Extracting factors associated with vaccination from Twitter data and mapping to behavioral models
Social media platform, particularly Twitter, is a rich data source that allows monitoring of public opinions and attitudes toward vaccines.Established behavioral models like the 5C psychological antecedents model and the Health Belief Model (HBM) provide a well-structured framework for analyzing shi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Human Vaccines & Immunotherapeutics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21645515.2023.2281729 |
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| _version_ | 1850231690762387456 |
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| author | Md. Rafiul Biswas Zubair Shah |
| author_facet | Md. Rafiul Biswas Zubair Shah |
| author_sort | Md. Rafiul Biswas |
| collection | DOAJ |
| description | Social media platform, particularly Twitter, is a rich data source that allows monitoring of public opinions and attitudes toward vaccines.Established behavioral models like the 5C psychological antecedents model and the Health Belief Model (HBM) provide a well-structured framework for analyzing shifts in vaccine-related behavior. This study examines if the extracted data from Twitter contains valuable insights regarding public attitudes toward vaccines and can be mapped to two behavioral models. This study focuses on the Arab population, and a search was carried out on Twitter using: ’ تلقيحي OR تطعيم OR تطعيمات OR لقاح OR لقاحات’ for two years from January 2020 to January 2022. Then, BERTopicmodeling was applied, and several topics were extracted. Finally, the topics were manually mapped to the factors of the 5C model and HBM. 1,068,466 unique users posted 3,368,258 vaccine-related tweets in Arabic. Topic modeling generated 25 topics, which were mapped to the 15 factors of the 5C model and HBM. Among the users, 32.87%were male, and 18.06% were female. A significant 55.77% of the users were from the MENA (Middle East and North Africa) region. Twitter users were more inclined to accept vaccines when they trusted vaccine safety and effectiveness, but vaccine hesitancy increased due to conspiracy theories and misinformation. The association of topics with these theoretical frameworks reveals the availability and diversity of Twitter data that can predict behavioral change toward vaccines. It allows the preparation of timely and effective interventions for vaccination programs compared to traditional methods. |
| format | Article |
| id | doaj-art-01a68e4a3f2b4c8b92066ed3961d16f5 |
| institution | OA Journals |
| issn | 2164-5515 2164-554X |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Human Vaccines & Immunotherapeutics |
| spelling | doaj-art-01a68e4a3f2b4c8b92066ed3961d16f52025-08-20T02:03:27ZengTaylor & Francis GroupHuman Vaccines & Immunotherapeutics2164-55152164-554X2023-12-0119310.1080/21645515.2023.2281729Extracting factors associated with vaccination from Twitter data and mapping to behavioral modelsMd. Rafiul Biswas0Zubair Shah1Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, QatarDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, QatarSocial media platform, particularly Twitter, is a rich data source that allows monitoring of public opinions and attitudes toward vaccines.Established behavioral models like the 5C psychological antecedents model and the Health Belief Model (HBM) provide a well-structured framework for analyzing shifts in vaccine-related behavior. This study examines if the extracted data from Twitter contains valuable insights regarding public attitudes toward vaccines and can be mapped to two behavioral models. This study focuses on the Arab population, and a search was carried out on Twitter using: ’ تلقيحي OR تطعيم OR تطعيمات OR لقاح OR لقاحات’ for two years from January 2020 to January 2022. Then, BERTopicmodeling was applied, and several topics were extracted. Finally, the topics were manually mapped to the factors of the 5C model and HBM. 1,068,466 unique users posted 3,368,258 vaccine-related tweets in Arabic. Topic modeling generated 25 topics, which were mapped to the 15 factors of the 5C model and HBM. Among the users, 32.87%were male, and 18.06% were female. A significant 55.77% of the users were from the MENA (Middle East and North Africa) region. Twitter users were more inclined to accept vaccines when they trusted vaccine safety and effectiveness, but vaccine hesitancy increased due to conspiracy theories and misinformation. The association of topics with these theoretical frameworks reveals the availability and diversity of Twitter data that can predict behavioral change toward vaccines. It allows the preparation of timely and effective interventions for vaccination programs compared to traditional methods.https://www.tandfonline.com/doi/10.1080/21645515.2023.2281729COVID-19vaccinationbehaviorattitudes5C modelhealth belief model |
| spellingShingle | Md. Rafiul Biswas Zubair Shah Extracting factors associated with vaccination from Twitter data and mapping to behavioral models Human Vaccines & Immunotherapeutics COVID-19 vaccination behavior attitudes 5C model health belief model |
| title | Extracting factors associated with vaccination from Twitter data and mapping to behavioral models |
| title_full | Extracting factors associated with vaccination from Twitter data and mapping to behavioral models |
| title_fullStr | Extracting factors associated with vaccination from Twitter data and mapping to behavioral models |
| title_full_unstemmed | Extracting factors associated with vaccination from Twitter data and mapping to behavioral models |
| title_short | Extracting factors associated with vaccination from Twitter data and mapping to behavioral models |
| title_sort | extracting factors associated with vaccination from twitter data and mapping to behavioral models |
| topic | COVID-19 vaccination behavior attitudes 5C model health belief model |
| url | https://www.tandfonline.com/doi/10.1080/21645515.2023.2281729 |
| work_keys_str_mv | AT mdrafiulbiswas extractingfactorsassociatedwithvaccinationfromtwitterdataandmappingtobehavioralmodels AT zubairshah extractingfactorsassociatedwithvaccinationfromtwitterdataandmappingtobehavioralmodels |