Abstract
This study looks for the relationship between social media and the sentiment in COVID-19 vaccine policy in Indonesia spread on Twitter. This study used social media data from Twitter with hashtags related to vaccination program issues. This study used Nvivo 12 to collect data and analyze data with word cloud analysis, matrix analysis, and crosstab analysis. The results showed that the government used Twitter to convey information about the COVID-19 vaccine by 100% pros. The pro-cons opinion mapping on the news is 37.5% for the pro opinion and 62.5% for the cons opinion. The general public opinion showed 21.32% for pro opinions and 77.68% for contra opinions. This study’s limitations were only hashtags and pros and cons.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
WHO, IFRC, and Unicef: Key Messages and Actions for Prevention and Control in Schools. Key Messag. Actions COVID-19 Prev. Control Sch. no. March, p. 13 (2020)
Dzulfaroh, A.N.: 40 Hari Virus Corona Dikonfirmasi di Indonesia, Apa yang Sudah Kita Lakukan? Halaman all - Kompas.com. kompas.com, p. 1 (2020)
Bonnevie, E., Gallegos-Jeffrey, A., Goldbarg, J., Byrd, B., Smyser, J.: Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. J. Commun. Healthc. 14(1), 1–8 (2020). https://doi.org/10.1080/17538068.2020.1858222
Rachman, F.F., Pramana, S.: Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter. Health Inf. Manag. J. 8(2), 2655–9129 (2020)
Baly, R., et al.: A characterization study of Arabic Twitter data with a benchmarking for state-of-the-art opinion mining models (2017)
Meduru, M., Mahimkar, A., Subramanian, K., Padiya, P.Y., Gunjgur, P.N.: Opinion mining using Twitter feeds for political analysis. Int. J. Comput. 25, 116–123 (2017)
Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in Twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the International Conference on Information and Knowledge Management, pp. 1031–1040 (2011). https://doi.org/10.1145/2063576.2063726
Priyanthan, P., Prasath, N., Perera, A.: Opinion mining and sentiment analysis on a Twitter data stream (2012)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision, Stanford, Technical (2009)
Pratama, E.E., Atmi, R.L.: A text mining implementation based on Twitter data to analyse information regarding corona virus in Indonesia. J. Comput. Soc. 1(1), 91–100 (2020)
Ja, H., Pa, G.: Adult cardiac surgery during the COVID-19 pandemic: a tiered patient triage guidance statement. Ann. Oncol. 7, 19–21 (2020)
Inayah, D., Purba, F.L.: Implementasi social network analysis Dalam Penyebaran Informasi Virus Corona (Covid-19) Di Twitter. In: Semin. Nas. Off. Stat., vol. 2020, no. 1, pp. 292–299 (2021). https://doi.org/10.34123/semnasoffstat.v2020i1.573
Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hai, M., Shah, Z.: Top concerns of tweeters during the COVID-19 pandemic: a surveillance study. J. Med. Internet Res. 22(4), 1–9 (2020). https://doi.org/10.2196/19016
Vergara, R.J.D., Sarmiento, P.J.D., Lagman, J.D.N.: Building public trust: a response to COVID-19 vaccine hesitancy predicament. J. Public Health (Bangkok) 43, 1–2 (2021). https://doi.org/10.1093/pubmed/fdaa282
Haberman, R., et al.: Covid-19 in immune-mediated inflammatory diseases—case series from New York. N. Engl. J. Med. 383(1), 85–88 (2020). https://doi.org/10.1056/nejmc2009567
Molotch, H., Lester, M.: News as purposive behavior: on the strategic use of routine events, accidents, and scandals. Am. Sociol. Rev. 39(1), 101 (1974). https://doi.org/10.2307/2094279
Kumar, A., Dogra, P., Dabas, V.: Emotion analysis of Twitter using opinion mining (2015)
Cordero, D.A.: Rebuilding public trust: a clarified response to COVID-19 vaccine hesitancy predicament. J. Public Health (Bangkok) 43, 1–2 (2021). https://doi.org/10.1093/pubmed/fdab020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hasti, I., Nurmandi, A., Muallidin, I., Kurniawan, D., Salahudin (2022). Pros and Cons of Vaccine Program in Indonesia (Social Media Analysis on Twitter). In: Ahram, T., Taiar, R. (eds) Human Interaction, Emerging Technologies and Future Systems V. IHIET 2021. Lecture Notes in Networks and Systems, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-85540-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-85540-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85539-0
Online ISBN: 978-3-030-85540-6
eBook Packages: EngineeringEngineering (R0)