[1] Aarons, G. A., Hurlburt, M., & Horwitz, S. M. (2011). Advancing a Conceptual Model of Evidence-Based Practice Implementation in Public Service Sectors. Administration and Policy in Mental Health and Mental Health Services Research, 38, 4-23.
https://doi.org/10.1007/s10488-010-0327-7
[2] Ahmad, N., & Siddique, J. (2017). Personality Assessment Using Twitter Tweets. Procedia Computer Science, 112, 1964-1973.
https://doi.org/10.1016/j.procs.2017.08.067
[3] Al-Dmour, H., Salman, A., Abuhashesh, M., & Al-Dmour, R. (2020). Influence of Social Media Platforms on Public Health Protection against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model. Journal of Medical Internet Research, 22, Article ID: e19996.
https://doi.org/10.2196/19996
[4] Allgaier, J., & Svalastog, A. L. (2015). The Communication Aspects of the Ebola Virus Disease Outbreak in Western Africa—Do We Need to Counter One, Two, or Many Epidemics? Croatian Medical Journal, 56, 496-499.
https://doi.org/10.3325/cmj.2015.56.496
[5] Amirthalingam, G., Whitaker, H., Brooks, T., Brown, K., Hoschler, K., Linley, E. et al. (2021). Seroprevalence of SARS-CoV-2 among Blood Donors and Changes after Introduction of Public Health and Social Measures, London, UK. Emerging Infectious Diseases, 27, 1795-1801.
https://doi.org/10.3201/eid2707.203167
[6] Ampofo, L., Collister, S., O’Loughlin, B., Chadwick, A., Halfpenny, P. J., & Procter, P. J. (2015). Text Mining and Social Media: When Quantitative Meet Qualitative and Software Meet People. In P. Halfpenny & R. Procter (Eds.), Innovations in Digital Research Methods (pp. 161-192). Sage.
https://doi.org/10.4135/9781473920651.n8
[7] Ayouni, I., Maatoug, J., Dhouib, W., Zammit, N., Fredj, S. B., Ghammam, R., & Ghannem, H. (2021). Effective Public Health Measures to Mitigate the Spread of COVID-19: A Systematic Review. BMC Public Health, 21, Article ID: 1015.
https://doi.org/10.1186/s12889-021-11111-1
[8] Bennett, G. G., & Glasgow, R. E. (2009). The Delivery of Public Health Interventions via the Internet: Actualizing Their Potential. Annual Review of Public Health, 30, 273-292.
https://doi.org/10.1146/annurev.publhealth.031308.100235
[9] Boon-Itt, S., & Skunkan, Y. (2020). Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health and Surveillance, 6, Article ID: e21978.
https://doi.org/10.2196/21978
[10] Chandrasekaran, R., Mehta, V., Valkunde, T., & Moustakas, E. (2020). Twitter Talks on COVID-19: A Temporal Examination of Topics, Trends, and Sentiments. Journal of Medical Internet Research, 22, Article ID: e22624.
https://doi.org/10.2196/22624
[11] Chun, S. A., Li, A. C. Y., Toliyat, A., & Geller, J. (2020). Tracking Citizen’s Concerns during the COVID-19 Pandemic. In S.-J. Eom, & J. Lee (Eds.), The 21st Annual International Conference on Digital Government Research (pp. 322-323). Association for Computing Machinery.
https://doi.org/10.1145/3396956.3397000
[12] Daoust, J. F., Nadeau, R., Dassonneville, R., Lachapelle, E., Bélanger, é., Savoie, J., & van der Linden, C. (2021). How to Survey Citizens’ Compliance with COVID-19 Public Health Measures: Evidence from Three Survey Experiments. Journal of Experimental Political Science, 8, 310-317.
https://doi.org/10.1017/XPS.2020.25
[13] Dickerson, M. (2018). A Gentle Introduction to Text Analysis with Voyant Tools. eScholarship, University of California.
[14] Du, J., Xu, J., Song, H., Liu, X., & Tao, C. (2017). Optimization on Machine Learning Based Approaches for Sentiment Analysis on HPV Vaccines Related Tweets. Journal of Biomedical Semantics, 8, Article No. 9.
https://doi.org/10.1186/s13326-017-0120-6
[15] Dunn, A. G., Mandl, K. D., & Coiera, E. (2018). Social Media Interventions for Precision Public Health: Promises and Risks. NPJ Digital Medicine, 1, Article No. 47.
https://doi.org/10.1038/s41746-018-0054-0
[16] Figueira, O., Hatori, Y., Liang, L., Chye, C., & Liu, Y. (2021). Understanding COVID-19 Public Sentiment towards Public Health Policies Using Social Media Data. In 2021 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 8-15). Institute of Electrical and Electronics Engineers.
https://doi.org/10.1109/GHTC53159.2021.9612509
[17] Giustini, D., Ali, S. M., Fraser, M., & Boulos, M. N. K. (2018). Effective Uses of Social Media in Public Health and Medicine: A Systematic Review of Systematic Reviews. Online Journal of Public Health Informatics, 10.
https://doi.org/10.5210/ojphi.v10i2.8270
[18] Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, K., Martinez-Hernandez, V., Sanchez, V., & Perez-Meana, H. (2018). A Web Scraping Methodology for Bypassing Twitter API Restrictions. arXiv preprint arXiv:1803.09875.
