Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination
Unsupervised Deep Learning of Twitter Posts from 2017 to 2022
Bibliographic Data
| ID | 22003971 |
|---|---|
| Authors | Qi Xuan Ng (0000-0001-8561-2513, Singapore General Hospital), Yu Qing Jolene Teo (Royal College of Surgeons in Ireland), Chee Yu Kiew (University College Cork), Bryant Po-Yuen Lim (NUS Yong Loo Lin School of Medicine, Singapore, Singapore), Yu Liang Lim (0000-0001-5815-0878, MOH Holdings), Tau Ming Liew (0000-0003-1555-7752, National University of Singapore, corresponding author) |
| Year | 2023 |
| Volume | 26 |
| Issue | 8 |
| Pages | 621-630 |
| Publication date | 2023-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cyberpsychology Behavior and Social Networking (JOURNAL) |
| Journal identifiers | ISSN: 2152-2715 • E-ISSN: 2152-2723 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1089/cyber.2023.0025 |
| PMID | 37358808 |
| OpenAlex | W4382011343 |
| Language | EN |
| Citations received | 1 |
| References cited | 29 |
Despite the proven safety and clinical efficacy of the Measles vaccine, many countries are seeing new heights of vaccine hesitancy or refusal, and are experiencing a resurgence of measles infections as a consequence. With the use of novel machine learning tools, we investigated the prevailing negative sentiments related to Measles vaccination through an analysis of public Twitter posts over a 5-year period. We extracted original tweets using the search terms related to "measles" and "vaccine," and posted in English from January 1, 2017, to December 15, 2022. Of these, 155,363 tweets were identified to be negative sentiment tweets from unique individuals, through the use of Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition and SieBERT, a pretrained sentiment in English analysis model. This was followed by topic modeling and qualitative thematic analysis performed inductively by the study investigators. A total of 11 topics were generated after applying BERTopic. To facilitate a global discussion of results, the topics were grouped into four different themes through iterative thematic analysis. These include (a) the rejection of "anti-vaxxers" or antivaccine sentiments, (b) misbeliefs and misinformation regarding Measles vaccination, (c) negative transference due to COVID-19 related policies, and (d) public reactions to contemporary Measles outbreaks. Theme 1 highlights that the current public discourse may further alienate those who are vaccine hesitant because of the disparaging language often used, while Themes 2 and 3 highlight the typology of misperceptions and misinformation underlying the negative sentiments related to Measles vaccination and the psychological tendency of disconfirmation bias. Nonetheless, the analysis was based solely on Twitter and only tweets in English were included; hence, the findings may not necessarily generalize to non-Western communities. It is important to further understand the thinking and feeling of those who are vaccine hesitant to address the issues at hand
Computer security · Disinformation · Measles · Measles vaccine · Misinformation · Pathology · Public health · Qualitative research · Social media · Social science · Sociology · Thematic analysis · Vaccination · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Medicine · Misinformation and Its Impacts · Psychology · Vaccine Coverage and Hesitancy · Virology
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |