de Carvalho Victor Diogho Heuer, Nepomuceno Thyago Celso Cavalcante, Poleto Thiago, Costa Ana Paula Cabral Seixas
Eixo das Tecnologias, Campus do Sertão, Universidade Federal de Alagoas, Delmiro Gouveia 57480-000, Brazil.
Núcleo de Tecnologia, Centro Acadêmico do Agreste, Universidade Federal de Pernambuco, Caruaru 55014-900, Brazil.
Trop Med Infect Dis. 2022 Dec 9;7(12):425. doi: 10.3390/tropicalmed7120425.
The context of the COVID-19 pandemic has brought to light the infodemic phenomenon and the problem of misinformation. Agencies involved in managing COVID-19 immunization programs are also looking for ways to combat this problem, demanding analytical tools specialized in identifying patterns of misinformation and understanding how they have evolved in time and space to demonstrate their effects on public trust. The aim of this article is to present the results of a study applying topic analysis in space and time with respect to public opinion on the Brazilian COVID-19 immunization program. The analytical process involves applying topic discovery to tweets with geoinformation extracted from the COVID-19 vaccination theme. After extracting the topics, they were submitted to manual annotation, whereby the polarity labels pro, anti, and neutral were applied based on the support and trust in the COVID-19 vaccination. A space and time analysis was carried out using the topic and polarity distributions, making it possible to understand moments during which the most significant quantities of posts occurred and the cities that generated the most tweets. The analytical process describes a framework capable of meeting the needs of agencies for tools, providing indications of how misinformation has evolved and where its dissemination focuses, in addition to defining the granularity of this information according to what managers define as adequate. The following research outcomes can be highlighted. (1) We identified a specific date containing a peak that stands out among the other dates, indicating an event that mobilized public opinion about COVID-19 vaccination. (2) We extracted 23 topics, enabling the manual polarity annotation of each topic and an understanding of which polarities were associated with tweets. (3) Based on the association between polarities, topics, and tweets, it was possible to identify the Brazilian cities that produced the majority of tweets for each polarity and the amount distribution of tweets relative to cities populations.
新冠疫情的背景使信息疫情现象和错误信息问题凸显出来。参与管理新冠疫苗接种项目的机构也在寻找应对这一问题的方法，需要专门用于识别错误信息模式并了解其在时空上如何演变以证明其对公众信任影响的分析工具。本文旨在展示一项关于巴西新冠疫苗接种项目公众舆论的时空主题分析研究结果。分析过程包括将主题发现应用于从新冠疫苗接种主题中提取地理信息的推文。提取主题后，对其进行人工标注，根据对新冠疫苗接种的支持和信任程度应用积极、消极和中性的极性标签。利用主题和极性分布进行时空分析，从而能够了解发布推文数量最多的时刻以及产生推文最多的城市。该分析过程描述了一个能够满足各机构对工具需求的框架，除了根据管理者定义的适当程度定义这些信息的粒度外，还能提供错误信息如何演变以及其传播集中在何处的指示。可以突出以下研究成果。（1）我们确定了一个在其他日期中脱颖而出的峰值日期，表明发生了一个动员公众对新冠疫苗接种舆论的事件。（2）我们提取了23个主题，能够对每个主题进行人工极性标注，并了解哪些极性与推文相关。（3）基于极性、主题和推文之间的关联，能够确定每个极性下产生推文最多的巴西城市以及相对于城市人口的推文数量分布。