[19] Himelboim, I., Xiao, X., Lee, D. K. L., Wang, M. Y., & Borah, P. (2020). A Social Networks Approach to Understanding Vaccine Conversations on Twitter: Network Clusters, Sentiment, and Certainty in HPV Social Networks. Health Communication, 35, 607-615.
https://doi.org/10.1080/10410236.2019.1573446
[20] HT Tech (2012, September). Twitter Guide: Express Yourself in 140 Characters.
https://tech.hindustantimes.com/tech/news/twitter-guide-express-yourself-in-140-characters-story-Ylqe0cYn6mZzrP4IJvbsuN.html
[21] Ji, X., Chun, S., Wei, Z., & Geller, J. (2015). Twitter Sentiment Classification for Measuring Public Health Concerns. Social Network Analysis and Mining, 5, Article No. 13.
https://doi.org/10.1007/s13278-015-0253-5
[22] Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E. et al. (2020). Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus, 12, Article ID: e7255.
https://doi.org/10.7759/cureus.7255
[23] Le, N. K., Le, A. V., Brooks, J. P., Khetpal, S., Liauw, D., Izurieta, R., & Reina Ortiz, M. (2020). Impact of Government-Imposed Social Distancing Measures on COVID-19 Morbidity and Mortality around the World. Bulletin of the World Health Organization, 10, 1-20.
[24] Lin, Y., Hu, Z., Alias, H., & Wong, L. P. (2020). Influence of Mass and Social Media on Psychobehavioral Responses among Medical Students during the Downward Trend of COVID-19 in Fujian, China: Cross-Sectional Study. Journal of Medical Internet Research, 22, Article ID: e19982.
https://doi.org/10.2196/19982
[25] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5, 1-167.
https://doi.org/10.2200/S00416ED1V01Y201204HLT016
[26] Liu, Q., Zheng, Z., Zheng, J., Chen, Q., Liu, G., Chen, S. et al. (2020). Health Communication through News Media during the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. Journal of Medical Internet Research, 22, Article ID: e19118.
https://doi.org/10.2196/19118
[27] Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends. JMIR Public Health and Surveillance, 6, Article ID: e19447.
https://doi.org/10.2196/19447
[28] Mackey, T., Purushothaman, V., Li, J., Shah, N., Nali, M., Bardier, C. et al. (2020). Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated with COVID-19 on Twitter: Retrospective Big Data Infoveillance Study. JMIR Public Health and Surveillance, 6, Article ID: e19509.
https://doi.org/10.2196/19509
[29] Maqbool, A., & Khan, N. Z. (2020). Analyzing Barriers for Implementation of Public Health and Social Measures to Prevent the Transmission of COVID-19 Disease using DEMATEL Method. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14, 887-892.
https://doi.org/10.1016/j.dsx.2020.06.024
[30] Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication. Journal of Medical Internet Research, 15, Article No. e85.
https://doi.org/10.2196/jmir.1933
[31] Moran, M. (2022, January 20). 20 Top Twitter Statistics: Usage, Demographics, Trends.
https://startupbonsai.com/twitter-statistics/
[32] Padidar, S., Liao, S. M., Magagula, S., Mahlaba, T. A. A., Nhlabatsi, N. M., & Lukas, S. (2021). Assessment of Early COVID-19 Compliance to and Challenges with Public Health and Social Prevention Measures in the Kingdom of Eswatini, Using an Online Survey. PLoS ONE, 16, Article ID: e0253954.
https://doi.org/10.1371/journal.pone.0253954
[33] Park, A., Conway, M., & Chen, A. T. (2018). Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Computers in Human Behavior, 78, 98-112.
https://doi.org/10.1016/j.chb.2017.09.001
[34] Pavlatos, C., & Vita, V. (2016). Linguistic Representation of Power System Signals. In P. Karampelas, & L. Ekonomou (Eds.), Electricity Distribution (pp. 285-295). Springer.
https://doi.org/10.1007/978-3-662-49434-9_12
[35] Porumbescu, G. A. (2016). Linking Public Sector Social Media and E-Government Website Used to Trust in Government. Government Information Quarterly, 33, 291-304.
https://doi.org/10.1016/j.giq.2016.04.006
[36] Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Flammini, A., & Menczer, F. (2011). Detecting and Tracking Political Abuse in Social Media. In Proceedings of the International AAAI Conference on Web and social media (Vol. 5, pp. 297-304).
[37] Sampsel, L. J. (2018). Voyant Tools. Music Reference Services Quarterly, 21, 153-157.
https://doi.org/10.1080/10588167.2018.1496754
[38] Saunders, G. H., Christensen, J. H., Gutenberg, J., Pontoppidan, N. H., Smith, A., Spanoudakis, G., & Bamiou, D. E. (2020). Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing. Ear and Hearing, 41, 1057-1063.
https://doi.org/10.1097/AUD.0000000000000850
[39] Seeman, N., & Rizo, C. (2010). Assessing and Responding in Real Time to Online Anti-Vaccine Sentiment during a Flu Pandemic. Healthcare Quarterly, 13, 8-15.
