Analysis of the Relationship between Public Sentiment on Social Media and Indonesian Covid-19 Dynamics

Analisis Hubungan Sentimen Publik di Media Sosial dengan Dinamika Covid-19 Indonesia

Authors

  • Nana Mulyana Maghfur Universitas Singaperbangsa Karawang
  • Fakhri Universitas Singaperbangsa Karawang
  • Apriade Voutama Universitas Singaperbangsa Karawang

DOI:

https://doi.org/10.35706/sys.v3i3.6075

Abstract

The bad side of the open access nature of social media is that it frees anyone to have an opinion as they please and is often accompanied by other agendas such as spreading panic, false information, fake news, hate speech and even distorting public opinion. This condition can be fatal in the pandemic era where public opinion can worsen the pandemic situation. Therefore, it is important to know whether it is true that changes in public sentiment in response to news on social media can affect the dynamics of the spread of COVID-19. We use sentiment analysis using machine learning methods to extract daily sentiment data and test its correlation with daily Covid-19 case data in Indonesia. The results of the associative hypothesis test with a Pearson correlation value of 0.151 show that public sentiment on social media towards the news of the COVID-19 variant is positively correlated with the dynamics of the daily Covid-19 cases. Therefore, the author invites all social media users, including the author himself, to be more vigilant and careful in giving opinions and accepting other people's opinions on social media.

Downloads

Download data is not yet available.

Author Biographies

Fakhri, Universitas Singaperbangsa Karawang

Mahasiswa pada Program Studi Teknik Informatika, Universitas Singaperbangsa Karawang

Apriade Voutama, Universitas Singaperbangsa Karawang

Dosen di Program Studi Sistem Informasi, Universitas Singaperbangsa Karawang

References

WHO, “WHO Coronavirus (COVID-19) Dashboard,” Geneva World Health Organization., 2020.

W. McKibbin and R. Fernando, “The economic impact of COVID-19,” Econ. Time COVID-19, vol. 45, no. 10.1162, 2020.

S. Maital and E. Barzani, “The global economic impact of COVID-19: A summary of research,” Samuel Neaman Inst. Natl. Policy Res., vol. 2020, pp. 1–12, 2020.

O. Dyer, “Covid-19: Indonesia becomes Asia’s new pandemic epicentre as delta variant spreads,” BMJ, vol. 374, p. n1815, Jul. 2021.

A. Wong, S. Ho, O. Olusanya, M. V. Antonini, and D. Lyness, “The use of social media and online communications in times of pandemic COVID-19,” J. Intensive Care Soc., vol. 22, no. 3, pp. 255–260, Aug. 2021.

H. Sahni and H. Sharma, “Role of social media during the COVID-19 pandemic: Beneficial, destructive, or reconstructive?,” Int. J. Acad. Med., vol. 6, no. 2, pp. 70–75, Apr. 2020.

A. Clark, “COVID-19-related Misinformation: Fabricated and Unverified Content on Social Media,” Anal. Metaphys., no. 19, pp. 87–93, 2020.

K. Chakraborty, S. Bhattacharyya, and R. Bag, “A Survey of Sentiment Analysis from Social Media Data,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 450–464, Apr. 2020.

E. Miranda, M. Aryuni, R. Hariyanto, and E. S. Surya, “Sentiment Analysis using Sentiwordnet and Machine Learning Approach (Indonesia general election opinion from the twitter content),” in 2019 International Conference on Information Management and Technology (ICIMTech), 2019, vol. 1, pp. 62–67.

F. S. Pamungkas and I. Kharisudin, “Analisis Sentimen dengan SVM, NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter,” PRISMA Pros. Semin. Nas. Mat., vol. 4, pp. 628–634, Feb. 2021.

F. Z. Ahmad, M. F. S. Arifandy, M. R. Caesarardhi, and N. A. Rakhmawati, “Bagaimana Masyarakat Menyikapi Pembelajaran Tatap Muka: Analisis Komentar Masyarakat pada Media Sosial Youtube Menggunakan Algoritma Deep Learning Sekuensial dan LDA,” J. Linguist. Komputasional, vol. 4, no. 2, pp. 40–46, 2021.

Rifiana Arief and Karel Imanuel, “Analisis Sentimen Topik Viral Desa Penari Pada Media Sosial Twitter Dengan Metode Lexicon Based,” J. Ilm. Matrik, vol. 21, no. 3, Dec. 2019.

B. Vidgen and L. Derczynski, “Directions in abusive language training data, a systematic review: Garbage in, garbage out,” PLOS ONE, vol. 15, no. 12, p. e0243300, Des 2020.

K. Mishev, A. Gjorgjevikj, I. Vodenska, L. T. Chitkushev, and D. Trajanov, “Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers,” IEEE Access, vol. 8, pp. 131662–131682, 2020.

V. Prabhakaran, A. M. Davani, and M. Díaz, “On Releasing Annotator-Level Labels and Information in Datasets,” ArXiv211005699 Cs, Oct. 2021.

P. Chikersal, S. Poria, and E. Cambria, “SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning,” presented at the Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015, pp. 647–651.

I. S. Joyosemito and N. M. Nasir, “Gelombang Kedua Pandemi Menuju Endemi Covid-19: Analisis Kebijakan Vaksinasi dan Pembatasan Kegiatan Masyarakat di Indonesia,” J. Sains Teknol. Dalam Pemberdaya. Masy., vol. 2, no. 1, 2021.

N. Aliyah Salsabila, Y. Ardhito Winatmoko, A. Akbar Septiandri, and A. Jamal, “Colloquial Indonesian Lexicon,” in 2018 International Conference on Asian Language Processing (IALP), 2018, pp. 226–229.

D. H. Wahid and A. Sn, “Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity,” IJCCS Indones. J. Comput. Cybern. Syst., vol. 10, no. 2, pp. 207–218, Jul. 2016.

W. Budiharto and M. Meiliana, “Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis,” J. Big Data, vol. 5, no. 1, p. 51, Dec. 2018.

Downloads

Published

2021-12-03

How to Cite

[1]
Nana Mulyana Maghfur, Fakhri Muhammad, and Apriade Voutama, “Analysis of the Relationship between Public Sentiment on Social Media and Indonesian Covid-19 Dynamics: Analisis Hubungan Sentimen Publik di Media Sosial dengan Dinamika Covid-19 Indonesia”, Systematics Journal, vol. 3, no. 3, pp. 336–345, Dec. 2021.