Public Health Surveillance for COVID-19 Using Twitter Sentiment Analysis
DOI: 10.21522/TIJPH.2013.12.03.Art034
Authors : Kashim I. A
Abstract:
By analyzing sentiments on Twitter/X during the COVID-19 pandemic, this study adds to the existing body of literature on new techniques to analyze social media’s ‘big data’. In this study, a sentiment classification model was developed to accurately predict public sentiments on Twitter/X. Twitter’s Apify crawler was used to scrape a sample of English language tweets from the social media platform using hashtags:#covid-19vaccine,#coronavaccine,#vaccinesaveslives, #getvaccinated, #coronavirusvaccine. The study was from December 2020 to November 2021. Data preprocessing techniques were first applied followed by a hybrid approach for sentiment analysis (both lexicon based natural language processing and machine learning). Approval for the study was received from Texila American University, ethical concerns were limited as no personalized data or human subjects were used. Data processing and analysis using PYTHON involved the use of Natural language processing techniques (VADER) to classify the sentiment and predict accuracy of the model developed. Trend analysis showed that as tweets increased with each month, tweets became negative with fear and anxiety being commonly expressed emotions. Over the study period, there was a statistically significant difference in sentiment polarity (positive p=0.000; negative p=0.02). VADER analysis predicted that this trend (increasing negative polarity with time) is likely to continue in future epidemics as correlates with existing machine learning models (Random Forest and Support Vector Machine) both validated this trend. It is evident that sentiment analysis techniques can be leveraged for the purpose of public health disease surveillance and to enable the identification of trends, forecasting public perception and behavior.References:
[1]. Cho, H., Li, P., Ngien A., Tan, M. G., Chen, A., Nekmat, E., 2023, The Bright And Dark Sides of Social Media Use During COVID-19 Lockdown: Contrasting Social Media Effects Through Social Liability vs. Social Support. Comput Human Behav. 146:107795. Doi: 10.1016/j.chb.2023.107795. PMID: 37124630; PMCID: PMC10123536.
[2]. Sahni, H. Sharma, H. 2020, Role of Social Media During the COVID-19 Pandemic: Beneficial, Destructive, or Reconstructive. International Journal of Academic Medicine 6(2):p 70-75, Doi: 10.4103/IJAM.IJAM_50_20
[3]. Abbas, J., Wang, D., Su, Z., and Ziapour, A., 2021, The Role of Social Media in the Advent of COVID-19 Pandemic: Crisis Management, Mental Health Challenges and Implications. Risk Management and Healthcare Policy. 14, 1917–1932. Available online: https://doi.org/10.2147/rmhp.s284313
[4]. Jaffery, T. N., Shan, H., Gillani, R., Hassan, U., Sehar, B., 2022, COVID 19 Vaccination Related Misconceptions and Myths. J Islamabad Med Dental Coll. 11(2):120-126.
[5]. Boon-Itt, S., Skunkan, Y., 2020, Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health Surveill, 6(4):e21978. Doi:10.2196/21978
[6]. Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. 2020, Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention. Psychological Science, 31(7), 770-780. https://doi.org/10.1177/0956797620939054
[7]. Capraro, V., & Celadin, T. 2023, “I Think This News Is Accurate”: Endorsing Accuracy Decreases the Sharing of Fake News and Increases the Sharing of Real News. Personality and Social Psychology Bulletin, 49(12), 1635-1645. https://doi.org/10.1177/01461672221117691
[8]. Mohammad, A., Kausar, A. S, Nasar, M., 2021, Public Sentiment Analysis on Twitter Data during COVID-19 Outbreak, International Journal of Advanced Computer Science and Applications, 12(2).
[9]. Tsai MH, Wang Y., 2021, Analyzing Twitter Data to Evaluate People's Attitudes towards Public Health Policies and Events in the Era of COVID-19. Int J Environ Res Public Health.18(12):6272. Doi: 10.3390/ijerph18126272. PMID: 34200576
[10]. Wang, S., Dhakal, S., and Upadhyay, B., 2024, Sentiment Analysis and Emotion Detection of COVID-19 Geo-Tagged Twitter Data," in 2024 9th International Conference on Big Data Analytics (ICBDA), Tokyo, Japan, pp. 180-185. Doi: 10.1109/ICBDA61153.2024.10607368
[11]. Christodoulakis, N., Abdelkader, W., Lokker, C., Cotterchio, M., Griffith, L. E., Vanderloo, L. M., Anderson, L. N., 2023, Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data. JMIR Form Res. 2023 Nov 2;7:e46874. Doi: 10.2196/46874. PMID: 37917123; PMCID: PMC10624214.
[12]. Aldosery, A., Carruthers, R., Kay, K., Cave, C., Reynolds, P., Kostkova, P., Enhancing Public Health Response: A Framework For Topics And Sentiment Analysis of COVID-19 in the UK using Twitter and the Embedded Topic Model. Front Public Health. 2024 Feb 21;12:1105383. Doi: 10.3389/fpubh.2024.1105383. PMID: 38450124
[13]. Nielbo, K. L., Karsdorp, F., Wevers, M., Lassche, A., Baglini, R. B., Kestemont, M., & Tahmasebi, N., 2024, Quantitative Text Analysis. Nature Reviews Methods Primers, 4(1), 1-16. https://doi.org/10.1038/s43586-024-00302-w
[14]. Sangam, Savita & Shinde, Subhash, 2019, A Novel Feature Selection Method Based on Genetic Algorithm for Opinion Mining of Social Media Reviews: Third International Conference, ICICCT 2018, New Delhi, India, Revised Selected Papers. 10.1007/978-981-13-5992-7_15.