[40] Semenza, J. C., Adlhoch, C., Baka, A., Broberg, E., Cenciarelli, O., De Angelis, S. et al. (2021). COVID-19 Research Priorities for Non-Pharmaceutical Public Health and Social Measures. Epidemiology & Infection, 149, Article No. e87.
https://doi.org/10.1017/S0950268821000716
[41] Sharevski, F. (2022). (Mis)perceptions and Engagement on Twitter: COVID-19 Vaccine Rumors on Efficacy and Mass Immunization Effort. International Journal of Information Management Data Insights, 2, Article ID: 100059.
https://doi.org/10.1016/j.jjimei.2022.100059
[42] Shen, C. W., Chen, M., & Wang, C. C. (2019). Analyzing the Trend of O2O Commerce by Bilingual Text Mining on Social Media. Computers in Human Behavior, 101, 474-483.
https://doi.org/10.1016/j.chb.2018.09.031
[43] Shi, W., Liu, D., Yang, J., Zhang, J., Wen, S., & Su, J. (2020). Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the Covid-19 Pandemic Discussions on Twitter. International Journal of Environmental Research and Public Health, 17, Article No. 8701.
https://doi.org/10.3390/ijerph17228701
[44] Spangler, K. J., & Caldwell, L. L. (2007). The Implications of Public Policy Related to Parks, Recreation, and Public Health: A Focus on Physical Activity. Journal of Physical Activity and Health, 4, S64-S71.
https://doi.org/10.1123/jpah.4.s1.s64
[45] Talic, S., Shah, S., Wild, H., Gasevic, D., Maharaj, A., Ademi, Z. et al. (2021). Effectiveness of Public Health Measures in Reducing the Incidence of Covid-19, SARS-CoV-2 Transmission, and Covid-19 Mortality: Systematic Review and Meta-Analysis. BMJ, 375, Article ID: e068302.
[46] Tan, J. Y., Conceicao, E. P., Sim, X. Y. J., Wee, L. E. I., Aung, M. K., & Venkatachalam, I. (2020). Public Health Measures during COVID-19 Pandemic Reduced Hospital Admissions for Community Respiratory Viral Infections. Journal of Hospital Infection, 106, 387-389.
https://doi.org/10.1016/j.jhin.2020.07.023
[47] Thelwall, M. (2017). The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength. In J. Holyst (Ed.), Cyberemotions (pp. 119-134). Springer.
https://doi.org/10.1007/978-3-319-43639-5_7
[48] Thelwall, M. (2018). Gender Bias in Sentiment Analysis. Online Information Review, 42, 45-57.
https://doi.org/10.1108/OIR-05-2017-0139
[49] Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter Events. Journal of the American Society for Information Science and Technology, 62, 406-418.
https://doi.org/10.1002/asi.21462
[50] Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment Strength Detection for the Social Web. Journal of the American Society for Information Science and Technology, 63, 163-173.
https://doi.org/10.1002/asi.21662
[51] Thelwall, M., Buckley, K., Paltoglou, G., Skowron, M., Garcia, D., Gobron, S. et al. (2013). Damping Sentiment Analysis in Online Communication: Discussions, Monologs and Dialogs. In A. Gelbukh (Ed.), International Conference on Intelligent Text Processing and Computational Linguistics (pp. 1-12). Springer.
https://doi.org/10.1007/978-3-642-37256-8_1
[52] Vanagas, G., Bala, M., & Lhachimi, S. K. (2017). Evidence-Based Public Health 2017. BioMed Research International, 2017, Article ID: 2607397.
https://doi.org/10.1155/2017/2607397
[53] World Health Organization (WHO) (2020a). Considerations for School-Related Public Health Measures in the Context of COVID-19: Annex to Considerations in Adjusting Public Health and Social Measures in the Context of COVID-19, 14 September 2020. No. WHO/2019-nCoV/Adjusting_PH_measures/Schools/2020.2, World Health Organization.
[54] World Health Organization (WHO) (2020b). Overview of Public Health and Social Measures in the Context of COVID-19: Interim Guidance, 18 May 2020. No. WHO/2019-nCoV/PHSM_Overview/2020.1, World Health Organization.
[55] World Health Organization (WHO) (2020c, March 16). WHO Director-General’s Opening Remarks at the Media Briefing on Covid-19—16 March 2020.
https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---16-march-2020
[56] Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twitter Discussions and Emotions about the COVID-19 Pandemic: Machine Learning Approach. Journal of Medical Internet Research, 22, Article ID: e20550.
https://doi.org/10.2196/20550
[57] Zadeh, A. H., Zolbanin, H. M., Sharda, R., & Delen, D. (2019). Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis. Information Systems Frontiers, 21, 743-760.
https://doi.org/10.1007/s10796-018-9893-0