Comparing COVID-19 vaccine passports attitudes across countries by analysing Reddit comments

Abstract

Topic mining and sentiment polarity analysis together can adequately represent the topics and attitudes of users. The goal of this article is to use Reddit’s location-based subreddits to look at country-level differences in attitudes towards COVID-19 vaccine passports. We used sentiment analysis and latent topic modelling on textual data obtained from 18 Reddit communities concentrating on COVID-19 vaccine passports from 1 January 2021 to 28 February 2022 to study COVID-19 vaccine passports–related discussion on Reddit. To discover changes in sentiment and latent topics, 11,168 comments were aggregated and examined by month. The number of comments on postings from country-specific subreddits was positively proportional to the number of new COVID-19 cases reported each day. The more subjective expressions and positive/negative interpretations occurred after July 2021. Communities indicated more positive sentiment than negative sentiment towards vaccine passports–related topics, according to polarity analysis. Topic modelling found that community members were concerned about a variety of concerns related to their socioeconomic status. Throughout the topic modelling, keywords suggesting people’s privacy concerns and acceptance of various COVID-19 control methods were found. The use of public opinion and topic modelling to analyse vaccine passports could help with important global health informatics concerns associated with their socioeconomic status.

Constructing a spatiotemporal situational awareness framework to sense the dynamic evolution of online public opinion on social media

“…Compared with traditional online public opinion analysis, this article considers temporal, spatial and intertwined spatiotemporal perspectives, overcoming the limitation that previous analysis of public opinion has always ignored spatial analysis. In addition, unlike the traditional subjective judgement of opinion sentiment (Yurtsever et al , 2023), this article uses BERT_BiLSTM to achieve a more fine-grained classification of opinion sentiment, which can provide richer and more detailed results on the perception of opinion sentiment. The synergistic analysis of topic and sentiment benefits event processing, especially in crisis event situations (Zhou and Zhang, 2017).…”

“…For the analysis of opinion sentiment, most existing research either applied existing tools (i.e. SnowNLP, TextBlob) or sentiment vocabulary ontology for sentiment score calculation (Rodríguez-Ibánez et al , 2023; Yurtsever et al , 2023), or used machine learning algorithms for sentiment classification (Ragini et al , 2018). The former is unsuitable for rapidly updated social media user-generated content, whereas the latter sentiment analysis is too coarse-grained to reveal the fine-grained reactions that portray the public.…”

Recent trends in vaccine coverage and confidence: A cause for concern

“… 33–35 Concerns about state control have also featured strongly, ranging from those around vaccine passports and mandates and the corresponding electronic infrastructure that has been built 36 to rumors around vaccination and depopulation. 37–39 Vaccine safety anxieties and beliefs about the compatibility of vaccines with religious beliefs were also prominent, with the use of aborted fetal cell lines in vaccine development and production often cited as reasons for vaccine refusal. 40 …”

Ethic wars: student and educator attitudes in the context of ChatGPT

“…Online learning has traditionally been seen as an option that could benefit a particular demographic of students who are noticeably older and have more familial, financial, and employment-related commitments. The COVID-19 pandemic [1][2][3], which caught everyone off guard in December 2019, however, drastically altered the educational landscape in a matter of weeks [4]. In order to immediately respond to the challenges provided by the pandemic, several higher education institutions have switched to online coursework, testing, and project.…”