[15]. Farooq, F., Amin, M. N., Khan, K., Sadiq, M. R., Javed, M. F., Aslam, F., & Alyousef, R., 2020, A Comparative Study of Random Forest And Genetic Engineering Programming For The Prediction of Compressive Strength Of High Strength Concrete (HSC). Applied Sciences (Switzerland), 10(20), 1–18. https://doi.org/10.3390/app10207330
[16]. Vapnik, V. N. 1998, Statistical Learning Theory Wiley-InterScience, New York, ISBN: 978-0-471-03003-4.
[17]. Ghani, R., 2021, Integrating Sentiment Analysis and Machine Learning to Gauge Public Perceptions of COVID-19. Journal of Medical Internet Research, 23(8), e31220.
[18]. Sharma, S. S, Kaur, D., Chawla, T. K., Kapoor, V., 2021, Information Sharing through Twitter by Public Health care Institution during COVID-19 Pandemic: A Case Study of AIIMS, Raipur. Indian J Community Health [Internet]. 33(1):189-92. https://iapsmupuk.org/journal/index.php/IJCH/article/view/2035
[19]. Niu, Q., Liu, J., Kato, M., Shinohara, Y., Matsumura, N., Aoyama, T., Nagai-Tanima, M., 2022, Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis. JMIR Infodemiology, 2(1):e32335. Doi: 10.2196/32335
[20]. Kisa, S., Kisa, A., 2024, A Comprehensive Analysis of COVID-19 Misinformation, Public Health Impacts, and Communication Strategies: Scoping Review, J Med Internet Res; 26:e56931 Doi: 10.2196/56931
[21]. Skafle, I, Nordahl-Hansen, A, Quintana, D. S, Wynn, R., Gabarron, E., 2022, Misinformation About COVID-19 Vaccines on Social Media: Rapid Review. J Med Internet Res. 4;24(8):e37367. Doi: 10.2196/37367. PMID: 35816685
[22]. Scannell, B. J., Drum, C., & Hine, C., 2021, Persuasion Techniques Used In Anti-Vaccine Twitter posts during the COVID-19 Pandemic. Health Communication, 36(11), 1368-1376.
[23]. Lyu. J. C, Han, E. L, Luli, G. K, 2021, COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis. J Med Internet Res., Jun 29;23(6):e24435. Doi: 10.2196/24435. PMID: 34115608
[24]. Cheng, T., Han, B., Liu, Y., 2023, Exploring Public Sentiment And Vaccination Uptake of COVID-19 Vaccines In England: A Spatiotemporal And Sociodemographic Analysis of Twitter data. Front Public Health. 17;11:1193750. Doi: 10.3389/fpubh.2023.1193750. PMID: 37663835
[25]. Dubé, E., Laberge, C., Guay, M., Bramadat, P., Roy, R., & Bettinger, J. A., 2013, Vaccine Hesitancy: An overview. Human Vaccines & Immunotherapeutics, 9(8), 1763–1773. https://doi.org/10.4161/hv.24657
[26]. Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., Shah, Z., 2020, Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study. J Med Internet Res, 22(4):e19016. Doi: 10.2196/19016
[27]. González-Padilla, D. A., Tortolero-Blanco, L., 2020, Social Media Influence in the COVID-19 pandemic. Int Braz J Urol., 46:120-4. 10.1590/S1677-5538.IBJU.2020.S121
[28]. Cuello-Garcia, C., Pérez-Gaxiola, G., van Amelsvoort, L., 2020, Social Media can have an Impact On How We Manage And Investigate the COVID-19 Pandemic. J Clin Epidemiol. 127:198-201. 10.1016/j.jclinepi.2020.06.028
[29]. Pennycook, G, Rand, D. G. 2019, Fighting Misinformation On Social Media using Crowdsourced Judgments Of News Source Quality. Proc Natl Acad Sci U S A, 116(7):2521-2526. Doi: 10.1073/pnas.1806781116. PMID: 30692252
[30]. World Health Organization: coronavirus disease (COVID-19) advice for the public., 2022, Accessed: August 18, 2024: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/myth-busters.
[31]. Islam, M. S, Sarkar, T., Khan, S. H., et al. 2020, COVID-19-related Infodemic And Its Impact on Public Health: A Global Social Media Analysis. Am J Trop Med Hyg. 103:1621-9. 10.4269/ajtmh.20-0812
[32]. Romer, D., Jamieson, K. H., 2020, Conspiracy Theories As Barriers To Controlling The Spread of COVID-19 in the U.S. Soc Sci Med. 263:113356. 10.1016/j.socscimed.2020.113356
[33]. Naeem, S. B., Bhatti, R., Khan, A., 2021, An Exploration Of How Fake News Is Taking Over Social Media And Putting Public Health At Risk. Health Info Libr J., 38:143-9. 10.1111/hir.12320
[34]. Joseph, A M., Fernandez, V., Kritzman, S., et al. 2022, COVID-19 Misinformation on Social Media: A Scoping Review. Cureus 14(4): e24601. Doi:10.7759/cureus.24601
[35]. Baker, S. A., Wade, M., & Walsh, M. J. 2020, The Challenges of Responding To Misinformation during A Pandemic: Content Moderation and The Limitations of The Concept of Harm. Media International Australia, 177(1), 103-107. https://doi.org/10.1177/1329878X20951301
[36]. Kılıç, N, Dikmen, E, Akşak, E, 2023, Managing Pandemic Communication Online: Turkish Ministry of Health’s Digital Communication Strategies During COVID-19. International Journal of Communication. Vol. 17.
[37]. Hutto, C. J. & Gilbert, E. E., 2014, VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014
