,我们发现关于该主题的推文数量呈上升趋势。根据总体分类分析,60%的推文被归类为中立,23%的推文反对疫苗接种,17%的推文支持疫苗接种。与疫苗相关的事件似乎能够影响推文的数量和意见极性。特别是,批准引入针对某些儿童疾病的强制性免疫接种的法令,在社交媒体讨论中对推文数量产生了显著影响。基于 Twitter 的意见挖掘分析被证明是一种潜在有用且及时的监测系统,可评估公众对疫苗接种的看法,并在未来可能有效地为制定适当的沟通和信息策略做出贡献。